Package: a4 Version: 1.14.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: 4169bbdec6e92f7952bf7ca76911cb66 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Automated Affymetrix Array Analysis Umbrella Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4_1.14.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R Package: a4Base Version: 1.14.0 Depends: methods, graphics, grid, Biobase, AnnotationDbi, annaffy, mpm, genefilter, limma, multtest, glmnet, a4Preproc, a4Core, gplots Suggests: Cairo, ALL Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 5f9ba6cba8869be59d4641faf391f6ba NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Automated Affymetrix Array Analysis biocViews: Microarray Author: Willem Talloen, Tobias Verbeke, Tine Casneuf, An De Bondt, Steven Osselaer and Hinrich Goehlmann, Willem Ligtenberg Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Base_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4Base_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4Base_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4Base_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4Base_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.14.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: b635b63a2233f4bed85b9cac18ba35c2 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Automated Affymetrix Array Analysis Classification Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Classif_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4Classif_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4Classif_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4Classif_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4Classif_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.14.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: ef3f8f07b9569c6a6b673a4369163825 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Automated Affymetrix Array Analysis Core Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Core_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4Core_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4Core_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4Core_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4Core_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.14.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: b698c447d07c2f5dba641ba4339e0f5b NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Automated Affymetrix Array Analysis Preprocessing Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Preproc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4Preproc_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4Preproc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4Preproc_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4Preproc_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif Package: a4Reporting Version: 1.14.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: c5811645d25e0b68e086943ab9ec1f91 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Automated Affymetrix Array Analysis Reporting Package biocViews: Microarray Author: Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Reporting_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/a4Reporting_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/a4Reporting_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/a4Reporting_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/a4Reporting_1.14.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: ABarray Version: 1.34.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 32962ddcec7298cbfd2ef77c67565357 NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun source.ver: src/contrib/ABarray_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ABarray_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ABarray_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ABarray_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ABarray_1.34.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABarray/inst/doc/ABarray.R, vignettes/ABarray/inst/doc/ABarrayGUI.R Package: ABSSeq Version: 1.2.0 Depends: R (>= 2.10), methods Imports: Rcpp LinkingTo: Rcpp License: GPL (>= 3) Archs: i386, x64 MD5sum: 5f912d77355fcad6a078078eb318a7c9 NeedsCompilation: yes Title: ABSSeq: a new RNA-Seq analysis method based on absolute expression differences and generalized Poisson model Description: Inferring differential expression genes by absolute expression differences between two groups, utilizing generalized Poisson model to account for over-dispersion across samples and heterogeneity of differential expression across genes. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang source.ver: src/contrib/ABSSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ABSSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ABSSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ABSSeq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ABSSeq_1.2.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R Package: aCGH Version: 1.44.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, cluster, grDevices, graphics, methods, multtest, stats, survival, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: 54d3a2039a3657085224211c21a589f0 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data. Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov source.ver: src/contrib/aCGH_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/aCGH_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/aCGH_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/aCGH_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/aCGH_1.44.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP Package: ACME Version: 2.22.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: d5c2628b5d5169e14c0edcf4654da53d NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis source.ver: src/contrib/ACME_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ACME_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ACME_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ACME_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ACME_2.22.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R Package: ADaCGH2 Version: 2.6.0 Depends: R (>= 2.15.0), parallel, ff Imports: bit, ffbase, DNAcopy, tilingArray, GLAD, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: c103784b3e14a89da1b0ad109c49eb28 NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Maintainer: Ramon Diaz-Uriarte source.ver: src/contrib/ADaCGH2_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ADaCGH2_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ADaCGH2_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ADaCGH2_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ADaCGH2_2.6.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R Package: adSplit Version: 1.36.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGG.db (>= 1.8.1), methods, multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: i386, x64 MD5sum: 11a8e6f12ef594aa0588708b01db0ad4 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml source.ver: src/contrib/adSplit_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/adSplit_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/adSplit_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/adSplit_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/adSplit_1.36.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R Package: affxparser Version: 1.38.0 Depends: R (>= 2.6.0) Suggests: R.oo (>= 1.18.0), R.utils (>= 1.32.4), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: 81141cacbcab7fcb7508f11ed297d5f9 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser/ source.ver: src/contrib/affxparser_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affxparser_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affxparser_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affxparser_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affxparser_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, rMAT, Starr importsMe: affyILM, AffyTiling, cn.farms, GeneRegionScan, ITALICS, oligo, rMAT Package: affy Version: 1.44.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocInstaller, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 98c1f22d735a3d0defbc908ac3f75435 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry source.ver: src/contrib/affy_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affy_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affy_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affy_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affy_1.44.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affycoretools, AffyExpress, affyPara, affypdnn, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, LMGene, logitT, maskBAD, MLP, panp, plw, prebs, puma, qpcrNorm, ReadqPCR, RefPlus, rHVDM, Risa, RPA, SCAN.UPC, simpleaffy, sscore, Starr, webbioc importsMe: affyILM, affylmGUI, affyQCReport, AffyTiling, ArrayExpress, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, Cormotif, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, lumi, LVSmiRNA, makecdfenv, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, MLSeq, oneChannelGUI, paxtoolsr, piano, PREDA, qcmetrics, siggenes Package: affycomp Version: 1.42.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 614981e4d47e4c1b2ae184260288d8c1 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry source.ver: src/contrib/affycomp_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affycomp_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affycomp_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affycomp_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affycomp_1.42.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R Package: AffyCompatible Version: 1.26.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 6e4a894252e66d434b76dd1ef2c16e41 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan source.ver: src/contrib/AffyCompatible_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AffyCompatible_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AffyCompatible_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AffyCompatible_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AffyCompatible_1.26.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R importsMe: IdMappingRetrieval Package: affyContam Version: 1.24.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata License: Artistic-2.0 MD5sum: 871889721843514f33479f262652b8bd NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey source.ver: src/contrib/affyContam_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyContam_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyContam_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyContam_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyContam_1.24.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R Package: affycoretools Version: 1.38.0 Depends: affy, Biobase, GO.db Imports: biomaRt, limma, GOstats, annotate, annaffy, genefilter, gcrma, splines, xtable, AnnotationDbi, lattice, gplots, oligoClasses, ReportingTools, hwriter Suggests: affydata, hgfocuscdf, rgl, BiocStyle, knitr License: Artistic-2.0 MD5sum: abebbc1c856d4b15f8a7f377c9cbe231 NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips. Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr source.ver: src/contrib/affycoretools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affycoretools_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affycoretools_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affycoretools_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affycoretools_1.38.0.tgz vignettes: vignettes/affycoretools/inst/doc/affycoretools_biomaRt.pdf, vignettes/affycoretools/inst/doc/affycoretools.pdf, vignettes/affycoretools/inst/doc/RefactoredAffycoretools.pdf vignetteTitles: affycoretools biomaRt Integration, affycoretools Overview, affycoretools,, refactored hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/affycoretools_biomaRt.R, vignettes/affycoretools/inst/doc/affycoretools.R, vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R Package: AffyExpress Version: 1.32.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: 515377c44b1661c259ad04f45bcd7515 NeedsCompilation: no Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li source.ver: src/contrib/AffyExpress_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AffyExpress_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AffyExpress_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AffyExpress_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AffyExpress_1.32.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyExpress/inst/doc/AffyExpress.R Package: affyILM Version: 1.18.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, methods, Biobase Suggests: AffymetrixDataTestFiles License: GPL version 3 MD5sum: 9e45debd20931b9b6053cfd4bfdf732b NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger source.ver: src/contrib/affyILM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyILM_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyILM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyILM_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyILM_1.18.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R Package: affyio Version: 1.34.0 Depends: R (>= 2.6.0) Imports: zlibbioc License: LGPL (>= 2) Archs: i386, x64 MD5sum: 854fe316d2df20ee54e5cbd134d9c376 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/affyio_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyio_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyio_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyio_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyio_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods Package: affylmGUI Version: 1.40.2 Depends: affyPLM, tkrplot Imports: limma, tcltk, affy, BiocInstaller, affyio, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: b69296e180bf3dff7ee9f9d245477721 NeedsCompilation: no Title: GUI for affy analysis using limma package Description: A Graphical User Interface for affy analysis using the limma Microarray package biocViews: Microarray, OneChannel, DataImport, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, GUI Author: James Wettenhall and Ken Simpson Division of Genetics and Bioinformatics, WEHI. Maintainer: Keith Satterley URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.40.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/affylmGUI_1.40.2.zip win64.binary.ver: bin/windows64/contrib/3.1/affylmGUI_1.40.2.zip mac.binary.ver: bin/macosx/contrib/3.1/affylmGUI_1.40.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affylmGUI_1.40.2.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R, vignettes/affylmGUI/inst/doc/extract.R dependsOnMe: oneChannelGUI Package: affyPara Version: 1.26.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: a242ef7e71cb9d5286018aa4cdcb236f NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de source.ver: src/contrib/affyPara_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyPara_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyPara_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyPara_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyPara_1.26.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R Package: affypdnn Version: 1.40.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 54d46a135e541c8e9b606a6eeea3da90 NeedsCompilation: no Title: Probe Dependent Nearest Neighbours (PDNN) for the affy package Description: The package contains functions to perform the PDNN method described by Li Zhang et al. biocViews: OneChannel, Microarray, Preprocessing Author: H. Bjorn Nielsen and Laurent Gautier (Many thanks to Li Zhang early communications about the existence of the PDNN program and related publications). Maintainer: Laurent Gautier source.ver: src/contrib/affypdnn_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affypdnn_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affypdnn_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affypdnn_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affypdnn_1.40.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affypdnn/inst/doc/affypdnn.R Package: affyPLM Version: 1.42.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: BiocGenerics, zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: d62e9d464c34697b1180f94e172ec25e NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: http://bmbolstad.com source.ver: src/contrib/affyPLM_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyPLM_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyPLM_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyPLM_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyPLM_1.42.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: affylmGUI, RefPlus importsMe: affyQCReport, arrayQualityMetrics suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, oneChannelGUI, piano Package: affyQCReport Version: 1.44.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) MD5sum: 3f3f3e9a57a8a3de48fa3b034eb959c6 NeedsCompilation: no Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman source.ver: src/contrib/affyQCReport_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/affyQCReport_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/affyQCReport_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/affyQCReport_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/affyQCReport_1.44.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyQCReport/inst/doc/affyQCReport.R suggestsMe: BiocCaseStudies Package: AffyRNADegradation Version: 1.12.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: eefce8d0cee1ced4bd55c4b59821ae8f NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold source.ver: src/contrib/AffyRNADegradation_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AffyRNADegradation_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AffyRNADegradation_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AffyRNADegradation_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AffyRNADegradation_1.12.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R Package: AffyTiling Version: 1.24.0 Depends: R (>= 2.6) Imports: affxparser, affy (>= 1.16), stats, utils, preprocessCore License: GPL (>= 2) Archs: i386, x64 MD5sum: 4b7b42625389b8a3158b2dd85c5e25b7 NeedsCompilation: yes Title: Easy extraction of individual probes in Affymetrix tiling arrays Description: This package provides easy, fast functions for the extraction and annotation of individual probes from Affymetrix tiling arrays. biocViews: Microarray, Preprocessing Author: Charles G. Danko Maintainer: Charles G. Danko source.ver: src/contrib/AffyTiling_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AffyTiling_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AffyTiling_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AffyTiling_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AffyTiling_1.24.0.tgz vignettes: vignettes/AffyTiling/inst/doc/AffyTiling.pdf vignetteTitles: AffyTiling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyTiling/inst/doc/AffyTiling.R Package: AGDEX Version: 1.14.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 233ef411a4bdd9cf8938872381fabf79 NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao source.ver: src/contrib/AGDEX_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AGDEX_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AGDEX_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AGDEX_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AGDEX_1.14.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R Package: agilp Version: 3.8.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 03128b2180027a0d7c7ab75ffd1a7d80 NeedsCompilation: no Title: Agilent expression array processing package Description: provides a pipeline for the low-level analysis of gene expression microarray data, primarily Agilent data biocViews: StatisticalMethod Author: Benny Chain Maintainer: Benny Chain source.ver: src/contrib/agilp_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/agilp_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/agilp_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/agilp_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/agilp_3.8.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R Package: AgiMicroRna Version: 2.16.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 6fef33dd652d700e381f9d22aef039c7 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero source.ver: src/contrib/AgiMicroRna_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AgiMicroRna_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AgiMicroRna_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AgiMicroRna_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AgiMicroRna_2.16.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R Package: ALDEx2 Version: 1.0.0 Depends: methods Imports: GenomicRanges Suggests: parallel, BiocParallel License: file LICENSE MD5sum: 7ae15f76a16e35f4bafad0a36f322d5c NeedsCompilation: no Title: Analysis of differential abundance taking sample variation into account Description: A differential abundance analysis for the comparison of two or more conditions. For example, single-organism and meta-RNA-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected false discovery rate given the biological and sampling variation using the Wilcox rank test or Welches t-test (aldex.ttest) or the glm and Kruskal Wallis tests (aldex.glm). Reports both P and fdr values calculated by the Benjamini Hochberg correction. biocViews: DifferentialExpression, RNASeq, DNASeq, ChIPSeq, GeneExpression, Bayesian, Sequencing, Software Author: Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Arianne Albert, Matt Links Maintainer: Greg Gloor source.ver: src/contrib/ALDEx2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ALDEx2_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ALDEx2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ALDEx2_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ALDEx2_1.0.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: An R Package for determining differential abundance in high throughput sequencing experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R Package: AllelicImbalance Version: 1.4.2 Depends: R (>= 3.1.0), grid, GenomicRanges, GenomicAlignments, Imports: methods, BiocGenerics, AnnotationDbi, Biostrings, S4Vectors, IRanges, Rsamtools, GenomicFeatures, Gviz, lattice, GenomeInfoDb Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608 License: GPL-3 MD5sum: 338f0d7e8b7b1a86ced636ac3d4bcf26 NeedsCompilation: no Title: Investigates allele specific expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance source.ver: src/contrib/AllelicImbalance_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/AllelicImbalance_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.1/AllelicImbalance_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.1/AllelicImbalance_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AllelicImbalance_1.4.2.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance.pdf vignetteTitles: AllelicImbalance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance.R Package: alsace Version: 1.2.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice License: GPL (>= 2) MD5sum: 97fab8f1907c239c1b4b63349fe3e9e7 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens source.ver: src/contrib/alsace_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/alsace_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/alsace_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/alsace_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/alsace_1.2.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alsace/inst/doc/alsace.R Package: altcdfenvs Version: 2.28.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: ca49ee7ec0fd6b6588f92ecc169079c2 NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/altcdfenvs_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/altcdfenvs_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/altcdfenvs_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/altcdfenvs_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/altcdfenvs_2.28.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, affy primer, affy primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight Package: ampliQueso Version: 1.4.0 Depends: R (>= 2.15.0), rnaSeqMap (>= 2.17.1), knitr, rgl, ggplot2, gplots, parallel, doParallel, foreach, VariantAnnotation,genefilter,statmod,xtable Imports: edgeR, DESeq, samr License: GPL-2 MD5sum: 0ceae26f6b8ff9c80275ef63b4d92d2a NeedsCompilation: no Title: Analysis of amplicon enrichment panels Description: The package provides tools and reports for the analysis of amplicon sequencing panels, such as AmpliSeq biocViews: ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Visualization Author: Alicja Szabelska ; Marek Wiewiorka ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/ampliQueso_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ampliQueso_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ampliQueso_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ampliQueso_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ampliQueso_1.4.0.tgz vignettes: vignettes/ampliQueso/inst/doc/ampliQueso.pdf vignetteTitles: ampliQueso primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ampliQueso/inst/doc/ampliQueso.R Package: annaffy Version: 1.38.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15) Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 6ac810128a03ab992b89ff598b9a1ce7 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith source.ver: src/contrib/annaffy_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/annaffy_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/annaffy_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/annaffy_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/annaffy_1.38.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: a4Base, a4Reporting, PGSEA, webbioc importsMe: affycoretools suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies Package: annmap Version: 1.8.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: f1065043cf5e7a774362665195a433c7 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk source.ver: src/contrib/annmap_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/annmap_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/annmap_1.8.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annmap/inst/doc/annmap.R, vignettes/annmap/inst/doc/cookbook.R, vignettes/annmap/inst/doc/INSTALL.R Package: annotate Version: 1.44.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.11.2) Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: dd92e55761ee9becd70c044feef9e565 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/annotate_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/annotate_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/annotate_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/annotate_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/annotate_1.44.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useHomology.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, ScISI, SemDist importsMe: affycoretools, CAFE, Category, categoryCompare, codelink, DOQTL, DrugVsDisease, facopy, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, mvGST, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, biomaRt, GlobalAncova, globaltest, GOstats, GSAR, GSEAlm, maigesPack, metagenomeSeq, MLP, oneChannelGUI, siggenes Package: AnnotationDbi Version: 1.28.2 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.11.2), Biobase (>= 1.17.0), GenomeInfoDb(>= 0.99.17) Imports: methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase, S4Vectors Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, org.TguttataTestingSubset.eg.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 973214a4486468a9b2d63aae143a6bac NeedsCompilation: no Title: Annotation Database Interface Description: Provides user interface and database connection code for annotation data packages using SQLite data storage. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Herve Pages, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik source.ver: src/contrib/AnnotationDbi_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/AnnotationDbi_1.28.2.zip win64.binary.ver: bin/windows64/contrib/3.1/AnnotationDbi_1.28.2.zip mac.binary.ver: bin/macosx/contrib/3.1/AnnotationDbi_1.28.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AnnotationDbi_1.28.2.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: How to use bimaps from the ".db" annotation packages, AnnotationDbi: Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, miRNAtap, MLP, OrganismDbi, PADOG, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, attract, beadarray, biomaRt, BioNet, biovizBase, CancerMutationAnalysis, Category, categoryCompare, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, csaw, customProDB, derfinder, domainsignatures, DOSE, ExpressionView, FunciSNP, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, GGBase, GGtools, GlobalAncova, globaltest, GOFunction, GOSemSim, goseq, GOSim, GOstats, goTools, graphite, GSEABase, Gviz, HTSanalyzeR, interactiveDisplay, lumi, MeSHDbi, methyAnalysis, methylumi, MineICA, MiRaGE, mvGST, OrganismDbi, PADOG, PAnnBuilder, pathview, pcaGoPromoter, PCpheno, phenoTest, qpgraph, ReactomePA, REDseq, rTRM, ScISI, SGSeq, SLGI, tigre, topGO, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, FGNet, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, GenoView, limma, MmPalateMiRNA, neaGUI, oneChannelGUI, qcmetrics, sigPathway Package: AnnotationForge Version: 1.8.2 Depends: R (>= 2.7.0), methods, utils, BiocGenerics (>= 0.1.13), Biobase (>= 1.17.0), AnnotationDbi (>= 1.19.15), org.Hs.eg.db Imports: methods, utils, DBI, RSQLite, BiocGenerics, S4Vectors, Biobase Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), XML, RCurl, hgu95av2.db, human.db0, affy, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 3af4728ed218d6f89ae1a8b8e8734ad6 NeedsCompilation: no Title: Code for Building Annotation Database Packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Herve Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationForge_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/AnnotationForge_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.1/AnnotationForge_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.1/AnnotationForge_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AnnotationForge_1.8.2.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R htmlDocs: vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html htmlTitles: "Making New Organism Packages" importsMe: GOstats suggestsMe: AnnotationDbi Package: AnnotationFuncs Version: 1.16.0 Depends: R (>= 2.7.0), AnnotationDbi Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: 1d1aa39da7bb2956856e83836958b9f0 NeedsCompilation: no Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions source.ver: src/contrib/AnnotationFuncs_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AnnotationFuncs_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AnnotationFuncs_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AnnotationFuncs_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AnnotationFuncs_1.16.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: AnnotationHub Version: 1.6.0 Depends: S4Vectors (>= 0.2.3), IRanges (>= 1.99.28) Imports: methods, stats, utils, rjson, BiocGenerics, httr, BiocInstaller (>= 1.11.0), AnnotationDbi, GenomicRanges, interactiveDisplay (>= 1.0.23) Suggests: RUnit, RCurl, Rsamtools License: Artistic-2.0 MD5sum: 7aadeb3488424ed24254830fa5982a17 NeedsCompilation: no Title: A client for retrieving Bioconductor objects from AnnotationHub Description: A client for retrieving data from the Bioconductor AnnotationHub online services. biocViews: Annotation, Infrastructure Author: Marc Carlson, Sonali Arora Maintainer: Marc Carlson Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik source.ver: src/contrib/AnnotationHub_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AnnotationHub_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AnnotationHub_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AnnotationHub_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AnnotationHub_1.6.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub.pdf, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.pdf vignetteTitles: AnnotationHub: A client package for retrieving data from the AnnotationHub web service, How to write recipes for new resources for the AnnotationHub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.R dependsOnMe: RefNet suggestsMe: GenomicRanges Package: annotationTools Version: 1.40.0 Imports: Biobase, stats License: GPL MD5sum: 0c2c87d7a3258d92055c49931e51f322 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases. Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/annotationTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/annotationTools_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/annotationTools_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/annotationTools_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/annotationTools_1.40.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: DOQTL Package: anota Version: 1.14.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: fa2c1f66243fdddfb85d5610eedc52f8 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson source.ver: src/contrib/anota_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/anota_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/anota_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/anota_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/anota_1.14.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome Package: antiProfiles Version: 1.6.0 Depends: R (>= 3.0), matrixStats (>= 0.5), methods (>= 2.14), Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 3834c7ba1b29697287b30384dbf3f8b6 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo source.ver: src/contrib/antiProfiles_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/antiProfiles_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/antiProfiles_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/antiProfiles_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/antiProfiles_1.6.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R Package: apComplex Version: 2.32.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 72e51b0323519f0e02b0b7593a08b5b0 NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/apComplex_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/apComplex_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/apComplex_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/apComplex_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/apComplex_2.32.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependsOnMe: ScISI suggestsMe: BiocCaseStudies Package: aroma.light Version: 2.2.1 Depends: R (>= 2.14.0) Imports: R.methodsS3 (>= 1.6.1), R.oo (>= 1.18.0), R.utils (>= 1.33.0), matrixStats (>= 0.10.0) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: 174f7704cec710070536916f4501c421 NeedsCompilation: no Title: Light-weight methods for normalization and visualization of microarray data using only basic R data types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb] Maintainer: Henrik Bengtsson URL: http://www.aroma-project.org/, https://github.com/HenrikBengtsson/aroma.light/ source.ver: src/contrib/aroma.light_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/aroma.light_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/aroma.light_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/aroma.light_2.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/aroma.light_2.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq Package: ArrayExpress Version: 1.26.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, affy, limma License: Artistic-2.0 MD5sum: 9282a248c3cf78cb37a4b75df0c898c7 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam Maintainer: Ibrahim Emam source.ver: src/contrib/ArrayExpress_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ArrayExpress_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ArrayExpress_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ArrayExpress_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ArrayExpress_1.26.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: gCMAPWeb Package: ArrayExpressHTS Version: 1.16.0 Depends: sampling, Rsamtools (>= 1.3.32), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges, R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rsamtools License: Artistic License 2.0 MD5sum: ea4a0b07025b6af21a679ccae34ce45e NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov source.ver: src/contrib/ArrayExpressHTS_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/ArrayExpressHTS_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ArrayExpressHTS_1.16.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R Package: arrayMvout Version: 1.24.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy, lumi Imports: simpleaffy, mdqc, affyContam, Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 1f417e3ac2e89d646af443dc40441c2a NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey source.ver: src/contrib/arrayMvout_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/arrayMvout_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/arrayMvout_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/arrayMvout_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/arrayMvout_1.24.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R Package: arrayQuality Version: 1.44.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: fbf8e76061f47477a6db1478b5d2029c NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ source.ver: src/contrib/arrayQuality_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/arrayQuality_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/arrayQuality_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/arrayQuality_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/arrayQuality_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.22.1 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, Cairo (>= 1.4-6), genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, SVGAnnotation (>= 0.9-0), utils, vsn (>= 3.23.3), XML Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 90c619ce5c1478143da9e5b68ca102e7 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Audrey Kauffmann VignetteBuilder: knitr source.ver: src/contrib/arrayQualityMetrics_3.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/arrayQualityMetrics_3.22.1.zip win64.binary.ver: bin/windows64/contrib/3.1/arrayQualityMetrics_3.22.1.zip mac.binary.ver: bin/macosx/contrib/3.1/arrayQualityMetrics_3.22.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/arrayQualityMetrics_3.22.1.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R Package: ArrayTools Version: 1.26.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) MD5sum: 800eb5b7d15f5e5aa2267e72a8faf7b3 NeedsCompilation: no Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li source.ver: src/contrib/ArrayTools_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ArrayTools_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ArrayTools_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ArrayTools_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ArrayTools_1.26.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTools/inst/doc/ArrayTools.R Package: ArrayTV Version: 1.4.0 Depends: R (>= 2.14) Imports: foreach, DNAcopy, methods, oligoClasses (>= 1.21.3) Suggests: RColorBrewer, crlmm, ff, BSgenome.Hsapiens.UCSC.hg18,BSgenome.Hsapiens.UCSC.hg19, lattice, latticeExtra, RUnit, BiocGenerics Enhances: doMC, doSNOW, doParallel License: GPL (>= 2) MD5sum: 245226f2c143435b23e6e00ca0d9673c NeedsCompilation: no Title: Implementation of wave correction for arrays Description: Wave correction for genotyping and copy number arrays biocViews: CopyNumberVariation Author: Eitan Halper-Stromberg Maintainer: Eitan Halper-Stromberg source.ver: src/contrib/ArrayTV_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ArrayTV_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ArrayTV_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ArrayTV_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ArrayTV_1.4.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTV/inst/doc/ArrayTV.R suggestsMe: VanillaICE Package: ARRmNormalization Version: 1.6.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: c8ebfc4a2729c8fd129eaad3560b8d94 NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin source.ver: src/contrib/ARRmNormalization_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ARRmNormalization_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ARRmNormalization_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ARRmNormalization_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ARRmNormalization_1.6.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R Package: ASEB Version: 1.10.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 6e6e0bf5287e93d9122c7db0eac5ada4 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang source.ver: src/contrib/ASEB_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ASEB_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ASEB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ASEB_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ASEB_1.10.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R Package: ASGSCA Version: 1.0.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: ba3a64be59e1c3c8c192f9e6385af3ee NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani source.ver: src/contrib/ASGSCA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ASGSCA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ASGSCA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ASGSCA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ASGSCA_1.0.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R Package: asmn Version: 1.2.0 Depends: R (>= 3.0.2) Imports: methylumi, stats, Biobase Suggests: TCGAMethylation450k, IlluminaHumanMethylation450k.db License: GPL-3 MD5sum: eb9d4947e79fa4f89805a001a5874dd4 NeedsCompilation: no Title: All sample mean normalization. Description: Performs all sample mean normalization using raw data output from BeadStudio and MethyLumiM data. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: 'Anna Decker [aut,cre], Paul Yousefi [aut,cre]' Maintainer: Anna Decker source.ver: src/contrib/asmn_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/asmn_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/asmn_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/asmn_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/asmn_1.2.0.tgz vignettes: vignettes/asmn/inst/doc/asmn-vignette.pdf vignetteTitles: Vignette for R package asmn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/asmn/inst/doc/asmn-vignette.R Package: ASSET Version: 1.4.0 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: e1d0e5388ba55ae6ae8d3cc89b393f06 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/ASSET_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ASSET_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ASSET_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ASSET_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ASSET_1.4.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R Package: ASSIGN Version: 1.2.0 Depends: Rlab, msm, gplots Imports: graphics, grDevices, stats, utils License: MIT MD5sum: 6d8176f7b8d06e8becb7344eb98f4e8b NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literatures or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, and W. Evan Johnson Maintainer: Ying Shen source.ver: src/contrib/ASSIGN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ASSIGN_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ASSIGN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ASSIGN_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ASSIGN_1.2.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R Package: AtlasRDF Version: 1.2.0 Depends: R (>= 2.10), hash, SPARQL, methods License: Apache License 2.0 MD5sum: 5a77b82b9ccb457a97644c2b08d276f3 NeedsCompilation: no Title: Gene Expression Atlas query and gene set enrichment package. Description: Query the Gene Expression Atlas RDF data at the European Bioinformatics Institute using genes, experimental factors (such as disease, cell type, compound treatments), pathways and proteins. Also contains a function to perform an enrichment of your gene list across Experimental Factor Ontology (EFO) using the Atlas background set. biocViews: Microarray, DataImport, GeneSetEnrichment, GeneExpression, DifferentialExpression, DataRepresentation Author: James Malone, Simon Jupp, Maryam Soleimani Maintainer: James Malone source.ver: src/contrib/AtlasRDF_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/AtlasRDF_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/AtlasRDF_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/AtlasRDF_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/AtlasRDF_1.2.0.tgz vignettes: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.pdf vignetteTitles: An introduction to the AtlasRDF-R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.R Package: attract Version: 1.18.0 Depends: R (>= 2.10.0), AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Imports: Biobase, AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: de6b5d3101b8644e58d99a6eb8c41e0a NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: StatisticalMethod, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar source.ver: src/contrib/attract_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/attract_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/attract_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/attract_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/attract_1.18.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R Package: BAC Version: 1.26.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 56c9ba6186c883426493da9a388741ca NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/BAC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BAC_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BAC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BAC_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BAC_1.26.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R Package: BADER Version: 1.4.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: 5ef8a1d764af9eae8fc53fd35152f448 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker source.ver: src/contrib/BADER_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BADER_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BADER_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BADER_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BADER_1.4.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R Package: BAGS Version: 2.6.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 478d46133ee4d3793b73951a9934f234 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate source.ver: src/contrib/BAGS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BAGS_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BAGS_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BAGS_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BAGS_2.6.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R Package: ballgown Version: 1.0.4 Depends: R (>= 3.0.0), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.1.2), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.25.13), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 6a2274688c6974f9a5aa8b1dde71f2a7 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Leonardo Collado-Torres, Jeffrey T. Leek Maintainer: Alyssa Frazee VignetteBuilder: knitr source.ver: src/contrib/ballgown_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/ballgown_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.1/ballgown_1.0.4.zip mac.binary.ver: bin/macosx/contrib/3.1/ballgown_1.0.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ballgown_1.0.4.tgz vignettes: vignettes/ballgown/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R htmlDocs: vignettes/ballgown/inst/doc/ballgown.html htmlTitles: "Flexible isoform-level differential expression analysis with Ballgown" suggestsMe: polyester Package: BaseSpaceR Version: 1.10.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: f924838cbf270c2cc558a18782ad69a3 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Adrian Alexa source.ver: src/contrib/BaseSpaceR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BaseSpaceR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BaseSpaceR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BaseSpaceR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BaseSpaceR_1.10.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R Package: Basic4Cseq Version: 1.2.0 Depends: R (>= 3.0.0), Biostrings, ShortRead, caTools, GenomicRanges Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 68e5a9a87e69b87cc55fa4c325150a3a NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: Visualization, QualityControl Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Basic4Cseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Basic4Cseq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Basic4Cseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Basic4Cseq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Basic4Cseq_1.2.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R Package: BayesPeak Version: 1.18.2 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: bc9d557432978526814c84e7ea38dc4a NeedsCompilation: yes Title: Bayesian Analysis of ChIP-seq Data Description: This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data. biocViews: ChIPSeq Author: Christiana Spyrou, Jonathan Cairns, Rory Stark, Andy Lynch, Simon Tavar\\'{e}, Maintainer: Jonathan Cairns source.ver: src/contrib/BayesPeak_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/BayesPeak_1.18.2.zip win64.binary.ver: bin/windows64/contrib/3.1/BayesPeak_1.18.2.zip mac.binary.ver: bin/macosx/contrib/3.1/BayesPeak_1.18.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BayesPeak_1.18.2.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesPeak/inst/doc/BayesPeak.R Package: baySeq Version: 2.0.50 Depends: R (>= 2.3.0), methods, GenomicRanges, abind Suggests: snow, edgeR, BiocStyle, BiocGenerics License: GPL-3 MD5sum: 8881f6658431b53b425c48d1364d04a1 NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/baySeq_2.0.50.tar.gz win.binary.ver: bin/windows/contrib/3.1/baySeq_2.0.50.zip win64.binary.ver: bin/windows64/contrib/3.1/baySeq_2.0.50.zip mac.binary.ver: bin/macosx/contrib/3.1/baySeq_2.0.50.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/baySeq_2.0.50.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR suggestsMe: compcodeR, oneChannelGUI, riboSeqR Package: BCRANK Version: 1.28.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 96f7a328f86d71fe756b305bceb53cdd NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur source.ver: src/contrib/BCRANK_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BCRANK_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BCRANK_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BCRANK_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BCRANK_1.28.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R Package: beadarray Version: 2.16.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, ggplot2 Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: GPL-2 Archs: i386, x64 MD5sum: 8a9b3c354a7f445ca3053c110b4dacb7 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr source.ver: src/contrib/beadarray_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/beadarray_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/beadarray_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/beadarray_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/beadarray_2.16.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi Package: beadarraySNP Version: 1.32.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 187bc04a0f2681793a8a65a09f0b21d4 NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting source.ver: src/contrib/beadarraySNP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/beadarraySNP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/beadarraySNP_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/beadarraySNP_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/beadarraySNP_1.32.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R Package: BeadDataPackR Version: 1.18.0 License: GPL-2 Archs: i386, x64 MD5sum: a81481cc2b31e2db9a41c12f917908a2 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith source.ver: src/contrib/BeadDataPackR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BeadDataPackR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BeadDataPackR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BeadDataPackR_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BeadDataPackR_1.18.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray Package: BEAT Version: 1.4.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: d30006c888d83b89f574466ca909e83a NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman source.ver: src/contrib/BEAT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BEAT_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BEAT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BEAT_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BEAT_1.4.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R Package: betr Version: 1.22.0 Depends: R(>= 2.6.0) Imports: Biobase (>= 2.5.5), limma, mvtnorm, methods, stats Suggests: Biobase License: LGPL MD5sum: 3a3f28077879d6e6a99bb93abc608b73 NeedsCompilation: no Title: Identify differentially expressed genes in microarray time-course data Description: The betr package implements the BETR (Bayesian Estimation of Temporal Regulation) algorithm to identify differentially expressed genes in microarray time-course data. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Martin Aryee Maintainer: Martin Aryee source.ver: src/contrib/betr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/betr_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/betr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/betr_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/betr_1.22.0.tgz vignettes: vignettes/betr/inst/doc/betr.pdf vignetteTitles: BETR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betr/inst/doc/betr.R Package: bgafun Version: 1.28.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: c3fd3f79057092d46644d6c0de8b83cb NeedsCompilation: no Title: BGAfun A method to identify specifity determining residues in protein families Description: A method to identify specifity determining residues in protein families using Between Group Analysis biocViews: Classification Author: Iain Wallace Maintainer: Iain Wallace source.ver: src/contrib/bgafun_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bgafun_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bgafun_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bgafun_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bgafun_1.28.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgafun/inst/doc/bgafun.R Package: BGmix Version: 1.26.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: ebf556904fd58dced2e9529ae30eb2a1 NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin source.ver: src/contrib/BGmix_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/BGmix_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BGmix_1.26.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R Package: bgx Version: 1.32.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: de980e2aecd6d0cd1f8fd7f80909c646 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro source.ver: src/contrib/bgx_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bgx_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bgx_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bgx_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bgx_1.32.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R Package: BHC Version: 1.18.0 License: GPL-3 Archs: i386, x64 MD5sum: 90761cede71d0884055df285bdd43327 NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage source.ver: src/contrib/BHC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BHC_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BHC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BHC_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BHC_1.18.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R Package: BicARE Version: 1.24.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 02435134f8be6501de58de3a77297971 NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr source.ver: src/contrib/BicARE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BicARE_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BicARE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BicARE_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BicARE_1.24.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R Package: BiGGR Version: 1.2.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM Imports: hypergraph License: file LICENSE MD5sum: 603bd48943845ddb47939e7cc87620f3 NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases. Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Network, Visualization, Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ source.ver: src/contrib/BiGGR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiGGR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BiGGR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BiGGR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiGGR_1.2.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R Package: bigmemoryExtras Version: 1.10.0 Depends: R (>= 2.12), bigmemory (>= 4.3) Imports: methods, Biobase Suggests: biganalytics, BiocGenerics, RUnit License: Artistic-2.0 OS_type: unix MD5sum: 6189109aac60ec8cdb793109a5e43cfc NeedsCompilation: no Title: An extension of the bigmemory package with added safety, convenience, and a factor class. Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras source.ver: src/contrib/bigmemoryExtras_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/bigmemoryExtras_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bigmemoryExtras_1.10.0.tgz vignettes: vignettes/bigmemoryExtras/inst/doc/bigmemoryExtras.pdf vignetteTitles: bigmemoryExtras hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmemoryExtras/inst/doc/bigmemoryExtras.R Package: bioassayR Version: 1.4.3 Depends: R (>= 3.1.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson Imports: XML Suggests: BiocStyle, RCurl, ape, ChemmineR License: Artistic-2.0 MD5sum: 38a070b79985cc00bb8df3262cfe720e NeedsCompilation: no Title: R library for Bioactivity analysis Description: bioassayR provides tools for statistical analysis of small molecule bioactivity data biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Tyler Backman source.ver: src/contrib/bioassayR_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/bioassayR_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.1/bioassayR_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.1/bioassayR_1.4.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bioassayR_1.4.3.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.pdf vignetteTitles: bioassayR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R Package: Biobase Version: 2.26.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: e5663ffd498e761c61ea845e1ab418eb NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Biobase_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Biobase_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Biobase_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Biobase_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Biobase_2.26.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/Bioconductor.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R, vignettes/Biobase/inst/doc/HowTo.R, vignettes/Biobase/inst/doc/Qviews.R dependsOnMe: a4Base, a4Core, ACME, affy, 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affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 MD5sum: cda6d1c8cfa0cb680efa626f32097e37 NeedsCompilation: no Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocCaseStudies_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocCaseStudies_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocCaseStudies_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocCaseStudies_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocCaseStudies_1.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.2.3 Depends: R (>= 3.1.0) Imports: biocViews (>= 1.33.7), BiocInstaller, graph, devtools (>= 1.4.1), httr, knitr, tools, optparse, codetools, methods Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, knitrBootstrap Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 4ffd4a299bb3ba74ffdd6ee9a4f9975c NeedsCompilation: no Title: Bioconductor-specific package checks Description: Bioconductor-specific package checks biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr source.ver: src/contrib/BiocCheck_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocCheck_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocCheck_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocCheck_1.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocCheck_1.2.3.tgz vignettes: vignettes/BiocCheck/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R htmlDocs: vignettes/BiocCheck/inst/doc/BiocCheck.html htmlTitles: "BiocCheck" Package: BiocGenerics Version: 0.12.1 Depends: methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, oligoClasses, oligo, affyPLM, flowClust, affy, RUnit, DESeq2 License: Artistic-2.0 MD5sum: 1dbae0ae43d794ecb0bd942ea3c01027 NeedsCompilation: no Title: S4 generic functions for Bioconductor Description: S4 generic functions needed by many Bioconductor packages. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocGenerics_0.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocGenerics_0.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocGenerics_0.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocGenerics_0.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocGenerics_0.12.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, AnnotationDbi, AnnotationForge, beadarray, Biobase, Biostrings, BSgenome, bsseq, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cleanUpdTSeq, cn.mops, codelink, copynumber, CopyNumber450k, CRISPRseek, cummeRbund, DESeq, dexus, DNaseR, ensemblVEP, FEM, flowQ, geneplotter, GenomeInfoDb, genomeIntervals, 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GeneOverlap, geneRxCluster, geNetClassifier, GOstats, GraphPAC, GWASTools, hiAnnotator, hiReadsProcessor, iClusterPlus, illuminaio, INPower, inSilicoMerging, kebabs, KEGGREST, M3D, massiR, MeSHDbi, Metab, metagene, metagenomeSeq, metaseqR, MethylAid, Mirsynergy, MLInterfaces, motifStack, MSnID, MultiMed, mzR, NetSAM, nondetects, PAA, PathNet, pathview, pepXMLTab, PhenStat, proBAMr, pRolocGUI, qpgraph, quantro, RBGL, rBiopaxParser, Rcade, Rcpi, Rgraphviz, riboSeqR, roar, ROntoTools, rpx, RTN, rTRM, sangerseqR, SANTA, sapFinder, segmentSeq, SeqArray, seqTools, SeqVarTools, simulatorZ, SNPRelate, SpacePAC, specL, STATegRa, STRINGdb, TCC, TFBSTools, ToPASeq, trackViewer Package: biocGraph Version: 1.28.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 00db115678b49c92466aa358084a261e NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne source.ver: src/contrib/biocGraph_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/biocGraph_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/biocGraph_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/biocGraph_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biocGraph_1.28.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: BiocCaseStudies Package: BiocInstaller Version: 1.16.3 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 584edbed31f92b552417ec1f174e3520 NeedsCompilation: no Title: Install/Update Bioconductor and CRAN Packages Description: Installs/updates Bioconductor and CRAN packages biocViews: Software Author: Dan Tenenbaum and Biocore Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocInstaller_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocInstaller_1.16.3.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocInstaller_1.16.3.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocInstaller_1.16.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocInstaller_1.16.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: affy, affylmGUI, AnnotationHub, BiocCheck, gcrma, oligoClasses, QuasR, webbioc suggestsMe: BSgenome, GOSemSim, metaseqR, pkgDepTools Package: BiocParallel Version: 1.0.3 Depends: methods Imports: parallel, foreach, tools, BatchJobs, BBmisc, BiocGenerics Suggests: doParallel, snow, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr License: GPL-2 | GPL-3 MD5sum: 38d721097cd13f0d734b8e317e9da68e NeedsCompilation: no Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Michel Lang [aut], Ryan Thompson [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/BiocParallel_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocParallel_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocParallel_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocParallel_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocParallel_1.0.3.tgz vignettes: vignettes/BiocParallel/inst/doc/IntroductionToBiocParallel.pdf vignetteTitles: Introduction to BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/Overview.R dependsOnMe: ClassifyR, DEXSeq, GenomicFiles, hiReadsProcessor, MBASED, MSnbase, pRoloc, Rqc, SGSeq, ShortRead, SigCheck importsMe: ChIPQC, derfinder, DESeq2, flowcatchR, GenomicAlignments, gmapR, h5vc, HTSeqGenie, MethylAid, qpgraph, synapter, TFBSTools, VariantFiltering, VariantTools suggestsMe: ALDEx2, chimera, DEGreport, specL, systemPipeR Package: BiocStyle Version: 1.4.1 Suggests: knitr (>= 1.7), rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: c45331dc9d86732910caca65e1ebef8c NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Martin Morgan, Andrzej Oles, Wolfgang Huber Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/BiocStyle_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiocStyle_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/BiocStyle_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/BiocStyle_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiocStyle_1.4.1.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/HtmlStyle.R, vignettes/BiocStyle/inst/doc/LatexStyle.R htmlDocs: vignettes/BiocStyle/inst/doc/HtmlStyle.html htmlTitles: "Bioconductor style for HTML documents" importsMe: Rqc suggestsMe: affycoretools, AnnotationDbi, AnnotationForge, arrayQualityMetrics, ASGSCA, BayesPeak, baySeq, beadarray, bioassayR, BiocParallel, BitSeq, blima, CAFE, ccrepe, CexoR, ChIPQC, ClassifyR, cleaver, clipper, compcodeR, CoRegNet, cosmiq, CRISPRseek, dagLogo, DEGreport, DESeq2, DEXSeq, DiffBind, easyRNASeq, EBImage, EDASeq, EnrichmentBrowser, flowcatchR, FourCSeq, gdsfmt, genefilter, GeneOverlap, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicInteractions, GenomicRanges, GenomicTuples, ggbio, graphite, groHMM, GSAR, Gviz, HiTC, illuminaio, imageHTS, M3D, MBASED, messina, MethylAid, MethylMix, missMethyl, motifStack, mQTL.NMR, MSnID, mygene, mzR, NarrowPeaks, nondetects, npGSEA, omicade4, OncoSimulR, PAA, paxtoolsr, Pbase, plethy, Polyfit, proteoQC, PSEA, qpgraph, quantro, QuasR, rain, Rcade, RefNet, ReQON, rfPred, RGSEA, riboSeqR, Rnits, rols, rpx, Rsamtools, RUVSeq, sangerseqR, sapFinder, segmentSeq, SeqArray, seqplots, SGSeq, shinyMethyl, ShortRead, SigCheck, SigFuge, simulatorZ, SNPRelate, specL, SSPA, STAN, STATegRa, sva, systemPipeR, TFBSTools, tigre, tracktables, trackViewer, TurboNorm, VariantAnnotation, VariantFiltering Package: biocViews Version: 1.34.1 Depends: R (>= 2.4.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, knitr Suggests: Biobase, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: d707901dc437d3644e3b0edd6a4bac11 NeedsCompilation: no Title: Categorized views of R package repositories Description: structures for vocabularies and narratives of views biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org/packages/release/BiocViews.html source.ver: src/contrib/biocViews_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/biocViews_1.34.1.zip win64.binary.ver: bin/windows64/contrib/3.1/biocViews_1.34.1.zip mac.binary.ver: bin/macosx/contrib/3.1/biocViews_1.34.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biocViews_1.34.1.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: BiocCheck Package: bioDist Version: 1.38.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 833358bec49cea3297892a686eed720b NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/bioDist_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bioDist_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bioDist_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bioDist_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bioDist_1.38.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R dependsOnMe: flowQ suggestsMe: BiocCaseStudies Package: biomaRt Version: 2.22.0 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi Suggests: annotate License: Artistic-2.0 MD5sum: 621f6e7d7eb4226ee7c4fdd87934aa2a NeedsCompilation: no Title: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (http://www.biomart.org). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck , Wolfgang Huber Maintainer: Steffen Durinck source.ver: src/contrib/biomaRt_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/biomaRt_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/biomaRt_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/biomaRt_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biomaRt_2.22.0.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.pdf vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: ChIPpeakAnno, customProDB, dagLogo, domainsignatures, DrugVsDisease, genefu, GenomeGraphs, MineICA, PSICQUIC, Roleswitch, VegaMC importsMe: affycoretools, ArrayExpressHTS, cobindR, customProDB, DEXSeq, DOQTL, easyRNASeq, GenomicFeatures, GOexpress, Gviz, HTSanalyzeR, IdMappingRetrieval, MEDIPS, metaseqR, methyAnalysis, oposSOM, phenoTest, R453Plus1Toolbox, RNAither, SeqGSEA suggestsMe: BiocCaseStudies, DEGreport, GeneAnswers, Genominator, h5vc, isobar, massiR, MineICA, MiRaGE, oneChannelGUI, paxtoolsr, Pbase, piano, Rcade, RIPSeeker, rTANDEM, rTRM, ShortRead, SIM, systemPipeR, trackViewer Package: BioMVCClass Version: 1.34.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 47aa83fe34ab0660122b16bcf778248e NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen source.ver: src/contrib/BioMVCClass_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BioMVCClass_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BioMVCClass_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BioMVCClass_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BioMVCClass_1.34.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMVCClass/inst/doc/BioMVCClass.R Package: biomvRCNS Version: 1.6.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: b12d9afd31c746696fb251d55f55b5c2 NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du source.ver: src/contrib/biomvRCNS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/biomvRCNS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/biomvRCNS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/biomvRCNS_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biomvRCNS_1.6.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R Package: BioNet Version: 1.26.1 Depends: R (>= 2.10.0), Biobase, graph, RBGL Imports: igraph (>= 0.7), AnnotationDbi Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 5eb49c4a4c963f7e984b3129e45cd574 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ source.ver: src/contrib/BioNet_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/BioNet_1.26.1.zip win64.binary.ver: bin/windows64/contrib/3.1/BioNet_1.26.1.zip mac.binary.ver: bin/macosx/contrib/3.1/BioNet_1.26.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BioNet_1.26.1.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: HTSanalyzeR suggestsMe: SANTA Package: BioSeqClass Version: 1.24.0 Depends: R (>= 2.10), scatterplot3d Imports: Biostrings, ipred, e1071, klaR, randomForest, class, tree, nnet, rpart, party, foreign, Biobase, utils, stats, grDevices Suggests: scatterplot3d License: LGPL (>= 2.0) MD5sum: c3df180004d7a0c062f4bb0466d78e4b NeedsCompilation: no Title: Classification for Biological Sequences Description: Extracting Features from Biological Sequences and Building Classification Model biocViews: Classification Author: Li Hong sysptm@gmail.com Maintainer: Li Hong source.ver: src/contrib/BioSeqClass_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BioSeqClass_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BioSeqClass_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BioSeqClass_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BioSeqClass_1.24.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioSeqClass/inst/doc/BioSeqClass.R Package: Biostrings Version: 2.34.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.2), IRanges (>= 1.99.27), XVector (>= 0.5.8) Imports: graphics, methods, stats, utils, BiocGenerics, IRanges, XVector, zlibbioc LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: 25049bfa29414e09b5b2dbc34342af34 NeedsCompilation: yes Title: String objects representing biological sequences, and matching algorithms Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Genetics, Sequencing, Infrastructure, DataImport, DataRepresentation Author: H. Pages, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pages source.ver: src/contrib/Biostrings_2.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/Biostrings_2.34.1.zip win64.binary.ver: bin/windows64/contrib/3.1/Biostrings_2.34.1.zip mac.binary.ver: bin/macosx/contrib/3.1/Biostrings_2.34.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Biostrings_2.34.1.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, Basic4Cseq, BRAIN, BSgenome, ChIPpeakAnno, ChIPsim, cleaver, CRISPRseek, DASiR, DECIPHER, deepSNV, GeneRegionScan, genomes, GenomicAlignments, GOTHiC, hiReadsProcessor, iPAC, kebabs, methVisual, minfi, MotifDb, motifRG, oligo, oneChannelGUI, qrqc, R453Plus1Toolbox, REDseq, rGADEM, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, seqbias, ShortRead, systemPipeR, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, ArrayExpressHTS, BCRANK, BEAT, BioSeqClass, biovizBase, BSgenome, charm, ChIPseqR, ChIPsim, CNEr, cobindR, compEpiTools, customProDB, dagLogo, easyRNASeq, ensemblVEP, FourCSeq, gcrma, GeneRegionScan, GenomicAlignments, GenomicFeatures, ggbio, GGtools, girafe, gmapR, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, KEGGREST, MEDIPS, MEDME, methVisual, methylPipe, microRNA, MotIV, oligoClasses, OTUbase, Pbase, pdInfoBuilder, phyloseq, polyester, proBAMr, Pviz, qrqc, QuasR, Rcpi, REDseq, Repitools, rGADEM, Rolexa, Rqc, rSFFreader, rtracklayer, SeqArray, seqplots, SGSeq, SomaticSignatures, synapter, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, CSAR, exomeCopy, GenomicRanges, genoset, methylumi, microRNA, MiRaGE, pcaGoPromoter, procoil, rpx, rTRM, XVector Package: biosvd Version: 2.2.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: 7338d0d5146b97ed9708ba62848ad402 NeedsCompilation: no Title: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling Description: The biosvd package contains functions to reduce the input data set from the feature x assay space to the reduced diagonalized eigenfeature x eigenassay space, with the eigenfeatures and eigenassays unique orthonormal superpositions of the features and assays, respectively. Results of SVD applied to the data can subsequently be inspected based on generated graphs, such as a heatmap of the eigenfeature x assay matrix and a bar plot with the eigenexpression fractions of all eigenfeatures. These graphs aid in deciding which eigenfeatures and eigenassays to filter out (i.e., eigenfeatures representing steady state, noise, or experimental artifacts; or when applied to the variance in the data, eigenfeatures representing steady-scale variance). After possible removal of steady state expression, steady-scale variance, noise and experimental artifacts, and after re-applying SVD to the normalized data, a summary html report of the eigensystem is generated, containing among others polar plots of the assays and features, a table with the list of features sortable according to their coordinates, radius and phase in the polar plot, and a visualization of the data sorted according to the two selected eigenfeatures and eigenassays with colored feature/assay annotation information when provided. This gives a global picture of the dynamics of expression/intensity levels, in which individual features and assays are classified in groups of similar regulation and function or similar cellular state and biological phenotype. biocViews: TimeCourse, Visualization Author: Anneleen Daemen , Matthew Brauer Maintainer: Anneleen Daemen , Matthew Brauer source.ver: src/contrib/biosvd_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/biosvd_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/biosvd_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/biosvd_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biosvd_2.2.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosvd/inst/doc/biosvd.R Package: biovizBase Version: 1.14.1 Depends: R (>= 2.10), methods Imports: methods, grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.2.4), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.19), Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.17.13), AnnotationDbi, VariantAnnotation (>= 1.11.4) Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: 976d6cfa82d6b4ed726d0a05ce2c6b07 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin, Michael Lawrence, Dianne Cook Maintainer: Tengfei Yin source.ver: src/contrib/biovizBase_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/biovizBase_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.1/biovizBase_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.1/biovizBase_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/biovizBase_1.14.1.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: GenoView, ggbio, Gviz, Pviz, qrqc suggestsMe: derfinder, derfinderPlot, regionReport Package: BiRewire Version: 1.8.0 Depends: igraph Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 6c634f22bcec9f91d563565760dec719 NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix) preserving degree distribution (or marginal totals). Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes function for the analysis of the introduced randomness across the switching and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks (not bipartite) is also provided. biocViews: Network Author: Andrea Gobbi [aut], Davide Albanese [cbt], Francesco Iorio [cbt], Giuseppe Jurman [cbt], Julio Saez-Rodriguez [cbt] . Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire source.ver: src/contrib/BiRewire_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiRewire_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BiRewire_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BiRewire_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiRewire_1.8.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R Package: birta Version: 1.10.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 6487a5943ed48ee3f2fda9011dd78c53 NeedsCompilation: yes Title: Bayesian Inference of Regulation of Transcriptional Activity Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states. biocViews: Microarray, Sequencing, GeneExpression, Transcription, GraphAndNetwork Author: Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich Maintainer: Benedikt Zacher , Holger Froehlich source.ver: src/contrib/birta_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/birta_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/birta_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/birta_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/birta_1.10.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birta/inst/doc/birta.R Package: BiSeq Version: 1.6.0 Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomicRanges, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 0171d07d5831a50521bfa9f53158f6f0 NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit source.ver: src/contrib/BiSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BiSeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BiSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BiSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BiSeq_1.6.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R importsMe: M3D Package: BitSeq Version: 1.10.0 Depends: Rsamtools, zlibbioc Imports: S4Vectors, IRanges LinkingTo: Rsamtools, zlibbioc Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 2c17552606c02ea139c76906e0687395 NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis source.ver: src/contrib/BitSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BitSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BitSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BitSeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BitSeq_1.10.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R Package: blima Version: 1.0.0 Depends: R(>= 3.0.0) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), BiocGenerics, grDevices, stats, graphics Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi License: GPL-3 MD5sum: ce36feb0ce61ae5021536f9af8f54fde NeedsCompilation: no Title: Package for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level. Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization Author: Vojtech Kulvait Maintainer: Vojtech Kulvait URL: https://bitbucket.org/kulvait/blima source.ver: src/contrib/blima_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/blima_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/blima_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/blima_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/blima_1.0.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R Package: BRAIN Version: 1.12.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 1433337cf45202a7294f7aedee4c7598 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald source.ver: src/contrib/BRAIN_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BRAIN_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BRAIN_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BRAIN_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BRAIN_1.12.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver Package: BrainStars Version: 1.10.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: b5d4db291b7f8fc33dbeadcca37f608c NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO source.ver: src/contrib/BrainStars_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BrainStars_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BrainStars_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BrainStars_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BrainStars_1.10.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainStars/inst/doc/BrainStars.R Package: bridge Version: 1.30.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: 490100601a4b54de34b2a1bd95c73e7d NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/bridge_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bridge_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bridge_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bridge_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bridge_1.30.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R Package: BridgeDbR Version: 1.0.0 Depends: R (>= 2.0.0), rJava Imports: RCurl License: AGPL-3 MD5sum: b918ad11091c9a749575ed01de080a32 NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R biocViews: Software, Annotation Author: Christ Leemans , Egon Willighagen , Anwesha Bohler Maintainer: Anwesha Bohler URL: https://github.com/bridgedb/BridgeDb, https://github.com/BiGCAT-UM/bridgedb-r source.ver: src/contrib/BridgeDbR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BridgeDbR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BridgeDbR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BridgeDbR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BridgeDbR_1.0.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.pdf vignetteTitles: tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R Package: BSgenome Version: 1.34.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.1.2), S4Vectors (>= 0.0.7), IRanges (>= 1.99.1), GenomeInfoDb (>= 1.1.4), GenomicRanges (>= 1.17.15), Biostrings (>= 2.33.3), rtracklayer (>= 1.25.8) Imports: methods, stats, BiocGenerics, S4Vectors, IRanges, XVector, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocInstaller, BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5, SNPlocs.Hsapiens.dbSNP.20100427, hgu95av2probe, Biobase, RUnit License: Artistic-2.0 MD5sum: 6854bd03123cd15e4ae1436876d90b12 NeedsCompilation: no Title: Infrastructure for Biostrings-based genome data packages Description: Infrastructure shared by all the Biostrings-based genome data packages biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Herve Pages Maintainer: H. Pages source.ver: src/contrib/BSgenome_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/BSgenome_1.34.1.zip win64.binary.ver: bin/windows64/contrib/3.1/BSgenome_1.34.1.zip mac.binary.ver: bin/macosx/contrib/3.1/BSgenome_1.34.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BSgenome_1.34.1.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: CAGEr, chipseq, cleanUpdTSeq, CRISPRseek, GOTHiC, htSeqTools, MEDIPS, motifRG, REDseq, rGADEM importsMe: BEAT, charm, ChIPpeakAnno, chipseq, cobindR, ggbio, girafe, gmapR, Gviz, hiAnnotator, MEDIPS, MethylSeekR, PING, QuasR, R453Plus1Toolbox, Repitools, seqplots, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, MEDIPS, metaseqR, MiRaGE, oneChannelGUI, QDNAseq, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.2.0 Depends: R (>= 2.15), methods, BiocGenerics, IRanges, GenomicRanges, parallel, matrixStats Imports: scales, stats, graphics, Biobase, locfit Suggests: RUnit, bsseqData License: Artistic-2.0 MD5sum: c766e77121970a84ba285a79b074d1d6 NeedsCompilation: no Title: Analyze, manage and store bisulfite sequencing data Description: Tools for analyzing and visualizing bisulfite sequencing data biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq source.ver: src/contrib/bsseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bsseq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bsseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bsseq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bsseq_1.2.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.pdf, vignettes/bsseq/inst/doc/bsseq.pdf vignetteTitles: Analyzing WGBS with bsseq, The bsseq user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R Package: BufferedMatrix Version: 1.30.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: dc2b9df5c9fb22d04ef76f5fec91fbce NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/BufferedMatrix_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BufferedMatrix_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BufferedMatrix_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BufferedMatrix_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BufferedMatrix_1.30.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods Package: BufferedMatrixMethods Version: 1.30.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: c89319c2f2c5501d4dd306b2df66bb18 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: B. M. Bolstad Maintainer: B. M. Bolstad URL: http://www.bmbolstad.com source.ver: src/contrib/BufferedMatrixMethods_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BufferedMatrixMethods_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BufferedMatrixMethods_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BufferedMatrixMethods_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BufferedMatrixMethods_1.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bumphunter Version: 1.6.0 Depends: R (>= 2.10), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils Suggests: RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 30821ef426bf0529d83f1346c08419ff NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison Author: Rafael A. Irizarry [cre, aut], Martin Ayree [aut], Kasper Daniel Hansen [aut], Hector Corrada Hansen [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb] Maintainer: Rafael A. Irizarry URL: https://github.com/ririzarr/bumphunter source.ver: src/contrib/bumphunter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/bumphunter_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/bumphunter_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/bumphunter_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/bumphunter_1.6.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: derfinder suggestsMe: derfinderPlot Package: BUS Version: 1.22.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: f0c1e036e5eb092a6e173648ef829e31 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu source.ver: src/contrib/BUS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/BUS_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/BUS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/BUS_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/BUS_1.22.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R Package: CAFE Version: 1.2.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: c88041ea197b37e87535449e466a99c9 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen source.ver: src/contrib/CAFE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CAFE_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CAFE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CAFE_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CAFE_1.2.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R Package: CAGEr Version: 1.10.3 Depends: methods, R (>= 2.15.0), BSgenome Imports: utils, Rsamtools, GenomicRanges, IRanges, data.table, beanplot, rtracklayer, som, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, FANTOM3and4CAGE Enhances: parallel License: GPL-3 MD5sum: 2129c64c39907f63ba90c0d4fbeeea24 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, HighThroughputSequencing, Transcription, Clustering, Visualization Author: Vanja Haberle, Department of Biology, University of Bergen, Norway Maintainer: Vanja Haberle source.ver: src/contrib/CAGEr_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/CAGEr_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.1/CAGEr_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.1/CAGEr_1.10.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CAGEr_1.10.3.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEr.pdf vignetteTitles: CAGEr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGEr.R Package: CALIB Version: 1.32.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: 97ecbd3d87cd96268fbad34281422bba NeedsCompilation: yes Title: Calibration model for estimating absolute expression levels from microarray data Description: This package contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes. biocViews: Microarray,TwoChannel,Preprocessing Author: Hui Zhao, Kristof Engelen, Bart De Moor and Kathleen Marchal Maintainer: Hui Zhao source.ver: src/contrib/CALIB_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CALIB_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CALIB_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CALIB_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CALIB_1.32.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CALIB/inst/doc/quickstart.R Package: CAMERA Version: 1.22.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5), igraph Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: i386, x64 MD5sum: 9938782ab968d233aec58cb56b5ab799 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, EIC correlation based tagging of unknown adducts and fragments biocViews: MassSpectrometry Author: Carsten Kuhl, Ralf Tautenhahn, Steffen Neumann {ckuhl|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Carsten Kuhl URL: http://msbi.ipb-halle.de/msbi/CAMERA/ source.ver: src/contrib/CAMERA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CAMERA_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CAMERA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CAMERA_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CAMERA_1.22.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf vignetteTitles: Molecule Identification with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, MAIT, metaMS suggestsMe: RMassBank Package: cancerclass Version: 1.10.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: c07a581502aeeb066f30805de507647e NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla source.ver: src/contrib/cancerclass_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cancerclass_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cancerclass_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cancerclass_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cancerclass_1.10.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R Package: CancerMutationAnalysis Version: 1.10.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 0327a2d13bdd20c6492fb2c0ea8f1a15 NeedsCompilation: yes Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca source.ver: src/contrib/CancerMutationAnalysis_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CancerMutationAnalysis_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CancerMutationAnalysis_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CancerMutationAnalysis_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CancerMutationAnalysis_1.10.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: casper Version: 2.0.2 Depends: R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors, sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: a25287132cf1b3e48a7a3bb18d5bfe0b NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell source.ver: src/contrib/casper_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/casper_2.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/casper_2.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/casper_2.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/casper_2.0.2.tgz vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R Package: Category Version: 2.32.0 Depends: methods, stats4, Matrix, BiocGenerics, AnnotationDbi, Biobase, GO.db, Imports: methods, utils, stats, stats4, BiocGenerics, graph, Biobase, AnnotationDbi, RBGL, GSEABase (>= 1.19.3), genefilter, annotate (>= 1.15.6) Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, SNPchip, geneplotter, limma, lattice, graph, Biobase, genefilter, methods, RUnit, org.Sc.sgd.db, GOstats License: Artistic-2.0 MD5sum: 490ebe563eb9356b1a9d3efa52e52940 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: R. Gentleman with contributions from S. Falcon and D.Sarkar Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Category_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Category_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Category_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Category_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Category_2.32.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats, meshr, PCpheno importsMe: categoryCompare, cellHTS2, eisa, gCMAP, GOstats, interactiveDisplay, PCpheno, phenoTest, ppiStats, RDAVIDWebService suggestsMe: BiocCaseStudies, cellHTS, MmPalateMiRNA, qpgraph Package: categoryCompare Version: 1.10.0 Depends: R (>= 2.10), Biobase, BiocGenerics Imports: AnnotationDbi, hwriter, GSEABase, Category, GOstats, annotate, colorspace, graph, RCytoscape (>= 1.5.11) Suggests: knitr, methods, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: cea8fe49ac9039ef35c751e5d16b4590 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 2.8.0) (if used for visualization of results, heavily suggested), CytoscapeRPC plugin (>= 1.8) VignetteBuilder: knitr source.ver: src/contrib/categoryCompare_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/categoryCompare_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/categoryCompare_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/categoryCompare_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/categoryCompare_1.10.0.tgz vignettes: vignettes/categoryCompare/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R htmlDocs: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html htmlTitles: "categoryCompare: High-throughput data meta-analysis using feature annotations" Package: ccrepe Version: 1.2.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: dfdaf233acc7c7136bab202d21717d50 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr source.ver: src/contrib/ccrepe_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ccrepe_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ccrepe_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ccrepe_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ccrepe_1.2.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R Package: cellGrowth Version: 1.10.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: 190013edfc83d4313bf94fecf9f0d6d3 NeedsCompilation: no Title: Fitting cell population growth models Description: This package provides functionalities for the fitting of cell population growth models on experimental OD curves. biocViews: CellBasedAssays, MicrotitrePlateAssay, DataImport, Visualization, TimeCourse Author: Julien Gagneur , Andreas Neudecker Maintainer: Julien Gagneur source.ver: src/contrib/cellGrowth_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cellGrowth_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cellGrowth_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cellGrowth_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cellGrowth_1.10.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellGrowth/inst/doc/cellGrowth.R Package: cellHTS Version: 1.36.0 Depends: R (>= 2.10), prada (>= 1.9.4), RColorBrewer, Biobase (>= 1.11.12), genefilter (>= 1.11.2) Suggests: Category, GO.db, vsn (>= 2.0.35) License: Artistic-2.0 MD5sum: 23d96cb23b92b1e16d66032d721ec120 NeedsCompilation: no Title: Analysis of cell-based screens Description: Analysis of cell-based RNA interference screens biocViews: CellBasedAssays, Visualization Author: Wolfgang Huber , Ligia Bras , Michael Boutros Maintainer: Ligia Bras URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber source.ver: src/contrib/cellHTS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cellHTS_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cellHTS_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cellHTS_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cellHTS_1.36.0.tgz vignettes: vignettes/cellHTS/inst/doc/cellhts.pdf, vignettes/cellHTS/inst/doc/twoChannels.pdf, vignettes/cellHTS/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: two-way assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS/inst/doc/cellhts.R, vignettes/cellHTS/inst/doc/twoChannels.R, vignettes/cellHTS/inst/doc/twoWay.R suggestsMe: prada Package: cellHTS2 Version: 2.30.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4 License: Artistic-2.0 MD5sum: 57ca8044877ee954d1d9fe4edb6ffd51 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber source.ver: src/contrib/cellHTS2_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cellHTS2_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cellHTS2_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cellHTS2_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cellHTS2_2.30.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: coRNAi, imageHTS, staRank importsMe: HTSanalyzeR, RNAinteract Package: CellNOptR Version: 1.12.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML Suggests: RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: 8ec70083cce88b9ac6dfa122ac15b142 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data. Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: T.Cokelaer SystemRequirements: Graphviz version >= 2.2 source.ver: src/contrib/CellNOptR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CellNOptR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CellNOptR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CellNOptR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CellNOptR_1.12.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR Package: CexoR Version: 1.4.0 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 66ba23dd4b41cbcd88f02a5fff81bfb1 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalized count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/CexoR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CexoR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CexoR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CexoR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CexoR_1.4.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R Package: CFAssay Version: 1.0.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 2482637b4670aaaa79438dac41cf0209 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann source.ver: src/contrib/CFAssay_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CFAssay_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CFAssay_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CFAssay_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CFAssay_1.0.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R Package: CGEN Version: 3.0.1 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 7dcab7179cb1f5d8a49174dcf25a38a0 NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: An R package for analysis of case-control studies in genetic epidemiology biocViews: SNP, MultipleComparisons, Clustering Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee, Summer Han and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/CGEN_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/CGEN_3.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/CGEN_3.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/CGEN_3.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CGEN_3.0.1.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R Package: CGHbase Version: 1.26.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 676c008c8618d170ddf99bdad78f710e NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel source.ver: src/contrib/CGHbase_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CGHbase_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CGHbase_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CGHbase_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CGHbase_1.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.28.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 900d585ccd7d7edcfd28672dfb8b86f8 NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel source.ver: src/contrib/CGHcall_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CGHcall_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CGHcall_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CGHcall_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CGHcall_2.28.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, focalCall importsMe: CGHnormaliter, QDNAseq Package: cghMCR Version: 1.24.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: b5eb7598c6f9796b35b0fd60f6fba48f NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang source.ver: src/contrib/cghMCR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cghMCR_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cghMCR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cghMCR_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cghMCR_1.24.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R Package: CGHnormaliter Version: 1.20.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 7c6ad93ec2aebcbca4849967a5cd7744 NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte source.ver: src/contrib/CGHnormaliter_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CGHnormaliter_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CGHnormaliter_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CGHnormaliter_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CGHnormaliter_1.20.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R Package: CGHregions Version: 1.24.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: c11f3f6ad48f1a5042c6d118e2488aad NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse source.ver: src/contrib/CGHregions_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CGHregions_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CGHregions_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CGHregions_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CGHregions_1.24.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 Package: ChAMP Version: 1.4.1 Depends: R (>= 3.0.1), minfi, ChAMPdata, Illumina450ProbeVariants.db Imports: sva, IlluminaHumanMethylation450kmanifest, limma, RPMM, DNAcopy, preprocessCore, impute, marray, wateRmelon,plyr License: GPL-3 MD5sum: bbcabd50f282b2975be43e47abde6626 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number aberrations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber Author: Tiffany Morris [cre, aut], Lee Butcher [aut], Andrew Feber [aut], Andrew Teschendorff [aut], Ankur Chakravarthy [aut] Maintainer: Tiffany Morris source.ver: src/contrib/ChAMP_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChAMP_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ChAMP_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ChAMP_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChAMP_1.4.1.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R Package: charm Version: 2.12.0 Depends: R (>= 2.14.0), Biobase, SQN, fields, RColorBrewer, genefilter Imports: BSgenome, Biobase, oligo (>= 1.11.31), oligoClasses(>= 1.17.39), ff, preprocessCore, methods, stats, Biostrings, IRanges, siggenes, nor1mix, gtools, grDevices, graphics, utils, limma, parallel, sva(>= 3.1.2) Suggests: charmData, BSgenome.Hsapiens.UCSC.hg18, corpcor License: LGPL (>= 2) MD5sum: 661c6bc6cffcbb79e713a4d3bcfb6a37 NeedsCompilation: no Title: Analysis of DNA methylation data from CHARM microarrays Description: This package implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools. biocViews: Microarray, DNAMethylation Author: Martin Aryee, Peter Murakami, Harris Jaffee, Rafael Irizarry Maintainer: Peter Murakami source.ver: src/contrib/charm_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/charm_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/charm_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/charm_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/charm_2.12.0.tgz vignettes: vignettes/charm/inst/doc/charm.pdf vignetteTitles: charm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/charm/inst/doc/charm.R Package: ChemmineOB Version: 1.4.1 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle,knitr,knitcitations,knitrBootstrap Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: df69d97ca336272b8fdbba7a3f4aa7de NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics Author: Kevin Horan, Thomas Girke Maintainer: Kevin Horan URL: http://manuals.bioinformatics.ucr.edu/home/chemminer SystemRequirements: OpenBabel (>= 2.3.1) with headers. http://openbabel.org VignetteBuilder: knitr source.ver: src/contrib/ChemmineOB_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChemmineOB_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ChemmineOB_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ChemmineOB_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChemmineOB_1.4.1.tgz vignettes: vignettes/ChemmineOB/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R htmlDocs: vignettes/ChemmineOB/inst/doc/ChemmineOB.html htmlTitles: "ChemmineOB" suggestsMe: ChemmineR Package: ChemmineR Version: 2.18.1 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, methods, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: RSQLite, scatterplot3d, gplots, fmcsR,snow, RPostgreSQL, BiocStyle,knitr,knitcitations, knitrBootstrap, ChemmineOB (>= 1.3.8), ChemmineDrugs Enhances: ChemmineOB, ChemmineDrugs License: Artistic-2.0 Archs: i386, x64 MD5sum: f1ec7b8adf74d26d604071e8d164afab NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: ChemmineR Team URL: http://manuals.bioinformatics.ucr.edu/home/chemminer VignetteBuilder: knitr source.ver: src/contrib/ChemmineR_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChemmineR_2.18.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ChemmineR_2.18.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ChemmineR_2.18.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChemmineR_2.18.1.tgz vignettes: vignettes/ChemmineR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R htmlDocs: vignettes/ChemmineR/inst/doc/ChemmineR.html htmlTitles: "ChemmineR" dependsOnMe: eiR, fmcsR importsMe: Rchemcpp, Rcpi suggestsMe: bioassayR, ChemmineOB Package: chimera Version: 1.8.5 Depends: Biobase, GenomicRanges (>= 1.13.3), Rsamtools (>= 1.13.1), GenomicAlignments, methods, AnnotationDbi, org.Hs.eg.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: BiocParallel, Rbowtie, geneplotter Enhances: Rsubread, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db License: Artistic-2.0 Archs: i386, x64 MD5sum: e8424fcba973c72907053882fda856b1 NeedsCompilation: yes Title: A package for secondary analysis of fusion products Description: This package facilitates the characterisation of fusion products events. It allows to import fusion data results from the following fusion finders: chimeraScan, bellerophontes, deFuse, FusionFinder, FusionHunter, mapSplice, tophat-fusion, tophat-fusion-post, FusionMap, STAR, Rsubread, fusionCatcher. biocViews: Infrastructure Author: Raffaele A Calogero, Matteo Carrara, Marco Beccuti, Francesca Cordero Maintainer: Raffaele A Calogero SystemRequirements: STAR, TopHat, bowtie and samtools are required for some functionalities source.ver: src/contrib/chimera_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/chimera_1.8.5.zip win64.binary.ver: bin/windows64/contrib/3.1/chimera_1.8.5.zip mac.binary.ver: bin/macosx/contrib/3.1/chimera_1.8.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/chimera_1.8.5.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimera/inst/doc/chimera.R dependsOnMe: oneChannelGUI Package: chipenrich Version: 1.4.0 Depends: R (>= 2.15.1) Imports: chipenrich.data, methods, GenomicRanges (>= 1.10.0), IRanges (>= 1.16.0), mgcv, plyr (>= 1.7.0), lattice, latticeExtra, grid, stringr (>= 0.6), rms Enhances: parallel License: GPL-3 MD5sum: 936cb90470b9f7750b2d5a92bbc4bd2e NeedsCompilation: no Title: Gene set enrichment for ChIP-seq peak data Description: ChIP-Enrich performs gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: Software, Bioinformatics, Enrichment, GeneSetEnrichment Author: Ryan P. Welch [aut, cre, cph], Chee Lee [aut, cre], Raymond G. Cavalcante [aut, cre], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Ryan P. Welch , Chee Lee , Raymond G. Cavalcante source.ver: src/contrib/chipenrich_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/chipenrich_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/chipenrich_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/chipenrich_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/chipenrich_1.4.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich.pdf vignetteTitles: ChIP-Enrich Vignette/Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich.R Package: ChIPpeakAnno Version: 2.16.4 Depends: R (>= 2.10), grid,VennDiagram, biomaRt, IRanges, Biostrings Imports: BiocGenerics (>= 0.1.0), GO.db, BSgenome, GenomicFeatures, AnnotationDbi, limma, multtest Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, org.Hs.eg.db, gplots, RUnit License: GPL (>= 2) MD5sum: b09144726a673bfd5f91f0ebd29d2d9e NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges. Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Herve Pages, Claude Gazin, Nathan Lawson, Jianhong Ou, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu source.ver: src/contrib/ChIPpeakAnno_2.16.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChIPpeakAnno_2.16.4.zip win64.binary.ver: bin/windows64/contrib/3.1/ChIPpeakAnno_2.16.4.zip mac.binary.ver: bin/macosx/contrib/3.1/ChIPpeakAnno_2.16.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPpeakAnno_2.16.4.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.pdf vignetteTitles: ChIPpeakAnno Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R dependsOnMe: REDseq importsMe: FunciSNP, REDseq suggestsMe: oneChannelGUI, RIPSeeker Package: ChIPQC Version: 1.2.2 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, BiocParallel, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils Suggests: BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: fa73d196e5118af0698322d8d066b638 NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark source.ver: src/contrib/ChIPQC_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChIPQC_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.1/ChIPQC_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.1/ChIPQC_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPQC_1.2.2.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R Package: ChIPseeker Version: 1.2.6 Depends: R (>= 3.0) Imports: BiocGenerics, AnnotationDbi, data.table, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, grDevices, gtools, methods, plotrix, plyr, RColorBrewer, rtracklayer, S4Vectors, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: clusterProfiler, ReactomePA, DOSE, GOSemSim, org.Hs.eg.db, knitr License: Artistic-2.0 MD5sum: 9c82a853d11576708eb5adf0250a389b NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/ChIPseeker VignetteBuilder: knitr source.ver: src/contrib/ChIPseeker_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChIPseeker_1.2.6.zip win64.binary.ver: bin/windows64/contrib/3.1/ChIPseeker_1.2.6.zip mac.binary.ver: bin/macosx/contrib/3.1/ChIPseeker_1.2.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPseeker_1.2.6.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.pdf vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison,, and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R suggestsMe: DOSE, GOSemSim, ReactomePA Package: chipseq Version: 1.16.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.0.1), IRanges (>= 1.99.1), GenomicRanges (>= 1.17.7), BSgenome, ShortRead Imports: methods, BiocGenerics, IRanges, BSgenome, GenomicRanges, lattice, ShortRead, stats Suggests: GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 16f291681563e2ab1b56f24ef91356c1 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/chipseq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/chipseq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/chipseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/chipseq_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/chipseq_1.16.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R dependsOnMe: PING importsMe: ChIPQC, HTSeqGenie suggestsMe: ggbio, oneChannelGUI Package: ChIPseqR Version: 1.20.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors, ShortRead Imports: Biostrings, fBasics, GenomicRanges, graphics, grDevices, HilbertVis, IRanges, methods, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 6775641f524754622f9c4a09860108c3 NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg source.ver: src/contrib/ChIPseqR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChIPseqR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ChIPseqR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ChIPseqR_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPseqR_1.20.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R Package: ChIPsim Version: 1.20.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: b236e31fb6d6bd54aa12e84820ad4c53 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg source.ver: src/contrib/ChIPsim_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChIPsim_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ChIPsim_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ChIPsim_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPsim_1.20.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R Package: ChIPXpress Version: 1.8.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 4abee91acc3dabc94a6927ef34b253a6 NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu source.ver: src/contrib/ChIPXpress_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/ChIPXpress_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChIPXpress_1.8.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R Package: chopsticks Version: 1.30.1 Depends: R(>= 2.10.0), survival, methods Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 7f9619865733a4e31dea9860b2c87a64 NeedsCompilation: yes Title: The snp.matrix and X.snp.matrix classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ source.ver: src/contrib/chopsticks_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/chopsticks_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.1/chopsticks_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.1/chopsticks_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/chopsticks_1.30.1.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R Package: chroGPS Version: 1.10.0 Depends: R (>= 2.13.0), IRanges, methods, Biobase, MASS, graphics, stats, changepoint Imports: graphics, cluster, DPpackage, ICSNP Enhances: parallel, XML, rgl License: GPL (>=2.14) MD5sum: 65da4164f4380225b1c35db5e105ab47 NeedsCompilation: no Title: chroGPS: visualizing the epigenome Description: We provide intuitive maps to visualize the association between genetic elements, with emphasis on epigenetics. The approach is based on Multi-Dimensional Scaling. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds. biocViews: Visualization, Clustering Author: Oscar Reina, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/chroGPS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/chroGPS_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/chroGPS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/chroGPS_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/chroGPS_1.10.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chroGPS/inst/doc/chroGPS.R Package: ChromHeatMap Version: 1.20.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: 2a3f9495b423585f5160a7e9addd8f01 NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner source.ver: src/contrib/ChromHeatMap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ChromHeatMap_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ChromHeatMap_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ChromHeatMap_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ChromHeatMap_1.20.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R Package: cisPath Version: 1.6.4 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: a903c3b8e86629d43cb48042acc17edd NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang source.ver: src/contrib/cisPath_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/cisPath_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.1/cisPath_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.1/cisPath_1.6.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cisPath_1.6.4.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R Package: ClassifyR Version: 1.0.18 Depends: R (>= 3.0.3), methods, Biobase, BiocParallel Imports: locfit, ROCR, grid Suggests: limma, edgeR, car, Rmixmod, ggplot2, gridExtra, BiocStyle, pamr, sparsediscrim, PoiClaClu, curatedOvarianData, parathyroidSE, knitr, klaR, gtable, scales License: GPL-3 MD5sum: 958b2051afa61ebb9527a90d79ac8738 NeedsCompilation: no Title: A framework for two-class classification problems, with applications to differential variability and differential distribution testing. Description: The software formalises a framework for classification in R. There are four stages. Data transformation, feature selection, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly does a couple of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, if they have better performing methods. biocViews: Classification, Survival Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr source.ver: src/contrib/ClassifyR_1.0.18.tar.gz win.binary.ver: bin/windows/contrib/3.1/ClassifyR_1.0.18.zip win64.binary.ver: bin/windows64/contrib/3.1/ClassifyR_1.0.18.zip mac.binary.ver: bin/macosx/contrib/3.1/ClassifyR_1.0.18.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ClassifyR_1.0.18.tgz vignettes: vignettes/ClassifyR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R htmlDocs: vignettes/ClassifyR/inst/doc/ClassifyR.html htmlTitles: "An Introduction to the ClassifyR Package" Package: cleanUpdTSeq Version: 1.4.0 Depends: R (>= 2.15), BiocGenerics (>= 0.1.0), BSgenome, BSgenome.Drerio.UCSC.danRer7, GenomicRanges, seqinr, e1071 License: GPL-2 MD5sum: f68ed9864dc1ad0ea6d3c8833cbce741 NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed. Description: This package uses the Naive Bayes classifier (from e1071) to assign probability values to putative polyadenylation sites (pA sites) based on training data from zebrafish. This will allow the user to separate true, biologically relevant pA sites from false, oligodT primed pA sites. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Sarah Sheppard ; Jianhong Ou ; Lihua Julie Zhu source.ver: src/contrib/cleanUpdTSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cleanUpdTSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cleanUpdTSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cleanUpdTSeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cleanUpdTSeq_1.4.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.pdf vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R Package: cleaver Version: 1.4.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: 8692e61d475ead910a679bcb1acf72cc NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr source.ver: src/contrib/cleaver_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cleaver_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cleaver_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cleaver_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cleaver_1.4.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.pdf vignetteTitles: in-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: Pbase, synapter Package: clippda Version: 1.16.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: ebf4e95f2e832e19991c49158c65b48b NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml source.ver: src/contrib/clippda_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/clippda_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/clippda_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/clippda_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clippda_1.16.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R Package: clipper Version: 1.6.2 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, RCytoscape (>= 1.6.3), graphite, ALL, hgu95av2.db, MASS, BiocStyle License: AGPL-3 MD5sum: ed586eb98ec4590c1e41a53faad01027 NeedsCompilation: no Title: Gene set analysis exploiting pathway topology Description: clipper is a package for topological gene set analysis. It implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini source.ver: src/contrib/clipper_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/clipper_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/clipper_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/clipper_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clipper_1.6.2.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R importsMe: ToPASeq suggestsMe: graphite Package: Clomial Version: 1.2.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 01990b87b895c6fc7321ed9310fd292f NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing Author: Habil Zare and Alex Hu Maintainer: Habil Zare source.ver: src/contrib/Clomial_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Clomial_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Clomial_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Clomial_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Clomial_1.2.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Clonality Version: 1.14.0 Depends: R (>= 2.12.2), DNAcopy Imports: DNAcopy, grDevices, graphics, stats, utils Suggests: gdata, DNAcopy License: GPL-3 MD5sum: aa712e13687e6d06077c6623741b4bf8 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: Microarray, CopyNumberVariation, Classification, aCGH Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya source.ver: src/contrib/Clonality_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Clonality_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Clonality_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Clonality_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Clonality_1.14.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R Package: clonotypeR Version: 1.4.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 51f4651b7c9fc7b376283eda9fcfa6e1 NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. . The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy VignetteBuilder: knitr source.ver: src/contrib/clonotypeR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/clonotypeR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/clonotypeR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/clonotypeR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clonotypeR_1.4.0.tgz vignettes: vignettes/clonotypeR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R htmlDocs: vignettes/clonotypeR/inst/doc/clonotypeR.html htmlTitles: "clonotypeR User's Guide" Package: clst Version: 1.14.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 3bb79e18f72c032ab1e47cbee397a90a NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman source.ver: src/contrib/clst_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/clst_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/clst_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/clst_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clst_1.14.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils Package: clstutils Version: 1.14.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 9cd2368b211b7e18df6276f3f0d10374 NeedsCompilation: no Title: Tools for performing taxonomic assignment. Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman source.ver: src/contrib/clstutils_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/clstutils_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/clstutils_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/clstutils_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clstutils_1.14.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R Package: clusterProfiler Version: 2.0.1 Depends: R (>= 3.0.0) Imports: methods, stats4, plyr, ggplot2, AnnotationDbi, GO.db, KEGG.db, DOSE, GOSemSim Suggests: org.Hs.eg.db, ReactomePA, pathview, knitr License: Artistic-2.0 MD5sum: da4ee8a1ab345d4e6d938fc83f2293ab NeedsCompilation: no Title: statistical analysis and visulization of functional profiles for genes and gene clusters Description: This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. biocViews: Clustering, GO, Pathways, Visualization, MultipleComparison, GeneSetEnrichment Author: Guangchuang Yu, Li-Gen Wang Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/clusterProfiler VignetteBuilder: knitr source.ver: src/contrib/clusterProfiler_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/clusterProfiler_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/clusterProfiler_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/clusterProfiler_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clusterProfiler_2.0.1.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.pdf vignetteTitles: An introduction to clusterProfiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R suggestsMe: ChIPseeker, DOSE, GOSemSim, ReactomePA Package: clusterStab Version: 1.38.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 947eee22304ea88df24eae1c4e53f21b NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald source.ver: src/contrib/clusterStab_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/clusterStab_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/clusterStab_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/clusterStab_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/clusterStab_1.38.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R Package: CMA Version: 1.24.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 048501e4e75a57772331db53814ec731 NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Christoph Bernau source.ver: src/contrib/CMA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CMA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CMA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CMA_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CMA_1.24.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R Package: cn.farms Version: 1.14.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 23e525634b09b685a98a9de385c3ccf6 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html source.ver: src/contrib/cn.farms_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cn.farms_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cn.farms_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cn.farms_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cn.farms_1.14.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R Package: cn.mops Version: 1.12.0 Depends: R (>= 2.12), BiocGenerics, Biobase, IRanges, GenomicRanges Imports: methods, graphics, Rsamtools, parallel Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 0131426975aaae2c3f6555c7ff81d73d NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html source.ver: src/contrib/cn.mops_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cn.mops_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cn.mops_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cn.mops_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cn.mops_1.12.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R Package: CNAnorm Version: 1.12.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: 2d897dcb18f295b3937fb5486fe9533c NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, source.ver: src/contrib/CNAnorm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNAnorm_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNAnorm_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNAnorm_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNAnorm_1.12.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNEr Version: 1.2.0 Depends: R (>= 3.0.2) Imports: Biostrings(>= 2.33.4), RSQLite(>= 0.11.4), GenomeInfoDb(>= 1.1.3), GenomicRanges(>= 1.17.11), rtracklayer(>= 1.25.5), XVector(>= 0.5.4), DBI(>= 0.2-7), GenomicAlignments(>= 1.1.9), methods, S4Vectors(>= 0.0.4), IRanges(>= 1.99.6) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz(>= 1.7.4), RUnit, BiocGenerics License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 20aee420a987cb632986182666d31f70 NeedsCompilation: yes Title: CNE detection and visualization. Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: http://ancora.genereg.net/ source.ver: src/contrib/CNEr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNEr_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNEr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNEr_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNEr_1.2.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.pdf vignetteTitles: CNEr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R importsMe: TFBSTools Package: CNORdt Version: 1.8.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 4fb23d9e849be1099e691621c89541e6 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara source.ver: src/contrib/CNORdt_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNORdt_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNORdt_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNORdt_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNORdt_1.8.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette.R Package: CNORfeeder Version: 1.6.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, igraph, Rgraphviz, RUnit, BiocGenerics License: GPL-3 MD5sum: 3f0de19c091114c62ecd8fe4a66fd79a NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: F.Eduati Maintainer: F.Eduati source.ver: src/contrib/CNORfeeder_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNORfeeder_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNORfeeder_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNORfeeder_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNORfeeder_1.6.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R Package: CNORfuzzy Version: 1.8.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: ea1363ec7ba1ec95b35ede66c9de0d54 NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer source.ver: src/contrib/CNORfuzzy_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNORfuzzy_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNORfuzzy_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNORfuzzy_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNORfuzzy_1.8.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R Package: CNORode Version: 1.8.1 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: fba5dffed9fa56878233a9d76b06878f NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer Maintainer: David Henriques source.ver: src/contrib/CNORode_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNORode_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/CNORode_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/CNORode_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNORode_1.8.1.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR Package: CNTools Version: 1.22.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 251f0b05a805afe1372a5d3c3c855233 NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang source.ver: src/contrib/CNTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNTools_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNTools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNTools_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNTools_1.22.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR Package: cnvGSA Version: 1.10.0 Depends: methods, brglm Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 3421394acba498d03ce8274ee830f269 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico ; packaged by Robert Ziman Maintainer: Robert Ziman source.ver: src/contrib/cnvGSA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cnvGSA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cnvGSA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cnvGSA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cnvGSA_1.10.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.R Package: CNVrd2 Version: 1.4.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: cb687353bad88d1e71472e3e0b11398c NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr source.ver: src/contrib/CNVrd2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNVrd2_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNVrd2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNVrd2_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNVrd2_1.4.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R Package: CNVtools Version: 1.60.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: 78bc39871746c3d4f3012f364d0374e5 NeedsCompilation: yes Title: A package to test genetic association with CNV data Description: This package is meant to facilitate the testing of Copy Number Variant data for genetic association, typically in case-control studies. biocViews: GeneticVariability Author: Chris Barnes and Vincent Plagnol Maintainer: Chris Barnes source.ver: src/contrib/CNVtools_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CNVtools_1.60.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CNVtools_1.60.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CNVtools_1.60.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CNVtools_1.60.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVtools/inst/doc/CNVtools-vignette.R Package: cobindR Version: 1.4.0 Imports: yaml, seqinr, Biostrings, biomaRt, BSgenome, methods, gmp, mclust, rtfbs, gplots, IRanges Suggests: RUnit, BiocGenerics Enhances: rGADEM, seqLogo, genoPlotR, parallel, VennDiagram, RColorBrewer, vcd, MotifDb, snowfall License: Artistic-2.0 MD5sum: 447a2755f15d20d5510ec5bbf5bd1bda NeedsCompilation: no Title: Finding Co-occuring motifs of transcription factor binding sites Description: Finding and analysing co-occuring motifs of transcription factor binding sites in groups of genes biocViews: ChIPSeq, CellBiology, MultipleComparison, SequenceMatching Author: Manuela Benary, Stefan Kroeger, Yuehien Lee, Robert Lehmann Maintainer: Manuela Benary source.ver: src/contrib/cobindR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cobindR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cobindR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cobindR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cobindR_1.4.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cobindR/inst/doc/cobindR.R Package: CoCiteStats Version: 1.38.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 3d77efe58c25c28ffa556dda3aa647f6 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/CoCiteStats_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CoCiteStats_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CoCiteStats_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CoCiteStats_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CoCiteStats_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.34.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 22c1b838a43d976f0c55433a37f73646 NeedsCompilation: no Title: Manipulation of Codelink microarray data. Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr source.ver: src/contrib/codelink_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/codelink_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/codelink_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/codelink_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/codelink_1.34.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R Package: CoGAPS Version: 2.0.0 Depends: R (>= 3.0.1), Rcpp (>= 0.11.2), RColorBrewer (>= 1.0.5), gplots (>= 2.8.0) Imports: graphics, grDevices, methods, stats, utils LinkingTo: Rcpp, BH License: GPL (==2) Archs: i386, x64 MD5sum: 942fbf5f2f79deeeb0f1e555c761fc3b NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Microarray Author: Elana J. Fertig, Michael F. Ochs Maintainer: Elana J. Fertig , Michael F. Ochs source.ver: src/contrib/CoGAPS_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CoGAPS_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CoGAPS_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CoGAPS_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CoGAPS_2.0.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf vignetteTitles: GAPS/CoGAPS Users Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.R Package: coGPS Version: 1.10.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: cae83924e0b752f983ae630ad8f8cdc0 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei source.ver: src/contrib/coGPS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/coGPS_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/coGPS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/coGPS_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/coGPS_1.10.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R Package: COHCAP Version: 1.4.0 Depends: WriteXLS, COHCAPanno License: GPL-3 MD5sum: 7e845d26da5d281ae6b93e0a57a63698 NeedsCompilation: no Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: This package provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k methylation array, targeted BS-Seq, etc.). It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/COHCAP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/COHCAP_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/COHCAP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/COHCAP_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/COHCAP_1.4.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R Package: COMPASS Version: 1.4.0 Depends: R (>= 3.0.2) Imports: Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.9.66), shiny, testthat, devtools, Kmisc License: Artistic-2.0 Archs: i386, x64 MD5sum: 751ea0fb13b5af1887dcbb73726dfef0 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: FlowCytometry Author: Lynn Lin [aut], Kevin Ushey [aut], Greg Finak [aut, cre], Raivo Kolde [ctb] (Author of 'pheatmap', which was modified and now used internally in COMPASS) Maintainer: Greg Finak VignetteBuilder: knitr source.ver: src/contrib/COMPASS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/COMPASS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/COMPASS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/COMPASS_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/COMPASS_1.4.0.tgz vignettes: vignettes/COMPASS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R htmlDocs: vignettes/COMPASS/inst/doc/COMPASS.html htmlTitles: "COMPASS" Package: compcodeR Version: 1.2.1 Depends: R (>= 3.0.2), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, gdata, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods Suggests: BiocStyle, EBSeq, DESeq, DESeq2 (>= 1.1.31), baySeq (>= 1.16.0), genefilter, NOISeq, DSS, TCC, samr, NBPSeq Enhances: rpanel License: GPL (>= 2) MD5sum: a8bb19a53e612e520c618a0dbe835e42 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data and interfaces to several packages for performing the differential expression analysis. biocViews: RNASeq, DifferentialExpression Author: Charlotte Soneson Maintainer: Charlotte Soneson VignetteBuilder: knitr source.ver: src/contrib/compcodeR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/compcodeR_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/compcodeR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/compcodeR_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/compcodeR_1.2.1.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R Package: compEpiTools Version: 1.0.3 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 34b0445e9f2698f8f5dbec813b006230 NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: http://genomics.iit.it/groups/computational-epigenomics.html Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/compEpiTools_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/compEpiTools_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/compEpiTools_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/compEpiTools_1.0.3.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R Package: CompGO Version: 1.2.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 7d654689a60fc69454b3bb7a1c5cc37f NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg source.ver: src/contrib/CompGO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CompGO_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CompGO_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CompGO_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CompGO_1.2.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf, vignettes/CompGO/inst/doc/CompGO-vignette.pdf vignetteTitles: Introduction, CompGO-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R, vignettes/CompGO/inst/doc/CompGO.R Package: ConsensusClusterPlus Version: 1.20.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 82d3f74ad28af43bafbf415002b3dde4 NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson source.ver: src/contrib/ConsensusClusterPlus_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ConsensusClusterPlus_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ConsensusClusterPlus_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ConsensusClusterPlus_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ConsensusClusterPlus_1.20.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: convert Version: 1.42.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 0ba11c398eb77877cea2dac8a17a3b56 NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html source.ver: src/contrib/convert_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/convert_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/convert_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/convert_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/convert_1.42.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/convert/inst/doc/convert.R dependsOnMe: maigesPack, TurboNorm suggestsMe: BiocCaseStudies, dyebias, OLIN Package: copa Version: 1.34.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 62ca9c9681ab4a81fa6e65d50f1c4008 NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald source.ver: src/contrib/copa_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/copa_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/copa_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/copa_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/copa_1.34.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R Package: COPDSexualDimorphism Version: 1.2.0 Depends: COPDSexualDimorphism.data, NCBI2R, RColorBrewer, beeswarm, limma, GenomicRanges, gplots, gtools License: LGPL-2.1 MD5sum: 50077d7593c56d7fde168cf416a34f6d NeedsCompilation: no Title: Sexual dimorphic and COPD differential analysis for gene expression and methylation. Description: Sexual dimoprhic and COPD differential (SDCD) analysis contrasts regression coefficients from two stratified analysis. Stratification can be done in two ways: by COPD status or by sex. For COPD-stratified analysis, SDCD analysis contrasts sexual dimorphism between cases and controls, while sex-stratified SDCD analsysis contrasts COPD differential expression pattern between males and females. The package is meant to be used in conjunction with the package limma. biocViews: Software, AssayDomain, Microarray, GeneExpression, DNAMethylation, DifferentialExpression Author: J Fah Sathirapongsasuti Maintainer: J Fah Sathirapongsasuti source.ver: src/contrib/COPDSexualDimorphism_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/COPDSexualDimorphism_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/COPDSexualDimorphism_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/COPDSexualDimorphism_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/COPDSexualDimorphism_1.2.0.tgz vignettes: vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_eQTL.pdf, vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_expression.pdf, vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_methp.pdf vignetteTitles: SDCD eQTL, SDCD Genes, SDCD Methylation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_eQTL.R, vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_expression.R, vignettes/COPDSexualDimorphism/inst/doc/lgrc_sdcd_methp.R Package: copynumber Version: 1.6.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: e3f0cf8e9b4a659804e8c37a576542d2 NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen source.ver: src/contrib/copynumber_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/copynumber_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/copynumber_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/copynumber_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/copynumber_1.6.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R Package: CopyNumber450k Version: 1.2.0 Depends: Biobase, minfi, DNAcopy, preprocessCore, BiocGenerics Imports: methods Suggests: CopyNumber450kData, minfiData License: Artistic-2.0 MD5sum: ba8d81feb4df184e259a394f47709179 NeedsCompilation: no Title: R package for calling CNV from Illumina 450k methylation microarrays Description: This package contains a set of functions that allow CNV calling from Illumina 450k methylation microarrays. biocViews: DNAMethylation, Microarray, Preprocessing, QualityControl, CopyNumberVariation Author: Simon Papillon-Cavanagh, Jean-Philippe Fortin, Nicolas De Jay Maintainer: Simon Papillon-Cavanagh source.ver: src/contrib/CopyNumber450k_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CopyNumber450k_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CopyNumber450k_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CopyNumber450k_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CopyNumber450k_1.2.0.tgz vignettes: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.pdf vignetteTitles: CopyNumber450k User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.R Package: CoRegNet Version: 1.1.7 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 416f291b5aaef7edfd908ffff2f73dfa NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr source.ver: src/contrib/CoRegNet_1.1.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/CoRegNet_1.1.7.zip win64.binary.ver: bin/windows64/contrib/3.1/CoRegNet_1.1.7.zip mac.binary.ver: bin/macosx/contrib/3.1/CoRegNet_1.1.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CoRegNet_1.1.7.tgz vignettes: vignettes/CoRegNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R htmlDocs: vignettes/CoRegNet/inst/doc/CoRegNet.html htmlTitles: "CoRegNet : Reconstruction and integrated analysis of Co-Regulatory Networks" Package: Cormotif Version: 1.12.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 7ae1dbc3bf065fe9c1c7d656fc3abfe1 NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei source.ver: src/contrib/Cormotif_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Cormotif_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Cormotif_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Cormotif_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Cormotif_1.12.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R Package: CorMut Version: 1.8.0 Depends: seqinr,igraph License: GPL-2 MD5sum: 19c75346061523b7888b6113ff757f9e NeedsCompilation: no Title: Detect the correlated mutations based on selection pressure Description: CorMut provides functions for computing kaks for individual sites or specific amino acids and detecting correlated mutations among them. Three methods are provided for detecting correlated mutations ,including conditional selection pressure, mutual information and Jaccard index. The computation consists of two steps: First, the positive selection sites are detected; Second, the mutation correlations are computed among the positive selection sites. Note that the first step is optional. Meanwhile, CorMut facilitates the comparison of the correlated mutations between two conditions by the means of correlated mutation network. biocViews: Sequencing Author: Zhenpeng Li, Yang Huang, Yabo Ouyang, Yiming Shao, Liying Ma Maintainer: Zhenpeng Li source.ver: src/contrib/CorMut_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CorMut_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CorMut_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CorMut_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CorMut_1.8.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CorMut/inst/doc/CorMut.R Package: coRNAi Version: 1.16.0 Depends: R (>= 2.10), cellHTS2, limma, locfit Imports: MASS, gplots, lattice, grDevices, graphics, stats License: Artistic-2.0 MD5sum: 9cca73cc9f9b817ea825bb8614767085 NeedsCompilation: no Title: Analysis of co-knock-down RNAi data Description: Analysis of combinatorial cell-based RNAi screens biocViews: CellBasedAssays Author: Elin Axelsson Maintainer: Elin Axelsson SystemRequirements: Graphviz source.ver: src/contrib/coRNAi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/coRNAi_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/coRNAi_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/coRNAi_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/coRNAi_1.16.0.tgz vignettes: vignettes/coRNAi/inst/doc/coRNAi.pdf vignetteTitles: coRNAi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRNAi/inst/doc/coRNAi.R Package: CORREP Version: 1.32.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: dd594e08e56026844195e5e0ab92237f NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu source.ver: src/contrib/CORREP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CORREP_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CORREP_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CORREP_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CORREP_1.32.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R Package: cosmiq Version: 1.0.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 4dcc214a00ed707776d6f34bfa17257e NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: MassSpectrometry, Metabolomics Author: David Fischer , Christian Panse , Endre Laczko Maintainer: David Fischer , Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html source.ver: src/contrib/cosmiq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cosmiq_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cosmiq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cosmiq_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cosmiq_1.0.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R Package: COSNet Version: 1.0.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: f3d94b3a7359a88423fc3d693ab32584 NeedsCompilation: yes Title: COSNet: Cost-Senstitive network for node label prediction Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca, Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca source.ver: src/contrib/COSNet_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/COSNet_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/COSNet_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/COSNet_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/COSNet_1.0.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R Package: CoverageView Version: 1.2.0 Depends: R (>= 2.10), methods,Rsamtools,rtracklayer Imports: S4Vectors,IRanges,GenomicRanges,GenomicAlignments,parallel,tools License: Artistic-2.0 MD5sum: 9c6ca84dd9ea8161c2680c8b4b37c966 NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy source.ver: src/contrib/CoverageView_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/CoverageView_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CoverageView_1.2.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R Package: cqn Version: 1.12.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 189867355f836f455e4ea8761812d692 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen source.ver: src/contrib/cqn_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cqn_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cqn_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cqn_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cqn_1.12.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R importsMe: tweeDEseq Package: CRImage Version: 1.14.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: e5acf19c3dc9bb936fddd0bd7963121f NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan source.ver: src/contrib/CRImage_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CRImage_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CRImage_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CRImage_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CRImage_1.14.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R Package: CRISPRseek Version: 1.4.2 Depends: R (>= 3.0.1), BiocGenerics, Biostrings, BSgenome, seqinr Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 4892d7a0860a8f09e603c832e75a7edf NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. This package leverages Biostrings and BSgenome packages. biocViews: GeneRegulation, SequenceMatching Author: Lihua Julie Zhu, Benjamin R. Holmes, Herve Pages, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/CRISPRseek_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.1/CRISPRseek_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.1/CRISPRseek_1.4.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CRISPRseek_1.4.2.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R Package: crlmm Version: 1.24.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, SNPchip, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: f16072a138502ade8b7ffed27ba3d4cb NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays. Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie source.ver: src/contrib/crlmm_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/crlmm_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/crlmm_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/crlmm_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/crlmm_1.24.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/AffyGW.R, vignettes/crlmm/inst/doc/CopyNumberOverview.R, vignettes/crlmm/inst/doc/genotyping.R, vignettes/crlmm/inst/doc/gtypeDownstream.R, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.R, vignettes/crlmm/inst/doc/Infrastructure.R importsMe: VanillaICE suggestsMe: ArrayTV, SNPchip Package: CSAR Version: 1.18.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 2576cda4ba5f528d8b55ff557d695eaf NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino source.ver: src/contrib/CSAR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/CSAR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/CSAR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/CSAR_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CSAR_1.18.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R importsMe: NarrowPeaks suggestsMe: NarrowPeaks Package: csaw Version: 1.0.7 Depends: R (>= 3.1.0), GenomicRanges Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, GenomicAlignments, S4Vectors, IRanges, GenomeInfoDb Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 890aedb2a977c796c35a7bb6543d9895 NeedsCompilation: yes Title: ChIP-seq analysis with windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation Author: Aaron Lun , Gordon Smyth Maintainer: Aaron Lun source.ver: src/contrib/csaw_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/csaw_1.0.7.zip win64.binary.ver: bin/windows64/contrib/3.1/csaw_1.0.7.zip mac.binary.ver: bin/macosx/contrib/3.1/csaw_1.0.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/csaw_1.0.7.tgz vignettes: vignettes/csaw/inst/doc/csaw.pdf, vignettes/csaw/inst/doc/csawUserGuide.pdf vignetteTitles: csaw Vignette, csawUserGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R Package: CSSP Version: 1.5.1 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 8f15cb8e1c7f3ab966b842ef5c014726 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo source.ver: src/contrib/CSSP_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/CSSP_1.5.1.zip win64.binary.ver: bin/windows64/contrib/3.1/CSSP_1.5.1.zip mac.binary.ver: bin/macosx/contrib/3.1/CSSP_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/CSSP_1.5.1.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R Package: ctc Version: 1.40.0 Depends: amap License: GPL-2 MD5sum: 8f0334444772cbaa65ef15af3a74111c NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc source.ver: src/contrib/ctc_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ctc_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ctc_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ctc_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ctc_1.40.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R Package: cummeRbund Version: 2.8.2 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite (>= 1.0.0), ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: aa25a0d86bd80a87cc32ffd3ea6d9aba NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff source.ver: src/contrib/cummeRbund_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/cummeRbund_2.8.2.zip win64.binary.ver: bin/windows64/contrib/3.1/cummeRbund_2.8.2.zip mac.binary.ver: bin/macosx/contrib/3.1/cummeRbund_2.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cummeRbund_2.8.2.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependsOnMe: meshr, spliceR suggestsMe: oneChannelGUI Package: customProDB Version: 1.6.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt Imports: IRanges, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.17.13), biomaRt (>= 2.17.1), stringr, RCurl, plyr, VariantAnnotation (>= 1.7.28), rtracklayer, RSQLite, AnnotationDbi Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: fcfafea63fff2dfbfa532518e80cff4d NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search. Description: Generate customized protein sequence database from RNA-Seq data for proteomics search biocViews: Proteomics Author: xiaojing wang Maintainer: xiaojing wang source.ver: src/contrib/customProDB_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/customProDB_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/customProDB_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/customProDB_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/customProDB_1.6.0.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R Package: cycle Version: 1.20.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 6dc01ae725ed220d3ddcf3e66d2ec4a4 NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://itb.biologie.hu-berlin.de/~futschik/software/R/cycle/index.html source.ver: src/contrib/cycle_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/cycle_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/cycle_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/cycle_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/cycle_1.20.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R Package: dagLogo Version: 1.4.1 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, UniProt.ws, RUnit, BiocGenerics, BiocStyle License: GPL (>=2) MD5sum: 4895bc7cc34d5f75f7fabaf3c14dc3ba NeedsCompilation: no Title: dagLogo Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/dagLogo_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/dagLogo_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/dagLogo_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/dagLogo_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/dagLogo_1.4.1.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.pdf vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R Package: daMA Version: 1.38.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 816854ae8a001c8f49932d9bdb363faf NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de source.ver: src/contrib/daMA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/daMA_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/daMA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/daMA_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/daMA_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DART Version: 1.12.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 61eeba788c88eea79886df3da5292f72 NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff Maintainer: Katherine Lawler source.ver: src/contrib/DART_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DART_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DART_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DART_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DART_1.12.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R Package: DASiR Version: 1.6.0 Depends: IRanges, GenomicRanges, XML, Biostrings License: LGPL (>= 3) MD5sum: efab5196e5211a63e9550c1cb29b4276 NeedsCompilation: no Title: Distributed Annotation System in R Description: R package for programmatic retrieval of information from DAS servers biocViews: Annotation Author: Oscar Flores, Anna Mantsoki Maintainer: Oscar Flores , Anna Mantsoki source.ver: src/contrib/DASiR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DASiR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DASiR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DASiR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DASiR_1.6.0.tgz vignettes: vignettes/DASiR/inst/doc/DASiR.pdf vignetteTitles: Programmatic retrieval of information from DAS servers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DASiR/inst/doc/DASiR.R Package: DAVIDQuery Version: 1.26.0 Depends: RCurl (>= 1.4.0), utils License: GPL-2 MD5sum: b5aa32db8065d3bb159182a582058d2e NeedsCompilation: no Title: Retrieval from the DAVID bioinformatics data resource into R Description: Tools to retrieve data from DAVID, the Database for Annotation, Visualization and Integrated Discovery biocViews: Annotation Author: Roger Day, Alex Lisovich Maintainer: Roger Day source.ver: src/contrib/DAVIDQuery_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DAVIDQuery_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DAVIDQuery_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DAVIDQuery_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DAVIDQuery_1.26.0.tgz vignettes: vignettes/DAVIDQuery/inst/doc/DAVIDQuery.pdf vignetteTitles: An R Package for retrieving data from DAVID into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAVIDQuery/inst/doc/DAVIDQuery.R importsMe: IdMappingRetrieval Package: DBChIP Version: 1.10.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: aeb746c91b0afdf82cb3885729fc6126 NeedsCompilation: no Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang source.ver: src/contrib/DBChIP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DBChIP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DBChIP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DBChIP_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DBChIP_1.10.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DBChIP/inst/doc/DBChIP.R Package: ddCt Version: 1.20.0 Depends: R (>= 2.3.0), Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, methods Suggests: RUnit License: LGPL-3 MD5sum: a72114df4b5726a200dd1ad13d1d94bb NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok and Markus Ruschhaupt Maintainer: Jitao David Zhang source.ver: src/contrib/ddCt_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ddCt_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ddCt_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ddCt_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ddCt_1.20.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R Package: ddgraph Version: 1.10.1 Depends: graph, methods, Rcpp Imports: bnlearn (>= 2.8), gtools, pcalg, RColorBrewer, plotrix, MASS LinkingTo: Rcpp Suggests: Rgraphviz, e1071, ROCR, testthat License: GPL-3 Archs: i386, x64 MD5sum: 4892af035393fc21f6bb92c57e63190e NeedsCompilation: yes Title: Distinguish direct and indirect interactions with Graphical Modelling Description: Distinguish direct from indirect interactions in gene regulation and infer combinatorial code from highly correlated variables such as transcription factor binding profiles. The package implements the Neighbourhood Consistent PC algorithm (NCPC) and draws Direct Dependence Graphs to represent dependence structure around a target variable. The package also provides a unified interface to other Graphical Modelling (Bayesian Network) packages for distinguishing direct and indirect interactions. biocViews: GraphAndNetwork Author: Robert Stojnic Maintainer: Robert Stojnic source.ver: src/contrib/ddgraph_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ddgraph_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ddgraph_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ddgraph_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ddgraph_1.10.1.tgz vignettes: vignettes/ddgraph/inst/doc/ddgraph.pdf vignetteTitles: Overview of the 'ddgraph' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddgraph/inst/doc/ddgraph.R Package: DECIPHER Version: 1.12.0 Depends: R (>= 2.13.0), Biostrings (>= 2.31.9), RSQLite (>= 0.9), stats, parallel Imports: methods, S4Vectors, IRanges, XVector LinkingTo: Biostrings, RSQLite, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 22ef5e2c212030b5aaf7935aa750e8b4 NeedsCompilation: yes Title: Database Enabled Code for Ideal Probe Hybridization Employing R Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment Author: Erik Wright Maintainer: Erik Wright source.ver: src/contrib/DECIPHER_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DECIPHER_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DECIPHER_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DECIPHER_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DECIPHER_1.12.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Finding Chimeric Sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/FindChimeras.R Package: DeconRNASeq Version: 1.8.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 0dac6bfbbca57ecb9b9c90504944056e NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong source.ver: src/contrib/DeconRNASeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DeconRNASeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DeconRNASeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DeconRNASeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DeconRNASeq_1.8.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R Package: DEDS Version: 1.40.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: 7c7718f751ca51c4c2c167d6314476e4 NeedsCompilation: yes Title: Differential Expression via Distance Summary for Microarray Data Description: This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach. biocViews: Microarray, DifferentialExpression Author: Yuanyuan Xiao , Jean Yee Hwa Yang . Maintainer: Yuanyuan Xiao source.ver: src/contrib/DEDS_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DEDS_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DEDS_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DEDS_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DEDS_1.40.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEDS/inst/doc/DEDS.R Package: deepSNV Version: 1.12.0 Depends: R (>= 2.13.0), Rsamtools (>= 1.4.3), GenomicRanges, IRanges, Biostrings, VGAM, methods, graphics, VariantAnnotation (>= 1.5.0), parallel Imports: Rsamtools LinkingTo: Rsamtools Suggests: RColorBrewer, knitr License: GPL-3 Archs: i386, x64 MD5sum: de0fa23db59a3049b44716d2657f49f6 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The new shearwater algorithm (beta) computes a Bayes classifier based on a beta- binomial model for variant calling with multiple samples for precisely estimating model parameters such as local error rates and dispersion and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Moritz Gerstung and Niko Beerenwinkel Maintainer: Moritz Gerstung URL: http://www.cbg.ethz.ch/software/deepSNV VignetteBuilder: knitr source.ver: src/contrib/deepSNV_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/deepSNV_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/deepSNV_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/deepSNV_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/deepSNV_1.12.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R suggestsMe: GenomicFiles Package: DEGraph Version: 1.18.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: a0b2a13691fe1c1e4d4051c3413ea5d1 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob source.ver: src/contrib/DEGraph_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DEGraph_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DEGraph_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DEGraph_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DEGraph_1.18.0.tgz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R suggestsMe: graphite, ToPASeq Package: DEGreport Version: 1.0.0 Depends: R (>= 3.0.0), rjags, quantreg Imports: plyr, utils, ggplot2, Nozzle.R1, coda, edgeR Suggests: knitr, biomaRt, RUnit, BiocStyle, BiocGenerics, BiocParallel License: GPL (>=2) MD5sum: 2a5e1ec32f8f935dbaeea4beb9afac12 NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression Author: Lorena Pantano Maintainer: Lorena Pantano SystemRequirements: jags (>= 3.0.0) VignetteBuilder: knitr source.ver: src/contrib/DEGreport_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DEGreport_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DEGreport_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DEGreport_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DEGreport_1.0.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.pdf vignetteTitles: DEGreport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R, vignettes/DEGreport/inst/doc/DEGreportHTML.R htmlDocs: vignettes/DEGreport/inst/doc/DEGreportHTML.html htmlTitles: "DEGReport" Package: DEGseq Version: 1.20.0 Depends: R (>= 2.8.0), qvalue, samr, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 9c2c5d8ed9d9373c9734de1acb9caa91 NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression Author: Likun Wang and Xi Wang . Maintainer: Likun Wang source.ver: src/contrib/DEGseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DEGseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DEGseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DEGseq_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DEGseq_1.20.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R Package: deltaGseg Version: 1.6.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 1f210dc62c3cd4c7cfe2ec9d7338b603 NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/deltaGseg_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/deltaGseg_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/deltaGseg_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/deltaGseg_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/deltaGseg_1.6.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R Package: derfinder Version: 1.0.10 Depends: R(>= 3.1.1) Imports: AnnotationDbi (>= 1.27.9), BiocParallel, bumphunter (>= 1.3.3), derfinderHelper (>= 1.0.0), GenomeInfoDb (>= 1.2.2), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 1.99.28), qvalue, Rsamtools, rtracklayer, S4Vectors (>= 0.2.3) Suggests: biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: cabf94af1778ad63792f84deabbb23a1 NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution Description: Annotation-agnostic differential expression analysis of RNA-seq data by calculating F-statistics at base-pair resolution biocViews: DifferentialExpression, Sequencing, RNASeq, Software Author: Leonardo Collado-Torres [aut, cre], Alyssa C. Frazee [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr source.ver: src/contrib/derfinder_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.1/derfinder_1.0.10.zip win64.binary.ver: bin/windows64/contrib/3.1/derfinder_1.0.10.zip mac.binary.ver: bin/macosx/contrib/3.1/derfinder_1.0.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/derfinder_1.0.10.tgz vignettes: vignettes/derfinder/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder.R, vignettes/derfinder/inst/doc/derfinderAdvanced.R htmlDocs: vignettes/derfinder/inst/doc/derfinder.html, vignettes/derfinder/inst/doc/derfinderAdvanced.html htmlTitles: "Introduction to derfinder", "derfinder advanced details and usage" importsMe: derfinderPlot, regionReport Package: derfinderHelper Version: 1.0.4 Depends: R(>= 3.1.1) Imports: IRanges (>= 1.99.27), Matrix, S4Vectors (>= 0.2.2) Suggests: devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: 14c5ebb633c4881423a9f3cb281c5eeb NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinderHelper VignetteBuilder: knitr source.ver: src/contrib/derfinderHelper_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/derfinderHelper_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.1/derfinderHelper_1.0.4.zip mac.binary.ver: bin/macosx/contrib/3.1/derfinderHelper_1.0.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/derfinderHelper_1.0.4.tgz vignettes: vignettes/derfinderHelper/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R htmlDocs: vignettes/derfinderHelper/inst/doc/derfinderHelper.html htmlTitles: "Introduction to derfinderHelper" importsMe: derfinder Package: derfinderPlot Version: 1.0.3 Depends: R(>= 3.1.1) Imports: derfinder (>= 1.0.0), GenomeInfoDb (>= 1.2.2), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, IRanges (>= 1.99.28), plyr, RColorBrewer, reshape2, scales Suggests: biovizBase, bumphunter, derfinderData (>= 0.99.0), devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 504b6e8ba992125e3bb2909219765d41 NeedsCompilation: no Title: Plotting functions for derfinder Description: Plotting functions for derfinder biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinderPlot VignetteBuilder: knitr source.ver: src/contrib/derfinderPlot_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/derfinderPlot_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/derfinderPlot_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/derfinderPlot_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/derfinderPlot_1.0.3.tgz vignettes: vignettes/derfinderPlot/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R htmlDocs: vignettes/derfinderPlot/inst/doc/derfinderPlot.html htmlTitles: "Introduction to derfinderPlot" importsMe: regionReport Package: DESeq Version: 1.18.0 Depends: BiocGenerics (>= 0.7.5), Biobase (>= 2.21.7), locfit, lattice Imports: genefilter, geneplotter, methods, MASS, RColorBrewer Suggests: pasilla (>= 0.2.10), vsn, gplots License: GPL (>= 3) Archs: i386, x64 MD5sum: d36afc3574f215c11c52d0bdaa11ee69 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution biocViews: Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq source.ver: src/contrib/DESeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DESeq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DESeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DESeq_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DESeq_1.18.0.tgz vignettes: vignettes/DESeq/inst/doc/DESeq.pdf vignetteTitles: Analysing RNA-Seq data with the "DESeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq/inst/doc/DESeq.R dependsOnMe: DBChIP, metaseqR, Polyfit, SeqGSEA, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, easyRNASeq, EDASeq, EDDA, HTSFilter, rnaSeqMap, ToPASeq suggestsMe: BitSeq, compcodeR, DESeq2, dexus, DiffBind, ELBOW, gage, gCMAP, genefilter, GenomicAlignments, GenomicRanges, oneChannelGUI, RUVSeq, SSPA Package: DESeq2 Version: 1.6.3 Depends: S4Vectors, IRanges, GenomicRanges, Rcpp (>= 0.10.1), RcppArmadillo (>= 0.3.4.4) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc LinkingTo: Rcpp, RcppArmadillo Suggests: RUnit, gplots, knitr, RColorBrewer, BiocStyle, airway, pasilla (>= 0.2.10), DESeq, vsn License: GPL (>= 3) Archs: i386, x64 MD5sum: 6c82284fb6cea0126666d8ae25020edc NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution biocViews: Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression, GeneExpression Author: Michael Love (HSPH Boston), Simon Anders, Wolfgang Huber (EMBL Heidelberg) Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/DESeq2_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/DESeq2_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.1/DESeq2_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.1/DESeq2_1.6.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DESeq2_1.6.3.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.pdf vignetteTitles: Analyzing RNA-Seq data with the "DESeq2" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/beginner.R, vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEXSeq, FourCSeq, MLSeq, TCC importsMe: FourCSeq, HTSFilter, phyloseq, ReportingTools, systemPipeR, ToPASeq suggestsMe: BiocGenerics, compcodeR, DiffBind, gage Package: DEXSeq Version: 1.12.2 Depends: Biobase, GenomicRanges, IRanges, DESeq2 (>= 1.5.63), BiocParallel Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter, RColorBrewer Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr Enhances: parallel License: GPL (>= 3) MD5sum: fb33bc363052dac4c82100672dddf1ee NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: Sequencing, RNASeq, DifferentialExpression Author: Simon Anders and Alejandro Reyes , both at EMBL Heidelberg Maintainer: Alejandro Reyes VignetteBuilder: knitr source.ver: src/contrib/DEXSeq_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/DEXSeq_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.1/DEXSeq_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.1/DEXSeq_1.12.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DEXSeq_1.12.2.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.pdf vignetteTitles: Analyzing RNA-seq data for differential exon usage with the "DEXSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R suggestsMe: GenomicRanges, oneChannelGUI Package: dexus Version: 1.6.0 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 191aee34718b83e388a4d72f66d79de5 NeedsCompilation: yes Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer source.ver: src/contrib/dexus_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/dexus_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/dexus_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/dexus_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/dexus_1.6.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dexus/inst/doc/dexus.R Package: DFP Version: 1.24.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 4ee65c47eabed5231ffbb485def74455 NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez source.ver: src/contrib/DFP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DFP_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DFP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DFP_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DFP_1.24.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R Package: DiffBind Version: 1.12.3 Depends: R (>= 2.15.0), GenomicRanges, limma, GenomicAlignments Imports: RColorBrewer, amap, edgeR (>= 2.3.58), gplots, grDevices, stats, utils, IRanges, zlibbioc, lattice, S4Vectors LinkingTo: Rsamtools Suggests: DESeq, Rsamtools, DESeq2, BiocStyle Enhances: rgl, parallel, BiocParallel License: Artistic-2.0 Archs: i386, x64 MD5sum: f75b5398082d67cf90fbc3518d1d91b3 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq peak data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq, DifferentialPeakCalling Author: Rory Stark, Gordon Brown Maintainer: Rory Stark source.ver: src/contrib/DiffBind_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/DiffBind_1.12.3.zip win64.binary.ver: bin/windows64/contrib/3.1/DiffBind_1.12.3.zip mac.binary.ver: bin/macosx/contrib/3.1/DiffBind_1.12.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DiffBind_1.12.3.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC Package: diffGeneAnalysis Version: 1.48.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 94e823042c57df15c825f59166805a90 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi source.ver: src/contrib/diffGeneAnalysis_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/diffGeneAnalysis_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.1/diffGeneAnalysis_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.1/diffGeneAnalysis_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/diffGeneAnalysis_1.48.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R Package: DirichletMultinomial Version: 1.8.0 Depends: S4Vectors, IRanges Imports: stats4, methods Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 8e27ca2211b3ef29cf8e3af7f0137d44 NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: Sequencing, Clustering, Classification Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl source.ver: src/contrib/DirichletMultinomial_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DirichletMultinomial_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DirichletMultinomial_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DirichletMultinomial_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DirichletMultinomial_1.8.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: TFBSTools Package: dks Version: 1.12.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: c43d167f0face5e5e34967e446ac81c0 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek source.ver: src/contrib/dks_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/dks_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/dks_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/dks_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/dks_1.12.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R Package: DMRcate Version: 1.2.0 Depends: R (>= 3.1.0), limma, minfi, DMRcatedata Imports: methods, graphics Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomicRanges License: file LICENSE MD5sum: c830015f1d5d5c89f3aa5e345806e5e5 NeedsCompilation: no Title: Illumina 450K methylation array spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) in the human genome using Illumina Infinium HumanMethylation450 BeadChip array data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes bedGraph generation, GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr source.ver: src/contrib/DMRcate_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DMRcate_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DMRcate_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DMRcate_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DMRcate_1.2.0.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R Package: DMRforPairs Version: 1.2.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: d1f8335250cb835d2d40d97ffa4c3fad NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ source.ver: src/contrib/DMRforPairs_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DMRforPairs_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DMRforPairs_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DMRforPairs_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DMRforPairs_1.2.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R Package: DNAcopy Version: 1.40.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: 181b1dc1e66e337026d26a5eb7805461 NeedsCompilation: yes Title: DNA copy number data analysis Description: Segments DNA copy number data using circular binary segmentation to detect regions with abnormal copy number biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan source.ver: src/contrib/DNAcopy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DNAcopy_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DNAcopy_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DNAcopy_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DNAcopy_1.40.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CopyNumber450k, CRImage, MEDIPS, snapCGH, SomatiCA importsMe: ADaCGH2, ArrayTV, ChAMP, Clonality, cn.farms, CNAnorm, CNVrd2, GWASTools, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH, SomatiCA suggestsMe: beadarraySNP, Clonality, cn.mops, fastseg, genoset Package: DNaseR Version: 1.4.0 Depends: R (>= 2.10.0), BiocGenerics, S4Vectors, IRanges Imports: GenomeInfoDb, GenomicRanges, Rsamtools Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 771a09cc0f0bc1c28f8b585422b55f61 NeedsCompilation: no Title: DNase I footprinting analysis of DNase-seq data Description: Strand-specific digital genomic footprinting in 'double-hit' DNase-seq data. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalized count differences of opposed sign at each DNA strand. This is done in order to determine the protein-binding footprint flanks. Preprocessing of the mapped reads is recommended before running DNaseR (e.g., quality checking and removal of sequence-specific biases). biocViews: Transcription, Genetics Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/DNaseR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DNaseR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DNaseR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DNaseR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DNaseR_1.4.0.tgz vignettes: vignettes/DNaseR/inst/doc/DNaseR.pdf vignetteTitles: DNaseR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNaseR/inst/doc/DNaseR.R Package: domainsignatures Version: 1.26.0 Depends: R (>= 2.4.0), KEGG.db, prada, biomaRt, methods Imports: AnnotationDbi License: Artistic-2.0 MD5sum: 50772b4b739894be3085780228c136fc NeedsCompilation: no Title: Geneset enrichment based on InterPro domain signatures Description: Find significantly enriched gene classifications in a list of functionally undescribed genes based on their InterPro domain structure. biocViews: Annotation, Pathways, GeneSetEnrichment Author: Florian Hahne, Tim Beissbarth Maintainer: Florian Hahne source.ver: src/contrib/domainsignatures_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/domainsignatures_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/domainsignatures_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/domainsignatures_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/domainsignatures_1.26.0.tgz vignettes: vignettes/domainsignatures/inst/doc/domainenrichment.pdf vignetteTitles: Gene set enrichment using InterPro domain signatures hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/domainsignatures/inst/doc/domainenrichment.R Package: DOQTL Version: 1.0.0 Depends: R (>= 2.10.0) Imports: annotate, annotationTools, biomaRt, Biobase, BiocGenerics, corpcor, GenomicRanges, hwriter, IRanges, mclust, MUGAExampleData, org.Hs.eg.db, org.Mm.eg.db, QTLRel, Rsamtools, RUnit, XML License: GPL-3 Archs: i386, x64 MD5sum: 7d222e3aec9752ea8845fa2db13023ab NeedsCompilation: yes Title: Genotyping and QTL Mapping in DO Mice Description: DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel Author: Daniel Gatti, Karl Broman, Andrey Shabalin Maintainer: Daniel Gatti URL: http://do.jax.org source.ver: src/contrib/DOQTL_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DOQTL_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DOQTL_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DOQTL_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DOQTL_1.0.0.tgz vignettes: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.pdf vignetteTitles: QTL Mapping using Diversity Outbred Mice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.R Package: DOSE Version: 2.4.0 Depends: R (>= 3.0.0) Imports: methods, plyr, qvalue, stats4, AnnotationDbi, DO.db, igraph, scales, reshape2, graphics, GOSemSim, grid, ggplot2 Suggests: org.Hs.eg.db, clusterProfiler, ReactomePA, ChIPseeker, knitr License: Artistic-2.0 MD5sum: d83bed94a4e50f6f2ec7cf0ae652c04b NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment Author: Guangchuang Yu, Li-Gen Wang Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/DOSE VignetteBuilder: knitr source.ver: src/contrib/DOSE_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DOSE_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DOSE_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DOSE_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DOSE_2.4.0.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.pdf vignetteTitles: DOSE - an R package for Disease Ontology Semantic and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: clusterProfiler, facopy, ReactomePA suggestsMe: ChIPseeker, GOSemSim Package: DriverNet Version: 1.6.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: e382ca15093cc496ddfc42014ace5306 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding source.ver: src/contrib/DriverNet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DriverNet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DriverNet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DriverNet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DriverNet_1.6.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R Package: DrugVsDisease Version: 2.6.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: 00ab00a921bfd2b89e88fd0cc72ebe79 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez source.ver: src/contrib/DrugVsDisease_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DrugVsDisease_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DrugVsDisease_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DrugVsDisease_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DrugVsDisease_2.6.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R Package: DSS Version: 2.4.1 Depends: Biobase,splines Imports: methods,bsseq,edgeR License: GPL MD5sum: 9f6562e200ceefd6d3d9399846b04486 NeedsCompilation: no Title: Dispersion shrinakge for sequencing data. Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, ChIPSeq, DNAMethylation, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu source.ver: src/contrib/DSS_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/DSS_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/DSS_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/DSS_2.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DSS_2.4.1.tgz vignettes: vignettes/DSS/inst/doc/DSS.pdf vignetteTitles: Differential expression for RNA-seq data with dispersion shrinkage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R suggestsMe: compcodeR Package: DTA Version: 2.12.1 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: a5db5ed0f297e47780988529a2992bfe NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb source.ver: src/contrib/DTA_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/DTA_2.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/DTA_2.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/DTA_2.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DTA_2.12.1.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R Package: dualKS Version: 1.26.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: cf4922adb4a8877420519800eb2eacec NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang source.ver: src/contrib/dualKS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/dualKS_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/dualKS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/dualKS_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/dualKS_1.26.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R Package: DupChecker Version: 1.4.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: 19111e09132370e85f2f469780c16835 NeedsCompilation: no Title: a package for checking high-throughput genomic data redundancy in meta-analysis Description: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. biocViews: Preprocessing Author: Quanhu Sheng, Yu Shyr, Xi Chen Maintainer: "Quanhu SHENG" VignetteBuilder: knitr source.ver: src/contrib/DupChecker_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DupChecker_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DupChecker_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DupChecker_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DupChecker_1.4.0.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DupChecker/inst/doc/DupChecker.R Package: dyebias Version: 1.24.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 0051b90ae2ab900d7bf47310c222aa08 NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad source.ver: src/contrib/dyebias_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/dyebias_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/dyebias_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/dyebias_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/dyebias_1.24.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R Package: DynDoc Version: 1.44.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: e53b08fac3bd8127703bf0b9bc196a6d NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/DynDoc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/DynDoc_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/DynDoc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/DynDoc_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/DynDoc_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.8.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: cea43a3a4e8972d38026308b7662f89a NeedsCompilation: no Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain source.ver: src/contrib/EasyqpcR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EasyqpcR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EasyqpcR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EasyqpcR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EasyqpcR_1.8.0.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.R Package: easyRNASeq Version: 2.2.1 Imports: Biobase (>= 2.26.0), BiocGenerics (>= 0.12.1), biomaRt (>= 2.22.0), Biostrings (>= 2.34.1), DESeq (>= 1.18.0), edgeR (>= 3.8.5), genomeIntervals (>= 1.22.0), GenomicAlignments (>= 1.2.1), GenomeInfoDb (>= 1.2.4), GenomicRanges (>= 1.18.4), graphics, IRanges (>= 2.0.1), LSD (>= 3.0), methods, parallel, Rsamtools (>= 1.18.2), S4Vectors (>= 0.4.0), ShortRead (>= 1.24.0), utils Suggests: BiocStyle (>= 1.4.1), BSgenome (>= 1.34.1), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), GenomicFeatures (>= 1.18.3), RnaSeqTutorial (>= 0.3.1), RUnit (>= 0.4.28) License: Artistic-2.0 MD5sum: e4ea6ac2d75661456bf6bf56107efaf4 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data. Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler Maintainer: Nicolas Delhomme source.ver: src/contrib/easyRNASeq_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/easyRNASeq_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/easyRNASeq_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/easyRNASeq_2.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/easyRNASeq_2.2.1.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf vignetteTitles: easyRNASeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R suggestsMe: SeqGSEA Package: EBarrays Version: 2.30.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: b12e203d01255bf4d62efa56bc1e0c9a NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan source.ver: src/contrib/EBarrays_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EBarrays_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EBarrays_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EBarrays_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EBarrays_2.30.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category Package: EBcoexpress Version: 1.10.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: 663a938efacdedb6701581a1eca7f9f0 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson source.ver: src/contrib/EBcoexpress_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EBcoexpress_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EBcoexpress_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EBcoexpress_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EBcoexpress_1.10.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R Package: EBImage Version: 4.8.3 Imports: BiocGenerics (>= 0.7.1), methods, graphics, grDevices, stats, abind, tiff, jpeg, png, locfit Suggests: BiocStyle License: LGPL Archs: i386, x64 MD5sum: b8c7a454adc1f5dc1cc64bafede1dd6d NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage is an R package which provides general purpose functionality for the reading, writing, processing and analysis of images. Furthermore, in the context of microscopy based cellular assays, EBImage offers tools to transform the images, segment cells and extract quantitative cellular descriptors. biocViews: Visualization Author: Andrzej Oles, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oles source.ver: src/contrib/EBImage_4.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/EBImage_4.8.3.zip win64.binary.ver: bin/windows64/contrib/3.1/EBImage_4.8.3.zip mac.binary.ver: bin/macosx/contrib/3.1/EBImage_4.8.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EBImage_4.8.3.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.pdf vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: flowCHIC suggestsMe: HilbertVis Package: EBSeq Version: 1.6.0 Depends: blockmodeling, gplots, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 466df0b7f02334eabb938f3ecba0c276 NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EBSeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EBSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EBSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EBSeq_1.6.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM suggestsMe: compcodeR Package: EBSeqHMM Version: 1.0.0 Depends: EBSeq License: Artistic-2.0 MD5sum: b083024ec27619db69b276778582bbc5 NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeqHMM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EBSeqHMM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EBSeqHMM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EBSeqHMM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EBSeqHMM_1.0.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R Package: ecolitk Version: 1.38.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: d11b56976989ac0af095d095b8a55de7 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/ecolitk_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ecolitk_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ecolitk_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ecolitk_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ecolitk_1.38.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R Package: EDASeq Version: 2.0.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), DESeq, aroma.light, Rsamtools (>= 1.5.75) Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR License: Artistic-2.0 MD5sum: c0ab648cbe8bd0ac60deb33038640811 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso and Sandrine Dudoit Maintainer: Davide Risso VignetteBuilder: knitr source.ver: src/contrib/EDASeq_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/EDASeq_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/EDASeq_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/EDASeq_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EDASeq_2.0.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.pdf vignetteTitles: EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq suggestsMe: HTSFilter, oneChannelGUI Package: EDDA Version: 1.5.3 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: cbf403b95d3be1835fbe049894e6124c NeedsCompilation: yes Title: Experimental Design in Differential Abundance analysis Description: EDDA is a tool for systematic assessment of the impact of experimental design and the statistical test used on the ability to detect differential abundance. EDDA can aid in the design of a range of common experiments such as RNA-seq, ChIP-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. More details of EDDA can be found at Luo, Huaien et al. "The Importance of Study Design for Detecting Differentially Abundant Features in High-Throughput Experiments." Genome Biology 2014;15(12):527 (http://www.ncbi.nlm.nih.gov/pubmed/25517037/). An accompanying web server (http://edda.gis.a-star.edu.sg/) is available for easy access to some functionality of EDDA. biocViews: Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://csb5.github.io/EDDA/ source.ver: src/contrib/EDDA_1.5.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/EDDA_1.5.3.zip win64.binary.ver: bin/windows64/contrib/3.1/EDDA_1.5.3.zip mac.binary.ver: bin/macosx/contrib/3.1/EDDA_1.5.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EDDA_1.5.3.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDDA/inst/doc/EDDA.R Package: edgeR Version: 3.8.6 Depends: R (>= 2.15.0), limma Imports: methods Suggests: MASS, statmod, splines, locfit, KernSmooth License: GPL (>=2) Archs: i386, x64 MD5sum: fb93436efec1f42cf46763368e2a8b54 NeedsCompilation: yes Title: Empirical analysis of digital gene expression data in R Description: Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication. Uses empirical Bayes estimation and exact tests based on the negative binomial distribution. Also useful for differential signal analysis with other types of genome-scale count data. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression, TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect, MultipleComparison, Normalization, QualityControl Author: Yunshun Chen , Davis McCarthy , Aaron Lun , Xiaobei Zhou , Mark Robinson , Gordon Smyth Maintainer: Yunshun Chen , Aaron Lun , Mark Robinson , Davis McCarthy , Gordon Smyth URL: http://bioinf.wehi.edu.au/edgeR source.ver: src/contrib/edgeR_3.8.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/edgeR_3.8.6.zip win64.binary.ver: bin/windows64/contrib/3.1/edgeR_3.8.6.zip mac.binary.ver: bin/macosx/contrib/3.1/edgeR_3.8.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/edgeR_3.8.6.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/edgeR/inst/doc/edgeR.R dependsOnMe: DBChIP, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, compcodeR, csaw, DEGreport, DiffBind, easyRNASeq, EDDA, erccdashboard, HTSFilter, MEDIPS, metaseqR, msmsTests, Repitools, rnaSeqMap, systemPipeR, ToPASeq, tweeDEseq suggestsMe: baySeq, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, missMethyl, oneChannelGUI, SSPA Package: eiR Version: 1.6.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods,ChemmineR,RCurl,digest, BiocGenerics LinkingTo: BH Suggests: RCurl,snow,BiocStyle,knitcitations,knitr,knitrBootstrap License: Artistic-2.0 MD5sum: cfb2bdbca2eb6c093fc8e05346d163e3 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Infrastructure, DataImport, Clustering, Proteomics Author: Kevin Horan Maintainer: Kevin Horan VignetteBuilder: knitr source.ver: src/contrib/eiR_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/eiR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/eiR_1.6.0.tgz vignettes: vignettes/eiR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R htmlDocs: vignettes/eiR/inst/doc/eiR.html htmlTitles: "eiR" Package: eisa Version: 1.18.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) MD5sum: 18c14b1943beac9470789d0c432422ae NeedsCompilation: no Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi source.ver: src/contrib/eisa_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/eisa_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/eisa_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/eisa_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/eisa_1.18.0.tgz vignettes: vignettes/eisa/inst/doc/EISA_biclust.pdf, vignettes/eisa/inst/doc/EISA_tutorial.pdf vignetteTitles: The eisa and the biclust packages, The Iterative Signature Algorithm for Gene Expression Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisa/inst/doc/EISA_biclust.R, vignettes/eisa/inst/doc/EISA_tutorial.R dependsOnMe: ExpressionView importsMe: ExpressionView Package: ELBOW Version: 1.2.0 Depends: R (>= 2.15.0) Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: c6c3bd47d89f60abb33759bc1f7dbe2e NeedsCompilation: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang source.ver: src/contrib/ELBOW_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ELBOW_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ELBOW_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ELBOW_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ELBOW_1.2.0.tgz vignettes: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.pdf vignetteTitles: Using ELBOW --- the definitive ELBOW tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.R Package: EnrichmentBrowser Version: 1.0.3 Depends: R(>= 3.0.0), Biobase, KEGGgraph Imports: KEGGREST, MASS, RColorBrewer, Rgraphviz, SPIA, graph, limma, mixtools, neaGUI, qvalue, safe, stringr Suggests: ALL, BiocStyle, hgu95av2.db License: Artistic-2.0 MD5sum: 9de03494deefc1e50907cc21e9c4dd43 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. In addition, the package facilitates the visualization and exploration of such sets and pathways. biocViews: Microarray, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger Maintainer: Ludwig Geistlinger source.ver: src/contrib/EnrichmentBrowser_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/EnrichmentBrowser_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/EnrichmentBrowser_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/EnrichmentBrowser_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/EnrichmentBrowser_1.0.3.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R Package: ensemblVEP Version: 1.6.2 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: Biostrings Suggests: RUnit License: Artistic-2.0 MD5sum: 3e52642ecf2c3a2b6d3c0e70243509d7 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API biocViews: Annotation Author: Valerie Obenchain , Maintainer: Valerie Obenchain SystemRequirements: Ensembl VEP (API version 78) and the Perl package DBD::mysql must be installed. See the package README and Ensembl web site, http://www.ensembl.org/info/docs/tools/vep/index.html for installation instructions. source.ver: src/contrib/ensemblVEP_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/ensemblVEP_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/ensemblVEP_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/ensemblVEP_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ensemblVEP_1.6.2.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf vignetteTitles: ensemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R Package: ENVISIONQuery Version: 1.14.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 68bbeb046aeb5e117217dafb183ee7ab NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/ENVISIONQuery_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ENVISIONQuery_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ENVISIONQuery_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ENVISIONQuery_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ENVISIONQuery_1.14.0.tgz vignettes: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.pdf vignetteTitles: An R Package for retrieving data from EnVision into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.R dependsOnMe: IdMappingRetrieval importsMe: IdMappingRetrieval Package: epigenomix Version: 1.6.0 Depends: R (>= 2.12.0), methods, Biobase, IRanges, GenomicRanges Imports: BiocGenerics, Rsamtools, beadarray License: LGPL-3 MD5sum: 60cbd41ceb3a5d43ef6e1aebdbcbc982 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein source.ver: src/contrib/epigenomix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/epigenomix_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/epigenomix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/epigenomix_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/epigenomix_1.6.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R Package: epivizr Version: 1.4.6 Depends: R (>= 3.0.1), methods, Biobase, GenomicRanges (>= 1.13.47) Imports: S4Vectors, httpuv (>= 1.3.0), rjson, OrganismDbi, R6 (>= 2.0.0), mime (>= 0.2), GenomeInfoDb, GenomicFeatures Suggests: testthat, roxygen2, knitr, antiProfilesData, hgu133plus2.db, knitrBootstrap, Mus.musculus License: Artistic-2.0 MD5sum: e8e9c84dfa782869e086810400c04dbf NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides Websocket communication to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and SummarizedExperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/epivizr_1.4.6.zip win64.binary.ver: bin/windows64/contrib/3.1/epivizr_1.4.6.zip mac.binary.ver: bin/macosx/contrib/3.1/epivizr_1.4.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/epivizr_1.4.6.tgz vignettes: vignettes/epivizr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R htmlDocs: vignettes/epivizr/inst/doc/IntroToEpivizr.html htmlTitles: "Introduction to epivizr" Package: erccdashboard Version: 1.0.0 Depends: R (>= 3.1), ggplot2, gridExtra Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, QuasiSeq, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: 5d078eaa5b8d29f4d14299d53130a381 NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/usnistgov/erccdashboard, http://tinyurl.com/erccsrm source.ver: src/contrib/erccdashboard_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/erccdashboard_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/erccdashboard_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/erccdashboard_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/erccdashboard_1.0.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R Package: ExiMiR Version: 2.8.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) License: GPL-2 MD5sum: e0f6be5e02fd892bf29d83dded6dae9f NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian source.ver: src/contrib/ExiMiR_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ExiMiR_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ExiMiR_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ExiMiR_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ExiMiR_2.8.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R Package: exomeCopy Version: 1.12.0 Depends: IRanges, GenomicRanges, Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: 9e83f9ea655d4a9020e28b4e12e74795 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love source.ver: src/contrib/exomeCopy_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/exomeCopy_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/exomeCopy_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/exomeCopy_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/exomeCopy_1.12.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: Rariant Package: exomePeak Version: 1.6.0 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer License: GPL-2 MD5sum: 7ffd7b622057568357e7bbdbdee18443 NeedsCompilation: no Title: exome-based anlaysis of MeRIP-Seq data: peak calling and differential analysis Description: The package is developed for the analysis of affinity-based epitranscriptome shortgun sequencing data from MeRIP-seq (maA-seq). It was built on the basis of the exomePeak MATLAB package (Meng, Jia, et al. "Exome-based analysis for RNA epigenome sequencing data." Bioinformatics 29.12 (2013): 1565-1567.) with new functions for differential analysis of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome. The exomePeak R-package accepts and statistically supports multiple biological replicates, internally removes PCR artifacts and multi-mapping reads, outputs exome-based binding sites (RNA methylation sites) and detects differential post-transcriptional RNA modification sites between two experimental conditions in term of percentage rather the absolute amount. The package is still under active development, and we welcome all biology and computation scientist for all kinds of collaborations and communications. Please feel free to contact Dr. Jia Meng if you have any questions. biocViews: Sequencing, HighThroughputSequencing, Methylseq, RNAseq Author: Jia Meng Maintainer: Jia Meng source.ver: src/contrib/exomePeak_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/exomePeak_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/exomePeak_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/exomePeak_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/exomePeak_1.6.0.tgz vignettes: vignettes/exomePeak/inst/doc/exomePeak-Overview.pdf vignetteTitles: An introduction to exomePeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak/inst/doc/exomePeak-Overview.R Package: explorase Version: 1.30.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 775956341362f9b8571b81add20f07d4 NeedsCompilation: no Title: GUI for exploratory data analysis of systems biology data Description: explore and analyze *omics data with R and GGobi biocViews: Visualization,Microarray,GUI Author: Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim Maintainer: Michael Lawrence URL: http://www.metnetdb.org/MetNet_exploRase.htm source.ver: src/contrib/explorase_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/explorase_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/explorase_1.30.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/explorase/inst/doc/explorase.R Package: ExpressionView Version: 1.18.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: 57e7253059e4d444d7070664b23f3335 NeedsCompilation: yes Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Andreas Luscher Maintainer: Gabor Csardi source.ver: src/contrib/ExpressionView_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ExpressionView_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ExpressionView_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ExpressionView_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ExpressionView_1.18.0.tgz vignettes: vignettes/ExpressionView/inst/doc/ExpressionView.format.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.pdf vignetteTitles: ExpressionView file format, How the ordering algorithm works, ExpressionView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionView/inst/doc/ExpressionView.format.R, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.R, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.R Package: fabia Version: 2.12.0 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 2b1372e6a6e3020acee3becd71681e3a NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/fabia/fabia.html source.ver: src/contrib/fabia_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/fabia_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/fabia_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/fabia_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/fabia_2.12.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia Package: facopy Version: 1.0.0 Depends: R (>= 3.0), methods, cgdsr (>= 1.1.30), coin (>= 1.0), ggplot2, gridExtra, facopy.annot Imports: annotate, data.table, DOSE, FactoMineR, GO.db, GOstats, graphite, igraph, IRanges, MASS, nnet, reshape2, Rgraphviz, scales License: CC BY-NC 4.0 MD5sum: a679bb3f3afb51e19e4f7e09a1d31234 NeedsCompilation: no Title: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer Description: facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported. biocViews: Software, CopyNumberVariation, GeneSetEnrichment, GenomicVariation, Genetics, Microarray, Sequencing, Visualization Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena source.ver: src/contrib/facopy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/facopy_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/facopy_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/facopy_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/facopy_1.0.0.tgz vignettes: vignettes/facopy/inst/doc/facopy.pdf vignetteTitles: facopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/facopy/inst/doc/facopy.R Package: factDesign Version: 1.42.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 68b4662f22f1520e28fe90f0f425f866 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/factDesign_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/factDesign_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/factDesign_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/factDesign_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/factDesign_1.42.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R Package: farms Version: 1.18.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: b2e669d742c3841f428f7b86672da4d5 NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html source.ver: src/contrib/farms_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/farms_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/farms_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/farms_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/farms_1.18.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R Package: fastLiquidAssociation Version: 1.2.2 Depends: methods, LiquidAssociation, parallel, stats, Hmisc Imports: WGCNA Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 71c8804788c69fe37d28c8f991d63b53 NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson source.ver: src/contrib/fastLiquidAssociation_1.2.2.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/fastLiquidAssociation_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/fastLiquidAssociation_1.2.2.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: fastseg Version: 1.12.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: graphics, stats, IRanges, BiocGenerics Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: cdbda611ae1842f2e7ecd581cf9062e5 NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html source.ver: src/contrib/fastseg_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/fastseg_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/fastseg_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/fastseg_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/fastseg_1.12.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R Package: fdrame Version: 1.38.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 403909c914bf2a15ad0605afdb5b2c96 NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg source.ver: src/contrib/fdrame_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/fdrame_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/fdrame_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/fdrame_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/fdrame_1.38.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fdrame/inst/doc/fdrame.R Package: FEM Version: 1.0.0 Depends: R (>= 2.10), Matrix, igraph, marray, corrplot, impute, limma, org.Hs.eg.db, graph, BiocGenerics License: GPL (>=2) MD5sum: e9cd97283f1c8f57bc7d86ace0512322 NeedsCompilation: no Title: Identification of FunctionalEpigenetic Modules Description: FEM can dentify interactome hotspots of differential promoter methylation and differential ex-pression, where an inverse association between promoter methylation and gene expression is assumed. biocViews: SystemsBiology,DNAMethylation,NetworkEnrichment,GeneRegulation,DifferentialMethylation,DifferentialExpression,Network Author: Andrew E. Teschendorff and Yinming Jiao Maintainer: Andrew E. Teschendorff , Yinming Jiao <20907099@mail.zju.edu.cn> source.ver: src/contrib/FEM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/FEM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/FEM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/FEM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/FEM_1.0.0.tgz vignettes: vignettes/FEM/inst/doc/IntroDoFEM.pdf vignetteTitles: A R package to identify interactome hotspots of differential promoter methylation and differential expression,, where an inverse association between promoter methylation and gene expression is assumed1. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEM/inst/doc/IntroDoFEM.R Package: ffpe Version: 1.10.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: a0192f3f590341cc659937c8244beb28 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron source.ver: src/contrib/ffpe_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ffpe_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ffpe_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ffpe_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ffpe_1.10.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R Package: FGNet Version: 3.0.7 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png Suggests: RGtk2, RCurl, RDAVIDWebService, gage, topGO, KEGGprofile, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi License: GPL (>= 2) MD5sum: 579948b63532b58586f272c412f54cdb NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr source.ver: src/contrib/FGNet_3.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/FGNet_3.0.7.zip win64.binary.ver: bin/windows64/contrib/3.1/FGNet_3.0.7.zip mac.binary.ver: bin/macosx/contrib/3.1/FGNet_3.0.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/FGNet_3.0.7.tgz vignettes: vignettes/FGNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet-vignette.R, vignettes/FGNet/inst/doc/FGNet.R htmlDocs: vignettes/FGNet/inst/doc/FGNet.html htmlTitles: "FGNet" Package: flagme Version: 1.22.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: 36fa63e8630f1d579f4fea45211baa05 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson Maintainer: Mark Robinson , Riccardo Romoli source.ver: src/contrib/flagme_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flagme_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flagme_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flagme_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flagme_1.22.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GCMS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R Package: flipflop Version: 1.4.1 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges License: GPL-3 Archs: i386, x64 MD5sum: 963c7ae014dd0f231efcca5d3de02cf0 NeedsCompilation: yes Title: Fast lasso-based isoform prediction as a flow problem Description: Flipflop discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data. biocViews: RNASeq Author: Elsa Bernard, Laurent Jacob, Julien Mairal and Jean-Philippe Vert Maintainer: Elsa Bernard source.ver: src/contrib/flipflop_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/flipflop_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/flipflop_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/flipflop_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flipflop_1.4.1.tgz vignettes: vignettes/flipflop/inst/doc/flipflop.pdf vignetteTitles: FlipFlop: Fast Lasso-based Isoform Prediction as a Flow Problem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flipflop/inst/doc/flipflop.R Package: flowBeads Version: 1.4.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 58c8fc71f1fbba92421b75dafd7d35fb NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos source.ver: src/contrib/flowBeads_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowBeads_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowBeads_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowBeads_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowBeads_1.4.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R Package: flowBin Version: 1.2.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 7ebe490345de2f3b8a7241360cb4c3a6 NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill source.ver: src/contrib/flowBin_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowBin_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowBin_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowBin_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowBin_1.2.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R Package: flowcatchR Version: 1.0.3 Depends: R (>= 2.10), methods, EBImage Imports: rgl, colorRamps, abind, BiocParallel Suggests: BiocStyle, knitr, shiny License: BSD_3_clause + file LICENSE MD5sum: e286c98c489beb2000f6fcc842f725b3 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells. Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). The main novel contribution investigates the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR SystemRequirements: ImageMagick VignetteBuilder: knitr source.ver: src/contrib/flowcatchR_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowcatchR_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/flowcatchR_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/flowcatchR_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowcatchR_1.0.3.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchR-vignette.pdf vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchR-vignette.R Package: flowCHIC Version: 1.0.2 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 126fb2a747d29e7088168970398e5b10 NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 source.ver: src/contrib/flowCHIC_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowCHIC_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/flowCHIC_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/flowCHIC_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowCHIC_1.0.2.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R Package: flowCL Version: 1.4.0 Depends: R (>= 3.0.2), Rgraphviz, SPARQL Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 83a1ab6236a13984decd5158cd6a74bf NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas source.ver: src/contrib/flowCL_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowCL_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowCL_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowCL_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowCL_1.4.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCL/inst/doc/flowCL.R Package: flowClean Version: 1.2.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 245c2d17d6f319201e22755b314ced86 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant source.ver: src/contrib/flowClean_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowClean_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowClean_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowClean_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowClean_1.2.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R Package: flowClust Version: 3.4.11 Depends: R(>= 2.5.0),methods, Biobase, graph, RBGL,ellipse, flowViz, mnormt, corpcor, flowCore, clue Imports: BiocGenerics, MCMCpack Suggests: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: b0e3539a39a7a953324e048e988aa90e NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make source.ver: src/contrib/flowClust_3.4.11.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowClust_3.4.11.zip win64.binary.ver: bin/windows64/contrib/3.1/flowClust_3.4.11.zip mac.binary.ver: bin/macosx/contrib/3.1/flowClust_3.4.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowClust_3.4.11.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.pdf vignetteTitles: flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowPhyto, flowTrans, flowType suggestsMe: BiocGenerics Package: flowCore Version: 1.32.2 Depends: R (>= 2.10.0) Imports: Biobase, BiocGenerics (>= 0.1.14), graph, graphics, methods, rrcov, stats, utils, stats4, corpcor Suggests: Rgraphviz, flowViz, ncdf, flowStats, testthat, flowWorkspace,openCyto License: Artistic-2.0 Archs: i386, x64 MD5sum: ab76f97f32bd8664b29ee9861c957667 NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan, J. Spidlen Maintainer: M.Jiang source.ver: src/contrib/flowCore_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowCore_1.32.2.zip win64.binary.ver: bin/windows64/contrib/3.1/flowCore_1.32.2.zip mac.binary.ver: bin/macosx/contrib/3.1/flowCore_1.32.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowCore_1.32.2.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowClust, flowFP, flowMatch, flowStats, flowTrans, flowUtils, flowViz, ncdfFlow, plateCore importsMe: flowBeads, flowCHIC, flowDensity, flowFit, flowFlowJo, flowFP, flowMeans, flowPhyto, flowQ, flowStats, flowTrans, flowType, flowViz, plateCore, spade suggestsMe: flowQB, RchyOptimyx Package: flowCyBar Version: 1.2.2 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 4786a9e4698334426f04bb2ca8194668 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 source.ver: src/contrib/flowCyBar_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowCyBar_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.1/flowCyBar_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.1/flowCyBar_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowCyBar_1.2.2.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R Package: flowDensity Version: 1.0.0 Depends: R (>= 2.10.0), methods Imports: flowCore, graphics, car, gplots, RFOC, GEOmap, methods, grDevices License: Artistic-2.0 MD5sum: ea658e4ca65105037844c40ea716f463 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, StemCells, DensityGating Author: M. Jafar Taghiyar, Mehrnoush Malek Maintainer: Mehrnoush Malek source.ver: src/contrib/flowDensity_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowDensity_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowDensity_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowDensity_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowDensity_1.0.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensityVignette.pdf vignetteTitles: Automated alternative to the current manual gating practice hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensityVignette.R Package: flowFit Version: 1.4.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, methods, kza, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: 028cba163c9952e2955ae504aa386dfd NeedsCompilation: no Title: Estimate proliferation in cell-tracking dye studies Description: This package estimate the proliferation of a cell population in cell-tracking dye studies. The package uses an R implementation of the Levenberg-Marquardt algorithm (minpack.lm) to fit a set of peaks (corresponding to different generations of cells) over the proliferation-tracking dye distribution in a FACS experiment. biocViews: FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi source.ver: src/contrib/flowFit_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowFit_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowFit_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowFit_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowFit_1.4.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFit/inst/doc/HowTo-flowFit.R Package: flowFlowJo Version: 1.24.0 Depends: R (>= 2.5.0), MASS, Imports: flowCore, XML (>= 1.96), methods, Biobase License: GPL (>=3) MD5sum: 98881a669bf98b4f0fe66a9c46487dfd NeedsCompilation: no Title: Tools for extracting information from a FlowJo workspace and working with the data in the flowCore paradigm. Description: FlowJo is a commercial GUI based software package from TreeStar Inc. for the visualization and analysis of flow cytometry data. One of the FlowJo standard export file types is the "FlowJo Workspace". This is an XML document that describes files and manipulations that have been performed in the FlowJo GUI environment. This package can take apart the FlowJo workspace and deliver the data into R in the flowCore paradigm. biocViews: FlowCytometry Author: John J. Gosink Maintainer: John J. Gosink source.ver: src/contrib/flowFlowJo_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowFlowJo_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowFlowJo_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowFlowJo_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowFlowJo_1.24.0.tgz vignettes: vignettes/flowFlowJo/inst/doc/flowFlowJo.pdf vignetteTitles: Basic Functions for working with FlowJo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFlowJo/inst/doc/flowFlowJo.R Package: flowFP Version: 1.24.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), flowCore, flowViz, graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: b5ac3dd0279ab6f1e5b030f98958d4c8 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst source.ver: src/contrib/flowFP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowFP_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowFP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowFP_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowFP_1.24.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin Package: flowMap Version: 1.4.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), ggplot2(>= 0.9.3.1), scales(>= 0.2.3), methods (>= 2.14), License: GPL (>=2) MD5sum: 5efe4755733195668fab5900a57adc8b NeedsCompilation: no Title: A probabilistic algorithm for matching and comparing multiple flow cytometry samples Description: This package provides an algorithm to compare and match cell populations across multiple flow cytometry samples. The method is based on the Friedman-Rafsky test, a nonparametric multivariate statistical test, where two cell distributions match if they occupy a similar feature space. The algorithm allows the users to specify a reference sample for comparison or to construct a reference sample from the available data. The output of the algorithm is a set of text files where the cell population labels are replaced by a metaset of population labels, generated from the matching process. biocViews: MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao and Yu Qian Maintainer: Chiaowen Joyce Hsiao source.ver: src/contrib/flowMap_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowMap_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowMap_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowMap_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowMap_1.4.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Multiple sample comparison in flow cytometry data with flowMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R Package: flowMatch Version: 1.2.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 Archs: i386, x64 MD5sum: 282dd87207982a4f568d430008274108 NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad source.ver: src/contrib/flowMatch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowMatch_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowMatch_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowMatch_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowMatch_1.2.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R Package: flowMeans Version: 1.18.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: ea6cfbcc262a3efbab58ae7ebddb928f NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour source.ver: src/contrib/flowMeans_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowMeans_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowMeans_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowMeans_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowMeans_1.18.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: flowPhyto, flowType Package: flowMerge Version: 2.14.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 66bceb94604bb8a6e77ede99d288b509 NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowMerge_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowMerge_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowMerge_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowMerge_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowMerge_2.14.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowMerge.pdf vignetteTitles: flowMerge package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowMerge.R importsMe: flowType Package: flowPeaks Version: 1.8.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 5d4b6c47bc16567110c346ec13088726 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge source.ver: src/contrib/flowPeaks_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowPeaks_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowPeaks_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowPeaks_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowPeaks_1.8.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R Package: flowPhyto Version: 1.18.0 Depends: R (>= 1.8.0) Imports: flowClust, flowCore, flowMeans, TTR, caroline(>= 0.6.6) Suggests: RPostgreSQL, zoo, maps, mapdata, plotrix License: Artistic-2.0 MD5sum: bc9fb794dee963e098d663866c4c57e7 NeedsCompilation: no Title: Methods for Continuous Flow Cytometry Description: Automated Analysis of Continuous Flow Cytometry Data. biocViews: FlowCytometry, DataImport, QualityControl, Classification, Visualization, Clustering Author: Francois Ribalet Maintainer: Chris Berthiaume URL: http://seaflow.ocean.washington.edu source.ver: src/contrib/flowPhyto_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowPhyto_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowPhyto_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowPhyto_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowPhyto_1.18.0.tgz vignettes: vignettes/flowPhyto/inst/doc/flowPhyto.pdf vignetteTitles: flowPhyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPhyto/inst/doc/flowPhyto.R Package: flowPlots Version: 1.14.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: b99cc6f6f268eb52dc283a419495907a NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins source.ver: src/contrib/flowPlots_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowPlots_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowPlots_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowPlots_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowPlots_1.14.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R Package: flowQ Version: 1.26.0 Depends: R (>= 2.10.0), methods, BiocGenerics, outliers, lattice, flowViz, mvoutlier, bioDist, parody, RColorBrewer, latticeExtra Imports: methods, BiocGenerics, geneplotter, flowCore, flowViz, IRanges Suggests: flowStats License: Artistic-2.0 MD5sum: 0775765a0ec2558a5105b62578d869c6 NeedsCompilation: no Title: Quality control for flow cytometry Description: Provides quality control and quality assessment tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: R. Gentleman, F. Hahne, J. Kettman, N. Le Meur, N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: ImageMagick source.ver: src/contrib/flowQ_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/flowQ_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowQ_1.26.0.tgz vignettes: vignettes/flowQ/inst/doc/DataQualityAssessment.pdf, vignettes/flowQ/inst/doc/Extending-flowQ.pdf vignetteTitles: Data Quality Assesment for Ungated Flow Cytometry Data, Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQ/inst/doc/DataQualityAssessment.R, vignettes/flowQ/inst/doc/Extending-flowQ.R Package: flowQB Version: 1.10.0 Imports: Biobase, graphics,methods, flowCore,stats,MASS Suggests: MASS, flowCore License: Artistic-2.0 MD5sum: 524bb09231a5a95635d2c8f28f38adf0 NeedsCompilation: no Title: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CVinstrinsic calculations. Description: flowQB is a fully automated R Bioconductor package to calculate automatically the detector efficiency (Q), optical background (B) and intrinsic CV of the beads. biocViews: FlowCytometry Author: Faysal El Khettabi Maintainer: Faysal El Khettabi source.ver: src/contrib/flowQB_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowQB_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowQB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowQB_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowQB_1.10.0.tgz vignettes: vignettes/flowQB/inst/doc/flowQBVignettes.pdf vignetteTitles: flowQB package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQB/inst/doc/AdvancedflowQBNIH2.R, vignettes/flowQB/inst/doc/AdvancedflowQBNIH3.R, vignettes/flowQB/inst/doc/flowQBVignettes.R, vignettes/flowQB/inst/doc/IntroductoryflowQBNIH.R Package: flowStats Version: 3.24.8 Depends: R (>= 2.10), flowCore, fda (>= 2.2.6), mvoutlier, cluster, flowWorkspace Imports: BiocGenerics, MASS, flowViz, flowCore, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, utils, KernSmooth, lattice,ks Suggests: xtable Enhances: RBGL,ncdfFlow,graph License: Artistic-2.0 MD5sum: 553497d02e51597b4d5cb580cc744865 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak and Mike Jiang source.ver: src/contrib/flowStats_3.24.8.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowStats_3.24.8.zip win64.binary.ver: bin/windows64/contrib/3.1/flowStats_3.24.8.zip mac.binary.ver: bin/macosx/contrib/3.1/flowStats_3.24.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowStats_3.24.8.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R importsMe: plateCore suggestsMe: flowCore, flowQ Package: flowTrans Version: 1.18.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 18725eae1941c2ddef4f150588f6fc32 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowTrans_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowTrans_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowTrans_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowTrans_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowTrans_1.18.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R Package: flowType Version: 2.4.0 Depends: R (>= 2.10), Rcpp (>= 0.10.4), BH (>= 1.51.0-3) Imports: Biobase, graphics, grDevices, methods, flowCore, flowMeans, sfsmisc, rrcov, flowClust, flowMerge, stats LinkingTo: Rcpp, BH Suggests: xtable License: Artistic-2.0 Archs: i386, x64 MD5sum: 423449c5a4c9b42d684634dea9e8ed80 NeedsCompilation: yes Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour source.ver: src/contrib/flowType_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowType_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowType_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowType_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowType_2.4.0.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowType/inst/doc/flowType.R importsMe: RchyOptimyx Package: flowUtils Version: 1.30.0 Depends: R (>= 2.2.0), flowCore (>= 1.31.16) Imports: Biobase, graph, methods, stats, utils, flowViz, corpcor, RUnit, XML Suggests: gatingMLData License: Artistic-2.0 MD5sum: fb175453587b68e01c1b22e2a3ae5af0 NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, J. Spidlen. Maintainer: Josef Spidlen source.ver: src/contrib/flowUtils_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowUtils_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/flowUtils_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/flowUtils_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowUtils_1.30.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R Package: flowViz Version: 1.30.1 Depends: R (>= 2.7.0), flowCore, lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats,knitr License: Artistic-2.0 MD5sum: a99cd937200a1a32f9b09bf30420d93b NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/flowViz_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowViz_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.1/flowViz_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.1/flowViz_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowViz_1.30.1.tgz vignettes: vignettes/flowViz/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R htmlDocs: vignettes/flowViz/inst/doc/filters.html htmlTitles: "Visualizing Gates with Flow Cytometry Data" dependsOnMe: flowClust, flowFP, flowQ, ncdfFlow, plateCore importsMe: flowFit, flowFP, flowQ, flowStats, flowTrans, flowUtils suggestsMe: flowBeads, flowClean, flowCore, spade Package: flowWorkspace Version: 3.12.06 Depends: R (>= 2.16.0),flowCore(>= 1.31.17),flowViz(>= 1.29.27),ncdfFlow(>= 2.11.34),gridExtra Imports: Biobase, BiocGenerics, graph, graphics, lattice, methods, stats, stats4, utils, RBGL, graph, XML, Biobase, IDPmisc, Cairo, tools,gridExtra,Rgraphviz ,data.table ,plyr ,latticeExtra ,Rcpp ,RColorBrewer LinkingTo: Rcpp Suggests: testthat ,flowWorkspaceData ,RSVGTipsDevice ,knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 4947b6966f452800bbf9310512572e6d NeedsCompilation: yes Title: Import flowJo Workspaces into BioConductor and replicate flowJo gating with flowCore Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/flowWorkspace_3.12.06.tar.gz win.binary.ver: bin/windows/contrib/3.1/flowWorkspace_3.12.06.zip win64.binary.ver: bin/windows64/contrib/3.1/flowWorkspace_3.12.06.zip mac.binary.ver: bin/macosx/contrib/3.1/flowWorkspace_3.12.06.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/flowWorkspace_3.12.06.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace.pdf vignetteTitles: Importing flowJo Workspaces into R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R htmlDocs: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html htmlTitles: "How to merge GatingSets", "How to plot gated data" dependsOnMe: flowStats, openCyto suggestsMe: COMPASS Package: fmcsR Version: 1.8.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods,ChemmineR, BiocGenerics,parallel Suggests: BiocStyle,knitr,knitcitations,knitrBootstrap License: Artistic-2.0 Archs: i386, x64 MD5sum: 1a5b16ef4788629b6d0b5003f3103aca NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: ChemmineR Team URL: http://manuals.bioinformatics.ucr.edu/home/chemminer VignetteBuilder: knitr source.ver: src/contrib/fmcsR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/fmcsR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/fmcsR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/fmcsR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/fmcsR_1.8.0.tgz vignettes: vignettes/fmcsR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R htmlDocs: vignettes/fmcsR/inst/doc/fmcsR.html htmlTitles: "fmcsR" importsMe: Rcpi Package: focalCall Version: 1.0.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: f16145d2fbbcdb64538b4d536e37b9b2 NeedsCompilation: no Title: Detection of focal aberrations in DNA copy number data Description: Detection of genomic focal aberrations in high-resolution DNA copy number data biocViews: Microarray,Preprocessing,Visualization,Sequencing Author: Oscar Krijgsman Maintainer: Oscar Krijgsman source.ver: src/contrib/focalCall_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/focalCall_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/focalCall_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/focalCall_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/focalCall_1.0.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/focalCall/inst/doc/focalCall.R Package: FourCSeq Version: 1.0.0 Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2, splines, methods Imports: DESeq2, Biobase, Biostrings, GenomicRanges, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: 2688756f94443c807c24fb9e0dea621b NeedsCompilation: no Title: Package analyse 4C sequencing data Description: FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated. biocViews: Software, Preprocessing, Sequencing Author: Felix A. Klein, EMBL Heidelberg Maintainer: Felix A. Klein VignetteBuilder: knitr source.ver: src/contrib/FourCSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/FourCSeq_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/FourCSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/FourCSeq_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/FourCSeq_1.0.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FourCSeq/inst/doc/FourCSeq.R Package: FRGEpistasis Version: 1.2.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: b309be76223b12b54993f920f3043b0b NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang source.ver: src/contrib/FRGEpistasis_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/FRGEpistasis_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/FRGEpistasis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/FRGEpistasis_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/FRGEpistasis_1.2.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R Package: frma Version: 1.18.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: d09be8e3df68cea3c8cd8edbaed7f768 NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org source.ver: src/contrib/frma_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/frma_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/frma_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/frma_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/frma_1.18.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress suggestsMe: frmaTools Package: frmaTools Version: 1.18.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: d2adbc4bcdb14ff6cd7f21d4675e538d NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org source.ver: src/contrib/frmaTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/frmaTools_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/frmaTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/frmaTools_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/frmaTools_1.18.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R Package: FunciSNP Version: 1.8.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: AnnotationDbi, IRanges, Rsamtools (>= 1.6.1), rtracklayer(>= 1.14.1), methods, ChIPpeakAnno (>= 2.2.0), GenomicRanges, VariantAnnotation, plyr, org.Hs.eg.db, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Enhances: parallel License: GPL-3 MD5sum: 8d8f01499aaee06a7852acce09c1aea3 NeedsCompilation: no Title: Integrating Functional Non-coding Datasets with Genetic Association Studies to Identify Candidate Regulatory SNPs Description: FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions. biocViews: Infrastructure, DataRepresentation, DataImport, SequenceMatching, Annotation Author: Simon G. Coetzee and Houtan Noushmehr, PhD Maintainer: Simon G. Coetzee URL: http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/ source.ver: src/contrib/FunciSNP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/FunciSNP_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/FunciSNP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/FunciSNP_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/FunciSNP_1.8.0.tgz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.R Package: gaga Version: 2.12.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: cd590de9bdc9bcb9ab5c880df32e0bbf NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell source.ver: src/contrib/gaga_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gaga_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gaga_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gaga_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gaga_2.12.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper Package: gage Version: 2.16.0 Depends: R (>= 2.10) Imports: graph, KEGGREST, AnnotationDbi Suggests: pathview, gageData, GO.db, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 53006475df014e02b8ce58cf39dc3c36 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: http://www.biomedcentral.com/1471-2105/10/161 source.ver: src/contrib/gage_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gage_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gage_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gage_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gage_2.16.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R suggestsMe: FGNet, pathview Package: gaggle Version: 1.34.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 378c0aebed964842077ac90dc8ecff9f NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ source.ver: src/contrib/gaggle_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gaggle_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gaggle_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gaggle_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gaggle_1.34.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R Package: gaia Version: 2.10.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: 79670f94eacdffaa54980a4738ef3cb0 NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella source.ver: src/contrib/gaia_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gaia_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gaia_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gaia_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gaia_2.10.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R Package: gaucho Version: 1.2.0 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: c666b10b60e3893ba4080471939cb4c3 NeedsCompilation: no Title: Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering Description: Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples biocViews: Software,Genetics,SNP,Sequencing,SomaticMutation Author: Alex Murison [aut, cre], Christopher Wardell [aut, cre] Maintainer: Alex Murison , Christopher Wardell VignetteBuilder: knitr source.ver: src/contrib/gaucho_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gaucho_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gaucho_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gaucho_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gaucho_1.2.0.tgz vignettes: vignettes/gaucho/inst/doc/gaucho_vignette.pdf vignetteTitles: An introduction to gaucho hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaucho/inst/doc/gaucho_vignette.R Package: gCMAP Version: 1.10.2 Depends: GSEABase, limma (>= 3.15.14) Imports: Biobase, BiocGenerics, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi Suggests: DESeq, KEGG.db, reactome.db, RUnit, GO.db, mgsa Enhances: bigmemory, bigmemoryExtras (>= 1.1.2) License: Artistic-2.0 MD5sum: b6dd797836a0f7617b9c73b804cb98a9 NeedsCompilation: no Title: Tools for Connectivity Map-like analyses Description: The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots. biocViews: Microarray, Software, Pathways, Annotation Author: Thomas Sandmann , Richard Bourgon and Sarah Kummerfeld Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAP_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/gCMAP_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.1/gCMAP_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.1/gCMAP_1.10.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gCMAP_1.10.2.tgz vignettes: vignettes/gCMAP/inst/doc/diffExprAnalysis.pdf, vignettes/gCMAP/inst/doc/gCMAP.pdf vignetteTitles: Creating reference datasets, gCMAP classes and methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAP/inst/doc/diffExprAnalysis.R, vignettes/gCMAP/inst/doc/gCMAP.R dependsOnMe: gCMAPWeb Package: gCMAPWeb Version: 1.6.0 Depends: brew, gCMAP (>= 1.3.0), R (>= 2.15.0), yaml, Rook Imports: Biobase, annotate, AnnotationDbi, BiocGenerics, brew, graphics, grDevices, GSEABase, hwriter, IRanges, methods, parallel, stats, utils Suggests: org.Hs.eg.db, org.Mm.eg.db, ArrayExpress, affy, hgfocus.db, ArrayExpress, hgu133a.db, mgug4104a.db, RUnit Enhances: bigmemory, bigmemoryExtras License: Artistic-2.0 MD5sum: 1d4e63e90012bbdca94394c426cd367f NeedsCompilation: no Title: A web interface for gene-set enrichment analyses Description: The gCMAPWeb R package provides a graphical user interface for the gCMAP package. gCMAPWeb uses the Rook package and can be used either on a local machine, leveraging R's internal web server, or run on a dedicated rApache web server installation. gCMAPWeb allows users to search their own data sources and instructions to generate reference datasets from public repositories are included with the package. The package supports three common types of analyses, specifically queries with 1. one or two sets of query gene identifiers, whose members are expected to show changes in gene expression in a consistent direction. For example, an up-regulated gene set might contain genes activated by a transcription factor, a down-regulated geneset targets repressed by the same factor. 2. a single set of query gene identifiers, whose members are expected to show divergent differential expression (non-directional query). For example, members of a particular signaling pathway, some of which may be up- some down-regulated in response to a stimulus. 3. a query with the complete results of a differential expression profiling experiment. For example, gene identifiers and z-scores from a previous perturbation experiment. gCMAPWeb accepts three types of identifiers: EntreIds, gene Symbols and microarray probe ids and can be configured to work with any species supported by Bioconductor. For each query submission, significantly similar reference datasets will be identified and reported in graphical and tabular form. biocViews: GUI, GeneSetEnrichment, Visualization Author: Thomas Sandmann Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAPWeb_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gCMAPWeb_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gCMAPWeb_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gCMAPWeb_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gCMAPWeb_1.6.0.tgz vignettes: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.pdf, vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf vignetteTitles: gCMAPWeb configuration, Recreating the Broad Connectivity Map v1 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.R, vignettes/gCMAPWeb/inst/doc/referenceDatasets.R Package: gcrma Version: 2.38.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocInstaller Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL Archs: i386, x64 MD5sum: edcb1241b8a538a0ec4aa97128136e14 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu source.ver: src/contrib/gcrma_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gcrma_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gcrma_2.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gcrma_2.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gcrma_2.38.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcrma/inst/doc/gcrma2.0.R dependsOnMe: affyILM, affyPLM, bgx, maskBAD, simpleaffy, webbioc importsMe: affycoretools, affylmGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp Package: gdsfmt Version: 1.2.2 Depends: R (>= 2.14.0) Imports: methods Suggests: parallel, RUnit, BiocStyle, BiocGenerics, knitr License: LGPL-3 Archs: i386, x64 MD5sum: ec2aad4606fe5e5fcbb8b9e6a2a3211e NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) files Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers with less than 8 bits, since a single genetic/genomic variant, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are also supported with relatively efficient random access. It is allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Software, Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources) Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/gdsfmt_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/gdsfmt_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.1/gdsfmt_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.1/gdsfmt_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gdsfmt_1.2.2.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.pdf vignetteTitles: gdsfmt vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.R dependsOnMe: GWASTools, SeqArray, SNPRelate Package: geecc Version: 1.0.0 Depends: R (>= 3.0.0), methods Imports: MASS, hypergea (>= 1.2.3), gplots Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) MD5sum: f8f43dc8d03ddbac8a8c37c2ec5522aa NeedsCompilation: no Title: Gene set Enrichment analysis Extended to Contingency Cubes Description: Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosmal position, phylostrata, .... biocViews: BiologicalQuestion, GeneSetEnrichment, WorkflowStep, GO, StatisticalMethod, GeneExpression, Transcription, RNASeq, Microarray Author: Markus Boenn Maintainer: Markus Boenn source.ver: src/contrib/geecc_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/geecc_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/geecc_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/geecc_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/geecc_1.0.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geecc/inst/doc/geecc.R Package: genArise Version: 1.42.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 78856604cd3ba9c1e9fa5317a407ea4c NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise source.ver: src/contrib/genArise_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/genArise_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/genArise_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/genArise_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genArise_1.42.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R Package: GENE.E Version: 1.6.0 Depends: R (>= 2.7.0), rhdf5 (>= 2.8.0), RCurl (>= 1.6-6) Imports: rhdf5, RCurl Suggests: RUnit, BiocGenerics, knitr, golubEsets (>= 1.0) License: GPL-2 MD5sum: 532dd99e1b81fefd2820e8c6dbc72d2b NeedsCompilation: no Title: Interact with GENE-E from R Description: Interactive exploration of matrices in GENE-E. biocViews: ThirdPartyClient Author: Joshua Gould Maintainer: Joshua Gould URL: http://www.broadinstitute.org/cancer/software/GENE-E SystemRequirements: GENE-E software. VignetteBuilder: knitr source.ver: src/contrib/GENE.E_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GENE.E_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GENE.E_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GENE.E_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GENE.E_1.6.0.tgz vignettes: vignettes/GENE.E/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENE.E/inst/doc/GENE.E-vignette.R htmlDocs: vignettes/GENE.E/inst/doc/GENE.E-vignette.html htmlTitles: "GENE.E Overview" Package: GeneAnswers Version: 2.8.0 Depends: R (>= 3.0.0), igraph, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: 75e85c629068f9141006438b75fb61a8 NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng source.ver: src/contrib/GeneAnswers_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneAnswers_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneAnswers_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneAnswers_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneAnswers_2.8.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/geneAnswers.R, vignettes/GeneAnswers/inst/doc/getListGIF.R Package: GeneExpressionSignature Version: 1.12.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 0bdb3f6faff6cdb1c91d4af07dcd8ef8 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao Maintainer: Yang Cao , Fei Li ,Lu Han source.ver: src/contrib/GeneExpressionSignature_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneExpressionSignature_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneExpressionSignature_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneExpressionSignature_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneExpressionSignature_1.12.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: genefilter Version: 1.48.1 Imports: AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: d9b538018e0b002cbf5d98e0fdaf88af NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes biocViews: Microarray Author: R. Gentleman, V. Carey, W. Huber, F. Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/genefilter_1.48.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/genefilter_1.48.1.zip win64.binary.ver: bin/windows64/contrib/3.1/genefilter_1.48.1.zip mac.binary.ver: bin/macosx/contrib/3.1/genefilter_1.48.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genefilter_1.48.1.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/independent_filtering.pdf vignetteTitles: Using the genefilter function to filter genes from a microarray dataset, How to find genes whose expression profile is similar to that of specified genes, Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010), Diagnostics for independent filtering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R, vignettes/genefilter/inst/doc/independent_filtering.R dependsOnMe: a4Base, cellHTS, cellHTS2, charm, CNTools, GeneMeta, simpleaffy, sva importsMe: affycoretools, affyQCReport, annmap, arrayQualityMetrics, Category, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GSRI, methyAnalysis, methylumi, minfi, MLInterfaces, PECA, phenoTest, Ringo, simpleaffy, tilingArray, XDE suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, Category, categoryCompare, clusterStab, codelink, compcodeR, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MCRestimate, npGSEA, oligo, oneChannelGUI, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO, XDE Package: genefu Version: 1.16.0 Depends: survcomp, mclust, biomaRt, R (>= 2.10) Imports: amap Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable License: Artistic-2.0 MD5sum: 27cc006b0b6f33c220f9eefe5f6ba593 NeedsCompilation: no Title: Relevant Functions for Gene Expression Analysis, Especially in Breast Cancer. Description: Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, survival analysis, ... biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Benjamin Haibe-Kains, Markus Schroeder, Gianluca Bontempi, Christos Sotiriou, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/genefu_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/genefu_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/genefu_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/genefu_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genefu_1.16.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R Package: GeneGA Version: 1.16.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: ea349a60520904fc0523fe0c144d1e65 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ source.ver: src/contrib/GeneGA_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/GeneGA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneGA_1.16.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R Package: GeneMeta Version: 1.38.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 6f61c482ed22f865db0e4eb0d4afbc7a NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, Metagenomics, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GeneMeta_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneMeta_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneMeta_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneMeta_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneMeta_1.38.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R suggestsMe: genefu, XDE Package: GeneNetworkBuilder Version: 1.8.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13), graph Imports: plyr, graph LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, Rgraphviz, XML, RCytoscape, RBGL License: GPL (>= 2) Archs: i386, x64 MD5sum: 1533b2e457d3914ce4fba339cd703ec4 NeedsCompilation: yes Title: Build Regulatory Network from ChIP-chip/ChIP-seq and Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou and Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/GeneNetworkBuilder_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneNetworkBuilder_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneNetworkBuilder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneNetworkBuilder_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneNetworkBuilder_1.8.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.pdf vignetteTitles: GeneNetworkBuilder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.R Package: GeneOverlap Version: 1.2.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: e4ae23b6ed3666a23546a07df20bfac4 NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Mount Sinai Maintainer: Li Shen, Mount Sinai URL: http://shenlab-sinai.github.io/shenlab-sinai/ source.ver: src/contrib/GeneOverlap_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneOverlap_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneOverlap_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneOverlap_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneOverlap_1.2.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R Package: geneplotter Version: 1.44.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 MD5sum: 1a46be23af4178cf461a3bab2653bfea NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/geneplotter_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/geneplotter_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/geneplotter_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/geneplotter_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/geneplotter_1.44.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HMMcopy importsMe: biocGraph, DESeq, DESeq2, DEXSeq, flowQ, IsoGeneGUI, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.38.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: af06fcf46dc5206c28148b2bfc24d0d5 NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather source.ver: src/contrib/geneRecommender_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/geneRecommender_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/geneRecommender_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/geneRecommender_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/geneRecommender_1.38.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R Package: GeneRegionScan Version: 1.22.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 6798f7eaa3fe548bf99a4da9ed4de976 NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen source.ver: src/contrib/GeneRegionScan_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneRegionScan_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneRegionScan_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneRegionScan_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneRegionScan_1.22.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R Package: geneRxCluster Version: 1.2.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: f186cedfe24a405ad6ba6b62b462c898 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry source.ver: src/contrib/geneRxCluster_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/geneRxCluster_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/geneRxCluster_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/geneRxCluster_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/geneRxCluster_1.2.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R Package: GeneSelectMMD Version: 2.10.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 1a6638e3a90d7df47454f810f6e3eb8c NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu source.ver: src/contrib/GeneSelectMMD_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneSelectMMD_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneSelectMMD_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneSelectMMD_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneSelectMMD_2.10.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R Package: GeneSelector Version: 2.16.0 Depends: R (>= 2.5.1), methods, stats, Biobase Imports: multtest, siggenes, samr, limma Suggests: multtest, siggenes, samr, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: a0acad6af44d292ad7279dc0fd13d03f NeedsCompilation: yes Title: Stability and Aggregation of ranked gene lists Description: The term 'GeneSelector' refers to a filter selecting those genes which are consistently identified as differentially expressed using various statistical procedures. 'Selected' genes are those present at the top of the list in various ranking methods (currently 14). In addition, the stability of the findings can be taken into account in the final ranking by examining perturbed versions of the original data set, e.g. by leaving samples, swapping class labels, generating bootstrap replicates or adding noise. Given multiple ranked lists, one can use aggregation methods in order to find a synthesis. biocViews: StatisticalMethod, DifferentialExpression Author: Martin Slawski , Anne-Laure Boulesteix . Maintainer: Martin Slawski source.ver: src/contrib/GeneSelector_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneSelector_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneSelector_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneSelector_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneSelector_2.16.0.tgz vignettes: vignettes/GeneSelector/inst/doc/GeneSelector.pdf vignetteTitles: GeneSelector.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelector/inst/doc/GeneSelector.R Package: geNetClassifier Version: 1.6.3 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 1313d582562cb1b2dc6a6a72dab66fc3 NeedsCompilation: no Title: classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org source.ver: src/contrib/geNetClassifier_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/geNetClassifier_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.1/geNetClassifier_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.1/geNetClassifier_1.6.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/geNetClassifier_1.6.3.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneticsDesign Version: 1.34.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: eb88d4c446619de0b7c0c246e47a703f NeedsCompilation: no Title: Functions for designing genetics studies Description: This package contains functions useful for designing genetics studies, including power and sample-size calculations. biocViews: Genetics Author: Gregory Warnes David Duffy , Michael Man Weiliang Qiu Ross Lazarus Maintainer: The R Genetics Project source.ver: src/contrib/GeneticsDesign_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneticsDesign_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneticsDesign_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneticsDesign_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GeneticsDesign_1.34.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneticsDesign/inst/doc/GPC.R Package: GeneticsPed Version: 1.28.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: 74b8834d1921754f5ef4e80748122ebe NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org source.ver: src/contrib/GeneticsPed_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GeneticsPed_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GeneticsPed_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GeneticsPed_1.28.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R Package: genoCN Version: 1.18.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: 843cb30811df50baf13204e615824719 NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun source.ver: src/contrib/genoCN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/genoCN_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/genoCN_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/genoCN_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genoCN_1.18.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R Package: GenomeGraphs Version: 1.26.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: c387f748807830d046064e1f7a99b79a NeedsCompilation: no Title: Plotting genomic information from Ensembl Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. GenomeGraphs uses the biomaRt package to perform live annotation queries to Ensembl and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. Another strength of GenomeGraphs is to plot different data types such as array CGH, gene expression, sequencing and other data, together in one plot using the same genome coordinate system. biocViews: Visualization, Microarray Author: Steffen Durinck , James Bullard Maintainer: Steffen Durinck source.ver: src/contrib/GenomeGraphs_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomeGraphs_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomeGraphs_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomeGraphs_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomeGraphs_1.26.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.R dependsOnMe: Genominator, waveTiling suggestsMe: rMAT, triplex Package: GenomeInfoDb Version: 1.2.5 Depends: R (>= 3.1), methods, stats4, BiocGenerics, S4Vectors (>= 0.2.0), IRanges (>= 1.99.26) Imports: methods, BiocGenerics, S4Vectors Suggests: GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Dmelanogaster.UCSC.dm3.ensGene, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 0dc628505a3e0d334e723245c8d77aa4 NeedsCompilation: no Title: Utilities for manipulating chromosome and other 'seqname' identifiers Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w source.ver: src/contrib/GenomeInfoDb_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomeInfoDb_1.2.5.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomeInfoDb_1.2.5.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomeInfoDb_1.2.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomeInfoDb_1.2.5.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb :Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: AnnotationDbi, BSgenome, bumphunter, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, Gviz, htSeqTools, methyAnalysis, Rsamtools, TitanCNA, VariantAnnotation importsMe: AllelicImbalance, ballgown, biovizBase, BSgenome, casper, CexoR, ChIPseeker, CNEr, compEpiTools, csaw, derfinder, derfinderPlot, DNaseR, easyRNASeq, epivizr, exomeCopy, genoset, ggbio, GGtools, gwascat, h5vc, methylPipe, methylumi, MinimumDistance, NarrowPeaks, qpgraph, Rariant, regionReport, Repitools, rtracklayer, seqplots, SGSeq, ShortRead, SNPchip, SomaticSignatures, VanillaICE, VariantFiltering, VariantTools suggestsMe: QDNAseq Package: genomeIntervals Version: 1.22.3 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.10.0) License: Artistic-2.0 MD5sum: cde75b7209be5106a24cd909177f2a0c NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur source.ver: src/contrib/genomeIntervals_1.22.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/genomeIntervals_1.22.3.zip win64.binary.ver: bin/windows64/contrib/3.1/genomeIntervals_1.22.3.zip mac.binary.ver: bin/macosx/contrib/3.1/genomeIntervals_1.22.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genomeIntervals_1.22.3.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe importsMe: easyRNASeq Package: genomes Version: 2.12.0 Depends: R (>= 2.11), XML, RCurl, GenomicRanges, IRanges, Biostrings License: Artistic-2.0 MD5sum: d0d0ecc03b4fea8879669f075b60397f NeedsCompilation: no Title: Genome sequencing project metadata Description: Collects genome sequencing project data from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben source.ver: src/contrib/genomes_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/genomes_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/genomes_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/genomes_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genomes_2.12.0.tgz vignettes: vignettes/genomes/inst/doc/genome-tables.pdf vignetteTitles: Introduction to genome projects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genome-tables.R Package: GenomicAlignments Version: 1.2.2 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.2), IRanges (>= 1.99.27), GenomeInfoDb (>= 1.1.20), GenomicRanges (>= 1.17.42), Biostrings (>= 2.33.14), Rsamtools (>= 1.18.3) Imports: methods, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq, edgeR, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 6b89be85820aa18ed3b6689a7e908db4 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, SNP, Coverage, Alignment Author: Herv\'e Pag\`es, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicAlignments_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicAlignments_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicAlignments_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicAlignments_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicAlignments_1.2.2.tgz vignettes: vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: chimera, DiffBind, groHMM, hiReadsProcessor, metagene, prebs, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: biovizBase, ChIPQC, CNEr, csaw, customProDB, derfinder, easyRNASeq, FourCSeq, GenomicFiles, ggbio, gmapR, Gviz, HTSeqGenie, methylPipe, PICS, QuasR, Repitools, roar, rtracklayer, SGSeq, SplicingGraphs, trackViewer suggestsMe: BiocParallel, gage, GenomeInfoDb, GenomicRanges, Rsamtools, Streamer Package: GenomicFeatures Version: 1.18.7 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.1.5), IRanges (>= 1.99.1), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12), AnnotationDbi (>= 1.27.9) Imports: methods, DBI (>= 0.2-5), RSQLite (>= 0.8-1), Biostrings (>= 2.23.3), rtracklayer (>= 1.26.3), biomaRt (>= 2.17.1), RCurl, utils, Biobase (>= 2.15.1) Suggests: org.Mm.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce2, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), Rsamtools, pasillaBamSubset (>= 0.0.5), RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: d6b6af252f1a4e84c4ab854c4eaa510d NeedsCompilation: no Title: Tools for making and manipulating transcript centric annotations Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/GenomicFeatures_1.18.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicFeatures_1.18.7.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicFeatures_1.18.7.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicFeatures_1.18.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicFeatures_1.18.7.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: exomePeak, OrganismDbi, SplicingGraphs importsMe: AllelicImbalance, biovizBase, casper, ChIPpeakAnno, ChIPseeker, compEpiTools, CompGO, csaw, customProDB, derfinder, derfinderPlot, epivizr, ggbio, gmapR, Gviz, HTSeqGenie, lumi, MEDIPS, methyAnalysis, proBAMr, qpgraph, QuasR, SGSeq, SplicingGraphs, trackViewer, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: biomvRCNS, Biostrings, chipseq, cummeRbund, DEXSeq, easyRNASeq, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, MiRaGE, RIPSeeker, Rsamtools, ShortRead, systemPipeR Package: GenomicFiles Version: 1.2.1 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), Rsamtools (>= 1.17.29), GenomicRanges (>= 1.17.16), rtracklayer (>= 1.25.3), BiocParallel (>= 0.7.0) Imports: GenomicAlignments, IRanges, Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14 License: Artistic-2.0 MD5sum: 739894053672801b3a448b3ac595f8a5 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Infrastructure, DataImport, Sequencing Author: Valerie Obenchain, Michael Love, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicFiles_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicFiles_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicFiles_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicFiles_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicFiles_1.2.1.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R importsMe: derfinder Package: GenomicInteractions Version: 1.0.3 Depends: R (>= 2.10) Imports: Rsamtools, GenomicRanges, IRanges, data.table, stringr, rtracklayer, ggplot2, gridExtra, methods, igraph, plotrix Suggests: knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9 License: GPL-3 MD5sum: c2c6a1abdfb9b41b3a3456309d50f40c NeedsCompilation: no Title: R package for handling genomic interaction data Description: R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots / statistics biocViews: Software,Infrastructure,DataImport,DataRepresentation Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic A., Lenhard B. Maintainer: Nathan Harmston VignetteBuilder: knitr source.ver: src/contrib/GenomicInteractions_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicInteractions_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicInteractions_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicInteractions_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicInteractions_1.0.3.tgz vignettes: vignettes/GenomicInteractions/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R htmlDocs: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html htmlTitles: "GenomicInteractions-ChIAPET", "GenomicInteractions-HiC" Package: GenomicRanges Version: 1.18.4 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.3), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.1.20) Imports: utils, stats, XVector LinkingTo: S4Vectors, IRanges Suggests: AnnotationDbi (>= 1.21.1), AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2, Biostrings (>= 2.25.3), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, KEGG.db, KEGGgraph, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Athaliana.BioMart.plantsmart22, org.Sc.sgd.db, VariantAnnotation, edgeR, DESeq, DEXSeq, pasilla, pasillaBamSubset, RUnit, digest, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 200ca3624e5e367f298426e845d43d9f NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyze high-throughput sequencing data (a.k.a. NGS data). The package defines general purpose containers for storing genomic intervals. Specialized containers for representing and manipulating short alignments against a reference genome are defined in the GenomicAlignments package. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: P. Aboyoun, H. Pages and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GenomicRanges_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicRanges_1.18.4.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicRanges_1.18.4.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicRanges_1.18.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicRanges_1.18.4.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf vignetteTitles: Extending GenomicRanges, GenomicRanges HOWTOs, An Introduction to GenomicRanges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R dependsOnMe: AllelicImbalance, annmap, Basic4Cseq, baySeq, biomvRCNS, BiSeq, BSgenome, bsseq, bumphunter, CAFE, casper, chimera, ChIPQC, chipseq, cleanUpdTSeq, cn.mops, compEpiTools, COPDSexualDimorphism, CSAR, csaw, DASiR, deepSNV, DESeq2, DEXSeq, DiffBind, DMRforPairs, ensemblVEP, epigenomix, epivizr, exomeCopy, fastseg, FourCSeq, genomes, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicTuples, genoset, GenoView, gmapR, GOTHiC, groHMM, Gviz, hiAnnotator, HiTC, htSeqTools, IdeoViz, intansv, MBASED, metagene, methyAnalysis, methylPipe, minfi, PING, QuasR, R453Plus1Toolbox, Rariant, Rcade, rfPred, riboSeqR, RIPSeeker, Rsamtools, RSVSim, rtracklayer, segmentSeq, seqbias, SGSeq, SigFuge, SomatiCA, SomaticSignatures, SplicingGraphs, trackViewer, VanillaICE, VariantAnnotation, VariantTools, vtpnet, wavClusteR importsMe: ALDEx2, AnnotationHub, ArrayExpressHTS, ballgown, beadarray, BEAT, biovizBase, BiSeq, BSgenome, CAGEr, CexoR, chipenrich, ChIPseeker, chipseq, ChIPseqR, CNEr, copynumber, customProDB, derfinder, derfinderPlot, DNaseR, DOQTL, easyRNASeq, flipflop, FourCSeq, FunciSNP, GenomicAlignments, GenomicInteractions, GGBase, ggbio, GGtools, gwascat, h5vc, HTSeqGenie, HTSFilter, lumi, M3D, MEDIPS, methyAnalysis, MethylSeekR, methylumi, MinimumDistance, NarrowPeaks, nucleR, oligoClasses, pepStat, PICS, prebs, proBAMr, Pviz, qpgraph, QuasR, regionReport, Repitools, rnaSeqMap, roar, SeqArray, seqplots, SeqVarTools, ShortRead, simulatorZ, SNPchip, SomatiCA, spliceR, SplicingGraphs, TFBSTools, ToPASeq, tracktables, triplex, VariantFiltering, waveTiling suggestsMe: BiocGenerics, BiocParallel, cummeRbund, DMRcate, GenomeInfoDb, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, SeqGSEA, STAN Package: GenomicTuples Version: 1.0.0 Depends: R (>= 2.10), GenomicRanges (>= 1.17.46), GenomeInfoDb, methods Imports: Rcpp (>= 0.11.2), BiocGenerics, S4Vectors, Biobase LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 5e68a95de8ab0db07bd3fd05c71097b7 NeedsCompilation: yes Title: Representation and manipulation of genomic tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey , with contributions from Marcin Cieslik Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr source.ver: src/contrib/GenomicTuples_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenomicTuples_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GenomicTuples_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GenomicTuples_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenomicTuples_1.0.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.pdf vignetteTitles: GenomicTuples: Classes and Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Genominator Version: 1.20.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges, GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 MD5sum: e1a3aa898a91cc6dff38956ac7e08a20 NeedsCompilation: no Title: Analyze, manage and store genomic data Description: Tools for storing, accessing, analyzing and visualizing genomic data. biocViews: Infrastructure Author: James Bullard, Kasper Daniel Hansen Maintainer: James Bullard source.ver: src/contrib/Genominator_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Genominator_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Genominator_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Genominator_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Genominator_1.20.0.tgz vignettes: vignettes/Genominator/inst/doc/Genominator.pdf, vignettes/Genominator/inst/doc/plotting.pdf, vignettes/Genominator/inst/doc/withShortRead.pdf vignetteTitles: The Genominator User Guide, Plotting with Genominator, Working with the ShortRead Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Genominator/inst/doc/Genominator.R, vignettes/Genominator/inst/doc/plotting.R, vignettes/Genominator/inst/doc/withShortRead.R suggestsMe: oneChannelGUI Package: genoset Version: 1.20.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), Biobase (>= 2.15.1), GenomicRanges (>= 1.17.19) Imports: S4Vectors (>= 0.2.3), GenomeInfoDb (>= 1.1.3), IRanges, methods, graphics Suggests: RUnit, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: d2698964fe89063eb0cce27e0b23051d NeedsCompilation: yes Title: Provides classes similar to ExpressionSet for copy number analysis Description: Load, manipulate, and plot copynumber and BAF data. GenoSet class extends eSet by adding a "locData" slot for a GRanges object. This object contains feature genome location data and provides for efficient subsetting on genome location. Provides convenience functions for processing of copy number and B-Allele Frequency data. Provides the class RleDataFrame to store runs of data along the genome for multiple samples. biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset source.ver: src/contrib/genoset_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/genoset_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/genoset_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/genoset_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/genoset_1.20.0.tgz vignettes: vignettes/genoset/inst/doc/genoset.pdf vignetteTitles: genoset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R dependsOnMe: VegaMC importsMe: methyAnalysis Package: GenoView Version: 1.0.0 Depends: R (>= 2.10), gridExtra, GenomicRanges Imports: ggbio, ggplot2, grid, biovizBase Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, PFAM.db, AnnotationDbi, gtable, gWidgets, gWidgetsRGtk2, RGtk2, RColorBrewer License: GPL-3 MD5sum: 621041eafe8a0465c29e84dd80b1cd9d NeedsCompilation: no Title: Condensed, overlapped plotting of genomic data tracks Description: Superimposing input data over existing genomic references allows for fast, accurate visual comparisons. The GenoView package is a novel bioinformatics package which condenses genomic data tracks to offer a comprehensive view of genetic variants. Its main function is to display mutation data over exons and protein domains, which easily identifies potential genomic locations of interest. biocViews: Visualization Author: Sharon Lee, Dennis Wang Maintainer: Sharon Lee source.ver: src/contrib/GenoView_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GenoView_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GenoView_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GenoView_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GenoView_1.0.0.tgz vignettes: vignettes/GenoView/inst/doc/GenoView.pdf vignetteTitles: GenoView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoView/inst/doc/GenoView.R Package: GEOmetadb Version: 1.26.1 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 6fe25d00bb2c53682231a73a7ff92b14 NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr source.ver: src/contrib/GEOmetadb_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/GEOmetadb_1.26.1.zip win64.binary.ver: bin/windows64/contrib/3.1/GEOmetadb_1.26.1.zip mac.binary.ver: bin/macosx/contrib/3.1/GEOmetadb_1.26.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GEOmetadb_1.26.1.tgz vignettes: vignettes/GEOmetadb/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R htmlDocs: vignettes/GEOmetadb/inst/doc/GEOmetadb.html htmlTitles: "GEOmetadb" Package: GEOquery Version: 2.32.0 Depends: methods, Biobase Imports: XML, RCurl Suggests: limma, knitr, rmarkdown License: GPL-2 MD5sum: 7949467c518faeec2608db7cd2015e79 NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr source.ver: src/contrib/GEOquery_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GEOquery_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GEOquery_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GEOquery_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GEOquery_2.32.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.pdf vignetteTitles: GEOquery.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R htmlDocs: vignettes/GEOquery/inst/doc/GEOquery.html htmlTitles: "Using GEOquery" dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: ChIPXpress, SRAdb suggestsMe: dyebias, ELBOW, PGSEA, RGSEA, Rnits, TargetScore Package: GEOsubmission Version: 1.18.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: cb92bcb3b095c6aadecce4dfe669b142 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/GEOsubmission_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GEOsubmission_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GEOsubmission_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GEOsubmission_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GEOsubmission_1.18.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R Package: GEWIST Version: 1.10.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 300aacd83af72ce305283edab7dfbf72 NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng source.ver: src/contrib/GEWIST_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GEWIST_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GEWIST_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GEWIST_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GEWIST_1.10.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R Package: GGBase Version: 3.28.0 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics, Matrix, AnnotationDbi, digest, GenomicRanges Suggests: GGtools, illuminaHumanv1.db License: Artistic-2.0 MD5sum: 6a1d5d7c38b717b21e6781ac001a2ace NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGBase_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GGBase_3.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GGBase_3.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GGBase_3.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GGBase_3.28.0.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.pdf vignetteTitles: GGBase -- infrastructure for GGtools,, genetics of gene expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGBase/inst/doc/ggbase.R dependsOnMe: GGtools Package: ggbio Version: 1.14.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, biovizBase (>= 1.13.8), reshape2, gtable, Biobase, S4Vectors (>= 0.2.3), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.40), Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, gridExtra, scales, VariantAnnotation (>= 1.11.4), Hmisc, rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.17.13), OrganismDbi, GGally Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 7e3be019cc22c567c584e212401cc8a6 NeedsCompilation: no Title: Visualization tools for genomic data. Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin, Dianne Cook, Michael Lawrence Maintainer: Tengfei Yin URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr source.ver: src/contrib/ggbio_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ggbio_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ggbio_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ggbio_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ggbio_1.14.0.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggbio/inst/doc/ggbio.R dependsOnMe: CAFE, intansv importsMe: derfinderPlot, FourCSeq, GenoView, Rariant, regionReport, ReportingTools, SomaticSignatures suggestsMe: beadarray, gwascat, interactiveDisplay Package: GGtools Version: 5.2.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel Imports: methods, utils, stats, BiocGenerics, snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP.20120608, multtest, aod, rmeta Enhances: MatrixEQTL, Homo.sapiens, parallel, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: d20dd5e6149fb757b234042daa26cb45 NeedsCompilation: no Title: software and data for analyses in genetics of gene expression Description: software and data for analyses in genetics of gene expression and/or DNA methylation biocViews: Genetics, GeneExpression, GeneticVariability, SNP Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGtools_5.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GGtools_5.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GGtools_5.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GGtools_5.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GGtools_5.2.0.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGtools/inst/doc/GGtools.R suggestsMe: GGBase Package: girafe Version: 1.18.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors, IRanges (>= 1.3.53), Rsamtools, ShortRead (>= 1.3.21), intervals (>= 0.13.1), genomeIntervals (>= 1.7.3), grid Imports: methods, Biobase, Biostrings, BSgenome, graphics, grDevices, stats, utils, IRanges Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: ada2c1c2dcb3e353e038d1f3bc5fe429 NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/girafe_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/girafe_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/girafe_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/girafe_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/girafe_1.18.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R Package: GLAD Version: 2.30.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: 4b3d5c1aa4541553de3867971341e1d2 NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. source.ver: src/contrib/GLAD_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GLAD_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GLAD_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GLAD_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GLAD_2.30.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ITALICS, MANOR, seqCNA importsMe: ADaCGH2, ITALICS, MANOR, snapCGH Package: GlobalAncova Version: 3.34.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi Suggests: Biobase, annotate, GO.db, KEGG.db, golubEsets, hu6800.db, vsn, GSEABase, Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 1c6d3e3321d0d6f85109b4af871598db NeedsCompilation: yes Title: Calculates a global test for differential gene expression between groups Description: We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel source.ver: src/contrib/GlobalAncova_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GlobalAncova_3.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GlobalAncova_3.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GlobalAncova_3.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GlobalAncova_3.34.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R Package: globaltest Version: 5.20.0 Depends: methods Imports: Biobase, survival, AnnotationDbi, annotate, multtest, graphics Suggests: vsn, golubEsets, KEGG.db, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, annotate, Biobase, survival, GSEABase, penalized, gss, MASS, boot, rpart License: GPL (>= 2) MD5sum: c773f035a89f33828f2005ab8912a633 NeedsCompilation: no Title: Testing groups of covariates/features for association with a response variable, with applications to gene set testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos and Aldo Solari Maintainer: Jelle Goeman URL: http://www.msbi.nl/goeman source.ver: src/contrib/globaltest_5.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/globaltest_5.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/globaltest_5.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/globaltest_5.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/globaltest_5.20.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, SIM suggestsMe: topGO Package: gmapR Version: 1.8.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12) Imports: S4Vectors, IRanges, Rsamtools (>= 1.17.8), rtracklayer (>= 1.25.5), GenomicFeatures (>= 1.17.13), Biostrings, VariantAnnotation (>= 1.11.4), tools, Biobase, BSgenome, GenomicAlignments (>= 1.1.9), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 405ed172e882b3dd2ee3b67e086e2115 NeedsCompilation: yes Title: Provides convenience methods to work with GMAP and GSNAP from within R Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence source.ver: src/contrib/gmapR_1.8.0.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: VariantTools Package: GOexpress Version: 1.0.1 Depends: R (>= 3.0.2), grid, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), VennDiagram (>= 1.6.5), randomForest (>= 4.6) License: GPL (>= 3) MD5sum: beda2daf2c927834c0b16a0b6a62ba8d NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression levels of genes from a microarray or RNA-seq experiment and offers a clustering analysis to identify GO terms enriched in genes with expression levels best clustering two or more predefined groups of samples. Annotations for the genes present in the expression dataset are obtained from Ensembl through the biomaRt package. The random forest framework is used to evaluate the ability of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. An ANOVA approach is also available as an alternative statistical framework. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Metagenomics, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization Author: Kevin Rue-Albrecht [aut, cre], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress-release source.ver: src/contrib/GOexpress_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOexpress_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/GOexpress_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/GOexpress_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOexpress_1.0.1.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R Package: GOFunction Version: 1.14.0 Depends: R (>= 2.11.0), methods, Biobase (>= 2.8.0), graph (>= 1.26.0), Rgraphviz (>= 1.26.0), GO.db (>= 2.4.1), AnnotationDbi (>= 1.10.2), SparseM (>= 0.85) Imports: methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, SparseM License: GPL (>= 2) MD5sum: fb410946776c6fed1dc46d635c6641b6 NeedsCompilation: no Title: GO-function: deriving biologcially relevant functions from statistically significant functions Description: The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing. biocViews: GO, Pathways, Microarray, GeneSetEnrichment Author: Jing Wang Maintainer: Jing Wang source.ver: src/contrib/GOFunction_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOFunction_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GOFunction_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GOFunction_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOFunction_1.14.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOFunction/inst/doc/GOFunction.R Package: goProfiles Version: 1.28.0 Depends: Biobase, AnnotationDbi, GO.db Suggests: org.Hs.eg.db License: GPL-2 MD5sum: bb68d2965752f55726e8ad4d5f2a08a1 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Microarray, GO Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez source.ver: src/contrib/goProfiles_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/goProfiles_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/goProfiles_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/goProfiles_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/goProfiles_1.28.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R Package: GOSemSim Version: 1.24.1 Depends: R (>= 3.0.0) Imports: Rcpp, AnnotationDbi, GO.db LinkingTo: Rcpp Suggests: DOSE, clusterProfiler, org.Hs.eg.db, ChIPseeker, ReactomePA, BiocInstaller, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 87eb72f13c909f93be0a720586d0f80e NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: Implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for estimating GO semantic similarities. Support many species, including Anopheles, Arabidopsis, Bovine, Canine, Chicken, Chimp, Coelicolor, E coli strain K12 and Sakai, Fly, Gondii, Human, Malaria, Mouse, Pig, Rhesus, Rat, Worm, Xenopus, Yeast, and Zebrafish. biocViews: GO, Clustering, Pathways, Network Author: Guangchuang Yu Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/GOSemSim VignetteBuilder: knitr source.ver: src/contrib/GOSemSim_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOSemSim_1.24.1.zip win64.binary.ver: bin/windows64/contrib/3.1/GOSemSim_1.24.1.zip mac.binary.ver: bin/macosx/contrib/3.1/GOSemSim_1.24.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOSemSim_1.24.1.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.pdf vignetteTitles: An introduction to GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, Rcpi suggestsMe: ChIPseeker, ReactomePA, SemDist Package: goseq Version: 1.18.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) MD5sum: 6c91ecf9c65dacc85db65cc8de90c034 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Nadia Davidson source.ver: src/contrib/goseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/goseq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/goseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/goseq_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/goseq_1.18.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R suggestsMe: oneChannelGUI Package: GOSim Version: 1.8.0 Depends: GO.db, annotate, igraph Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp, utils LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 7f347fd2056e59b532a3a8632bd52c67 NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Network, DecisionTree Author: Holger Froehlich Maintainer: Holger Froehlich source.ver: src/contrib/GOSim_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOSim_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GOSim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GOSim_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOSim_1.8.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R Package: GOstats Version: 2.32.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.3.26), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), Biobase (>= 1.15.29), Category (>= 2.3.26), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), graph (>= 1.15.15), AnnotationForge Suggests: hgu95av2.db (>= 1.13.0), ALL, GO.db (>= 1.13.0), annotate, multtest, genefilter, RColorBrewer, Rgraphviz, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 3bf6e9a88466ea17533d7aedc5ac2dab NeedsCompilation: no Title: Tools for manipulating GO and microarrays. Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: R. Gentleman and S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GOstats_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOstats_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GOstats_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GOstats_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOstats_2.32.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: attract, MineICA, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, facopy, mvGST, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, MLP, MmPalateMiRNA, oneChannelGUI, phenoDist, qpgraph, safe Package: GOsummaries Version: 2.0.0 Depends: R (>= 2.15), ggplot2, Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: d644a05e93fb0758689009504f7d7343 NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries source.ver: src/contrib/GOsummaries_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOsummaries_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GOsummaries_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GOsummaries_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOsummaries_2.0.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R Package: GOTHiC Version: 1.2.2 Depends: R (>= 2.15.1), methods, utils, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors, IRanges, ShortRead, rtracklayer, ggplot2 Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 591281bdcfd3d4fb8555f8182a421bff NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: Sequencing, Preprocessing, Epigenetics Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud source.ver: src/contrib/GOTHiC_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/GOTHiC_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.1/GOTHiC_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.1/GOTHiC_1.2.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GOTHiC_1.2.2.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: goTools Version: 1.40.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: cdbccb356b65fe80ba4c8afe5d6544da NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet source.ver: src/contrib/goTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/goTools_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/goTools_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/goTools_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/goTools_1.40.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R Package: gpls Version: 1.38.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: af82f84151781dfe19cd879d2fa3fc45 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/gpls_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gpls_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gpls_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gpls_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gpls_1.38.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MCRestimate, MLInterfaces Package: gprege Version: 1.10.0 Depends: R (>= 2.8.0), gptk Suggests: spam License: AGPL-3 MD5sum: f904aeb4c4b5a045818fc80a2fd4094c NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis source.ver: src/contrib/gprege_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gprege_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gprege_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gprege_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gprege_1.10.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R Package: graph Version: 1.44.1 Depends: R (>= 2.10), methods Imports: stats, stats4, tools, utils, BiocGenerics (>= 0.1.11) Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 25fa879bf8f3066d28a9448f0cbf20b3 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/graph_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/graph_1.44.1.zip win64.binary.ver: bin/windows64/contrib/3.1/graph_1.44.1.zip mac.binary.ver: bin/macosx/contrib/3.1/graph_1.44.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/graph_1.44.1.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, CellNOptR, clipper, CNORfeeder, ddgraph, FEM, flowClust, gaggle, gaucho, GeneNetworkBuilder, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, KEGGgraph, maigesPack, MineICA, NCIgraph, nem, netresponse, NetSAM, pathRender, pkgDepTools, RbcBook1, RBGL, RBioinf, RCytoscape, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, ToPASeq, topGO, vtpnet importsMe: BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, DEGraph, EnrichmentBrowser, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GOFunction, GOSim, GOstats, GraphAT, graphite, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, mvGST, NCIgraph, nem, OncoSimulR, OrganismDbi, pathview, PCpheno, pkgDepTools, ppiStats, qpgraph, RchyOptimyx, rsbml, Rtreemix, SplicingGraphs, Streamer, topGO suggestsMe: AnnotationDbi, BiocCaseStudies, Category, DEGraph, EBcoexpress, ecolitk, GeneAnswers, gwascat, mmnet, MmPalateMiRNA, rBiopaxParser, rTRM, SPIA, VariantTools Package: GraphAlignment Version: 1.30.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 9d7e6daac78730102f9c83311d9545bc NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ source.ver: src/contrib/GraphAlignment_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GraphAlignment_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GraphAlignment_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GraphAlignment_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GraphAlignment_1.30.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R Package: GraphAT Version: 1.38.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: f2b60295fb17c00025151c5f41f76d0c NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise source.ver: src/contrib/GraphAT_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GraphAT_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GraphAT_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GraphAT_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GraphAT_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.12.0 Depends: R (>= 2.10) Imports: AnnotationDbi, graph, graphics, methods, org.Hs.eg.db, stats, utils Suggests: BiocStyle, DEGraph (>= 1.4), hgu133plus2.db, RCytoscape (>= 1.6), SPIA (>= 2.2), topologyGSA (>= 1.4.0), clipper, ALL License: AGPL-3 MD5sum: fa23b6ee34d2e7c6c330f4fd376c3835 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, Reactome and SPIKE databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales source.ver: src/contrib/graphite_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/graphite_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/graphite_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/graphite_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/graphite_1.12.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: ToPASeq importsMe: facopy, ReactomePA suggestsMe: clipper Package: GraphPAC Version: 1.8.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: ee87e6775dd6143e8362df80dede56c7 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/GraphPAC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GraphPAC_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GraphPAC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GraphPAC_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GraphPAC_1.8.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R Package: GRENITS Version: 1.18.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) Archs: i386, x64 MD5sum: 0f2baa07a043c518412ae4663d1e7d90 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey source.ver: src/contrib/GRENITS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GRENITS_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GRENITS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GRENITS_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GRENITS_1.18.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R Package: groHMM Version: 1.0.2 Depends: R (>= 3.0.2), MASS, S4Vectors, IRanges, GenomicRanges, GenomicAlignments, rtracklayer, parallel Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 8f9687797ff85fda29229736a695b563 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline. Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Minho Chae, W. Lee Kraus source.ver: src/contrib/groHMM_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/groHMM_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/groHMM_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/groHMM_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/groHMM_1.0.2.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R Package: GSAR Version: 1.0.0 Depends: R (>= 3.0.1), igraph (>= 0.7.0) Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 4641d1e3e351a93b29fddc5cc1cee3e4 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko source.ver: src/contrib/GSAR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSAR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSAR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSAR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSAR_1.0.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R Package: GSCA Version: 1.4.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, rgl, shinyRGL Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 5b7dd6af79b0b3dff74e5c5c9f554828 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji source.ver: src/contrib/GSCA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSCA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSCA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSCA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSCA_1.4.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R Package: GSEABase Version: 1.28.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), annotate, methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools License: Artistic-2.0 MD5sum: e22105b637ed4f676a22a035f11313d0 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GSEABase_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSEABase_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSEABase_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSEABase_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSEABase_1.28.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, gCMAP, npGSEA, PROMISE importsMe: Category, categoryCompare, cellHTS2, gCMAPWeb, GSRI, GSVA, HTSanalyzeR, PCpheno, phenoTest, PROMISE, ReportingTools suggestsMe: BiocCaseStudies, gage, GlobalAncova, globaltest, GOstats, GSAR, PGSEA, phenoTest Package: GSEAlm Version: 1.26.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 92224bc7277d8fdb7aae867506ac13f0 NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron source.ver: src/contrib/GSEAlm_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSEAlm_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSEAlm_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSEAlm_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSEAlm_1.26.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R importsMe: gCMAP Package: GSReg Version: 1.0.0 Depends: R (>= 2.13.1) Suggests: GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: 14fdfab337ee7dc8fdfde99e606b28e3 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari source.ver: src/contrib/GSReg_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSReg_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSReg_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSReg_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSReg_1.0.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R Package: GSRI Version: 2.14.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: 4b9d28ac97ba75e7d1829cc1e43a7903 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring URL: http://julian-gehring.github.com/GSRI/ source.ver: src/contrib/GSRI_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSRI_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/GSRI_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/GSRI_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSRI_2.14.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R Package: GSVA Version: 1.14.1 Depends: R (>= 2.13.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4) Suggests: limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) Archs: i386, x64 MD5sum: d765012e8c7d4218b4a3207e027b28bd NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: Microarray, Pathways, GeneSetEnrichment Author: Justin Guinney with contributions from Robert Castelo Maintainer: Justin Guinney URL: http://www.sagebase.org source.ver: src/contrib/GSVA_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/GSVA_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.1/GSVA_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.1/GSVA_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GSVA_1.14.1.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R Package: Gviz Version: 1.10.11 Depends: R (>= 2.10.0), methods, grid, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.18), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.20) Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16) Suggests: xtable, BSgenome.Hsapiens.UCSC.hg19, BiocStyle License: Artistic-2.0 MD5sum: d0ce8d73837bd58b0835037d21421381 NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray Author: Florian Hahne, Steffen Durinck, Robert Ivanek, Arne Mueller, Steve Lianoglou, Ge Tan , Lance Parsons Maintainer: Florian Hahne source.ver: src/contrib/Gviz_1.10.11.tar.gz win.binary.ver: bin/windows/contrib/3.1/Gviz_1.10.11.zip win64.binary.ver: bin/windows64/contrib/3.1/Gviz_1.10.11.zip mac.binary.ver: bin/macosx/contrib/3.1/Gviz_1.10.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Gviz_1.10.11.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.pdf vignetteTitles: Gviz users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, GGtools, methyAnalysis, methylPipe, PING, trackViewer suggestsMe: annmap, CNEr, gwascat, interactiveDisplay, QuasR, SplicingGraphs, STAN Package: gwascat Version: 1.10.0 Depends: R (>= 3.0.0) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, snpStats, Biostrings, Rsamtools, rtracklayer Suggests: DO.db, Gviz, ggbio, graph Enhances: SNPlocs.Hsapiens.dbSNP.20120608, pd.genomewidesnp.6 License: Artistic-2.0 MD5sum: 0ea6776457716c878c5afd369a585146 NeedsCompilation: no Title: representing and modeling data in the NHGRI GWAS catalog Description: representing and modeling data in the NHGRI GWAS catalog biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/gwascat_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/gwascat_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/gwascat_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/gwascat_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/gwascat_1.10.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf vignetteTitles: gwascat -- exploring NHGRI GWAS catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R dependsOnMe: vtpnet Package: GWASTools Version: 1.12.2 Depends: Biobase, ncdf, gdsfmt Imports: methods, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, quantsmooth Suggests: GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation License: Artistic-2.0 MD5sum: fd7095f2e5d67d474371a3b1b8dfd015 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp Maintainer: Stephanie M. Gogarten , Adrienne Stilp source.ver: src/contrib/GWASTools_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/GWASTools_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.1/GWASTools_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.1/GWASTools_1.12.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/GWASTools_1.12.2.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R, vignettes/GWASTools/inst/doc/VCF.R Package: h5vc Version: 2.0.6 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools, methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, bit64, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38 License: GPL (>= 3) Archs: i386, x64 MD5sum: 875b4b246f1616ede185c59a86ebb8bd NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. For detail see the webpage at http://www.ebi.ac.uk/~pyl/h5vc. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl VignetteBuilder: knitr source.ver: src/contrib/h5vc_2.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/h5vc_2.0.6.zip win64.binary.ver: bin/windows64/contrib/3.1/h5vc_2.0.6.zip mac.binary.ver: bin/macosx/contrib/3.1/h5vc_2.0.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/h5vc_2.0.6.tgz vignettes: vignettes/h5vc/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R htmlDocs: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html htmlTitles: "Building a minimal genome browser with h5vc and shiny", "h5vc -- Tour" Package: hapFabia Version: 1.8.0 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: a3dd13065044da2183ca3ca3319600fe NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html source.ver: src/contrib/hapFabia_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/hapFabia_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/hapFabia_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/hapFabia_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hapFabia_1.8.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R Package: Harshlight Version: 1.38.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 5fbfae1d1caf64af77fd99c8a1613f92 NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ source.ver: src/contrib/Harshlight_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Harshlight_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Harshlight_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Harshlight_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Harshlight_1.38.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R Package: HCsnip Version: 1.6.0 Depends: R(>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase License: GPL (>= 2) MD5sum: 0cb98691a1d2953b5a40c1f56f81c2e3 NeedsCompilation: no Title: Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree Description: Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy. biocViews: Microarray, aCGH, GeneExpression, Clustering Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/HCsnip_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HCsnip_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HCsnip_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HCsnip_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HCsnip_1.6.0.tgz vignettes: vignettes/HCsnip/inst/doc/HCsnip.pdf vignetteTitles: HCsnip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCsnip/inst/doc/HCsnip.R Package: HDTD Version: 1.0.0 License: GPL-3 MD5sum: e2ebef71a11e32eb476931d08a52ce71 NeedsCompilation: no Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: GeneExpression, DifferentialExpression, Genetics, Microarray, RNASeq, StatisticalMethod, Software Author: Anestis Touloumis, John C. Marioni and Simon Tavare Maintainer: Anestis Touloumis source.ver: src/contrib/HDTD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HDTD_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HDTD_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HDTD_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HDTD_1.0.0.tgz vignettes: vignettes/HDTD/inst/doc/Manual.pdf vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/Manual.R Package: Heatplus Version: 2.12.0 Imports: graphics, grDevices, stats Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 3c869e8bb5c259d52cf640019c03325c NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner source.ver: src/contrib/Heatplus_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Heatplus_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Heatplus_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Heatplus_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Heatplus_2.12.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: GeneAnswers, phenoTest, tRanslatome Package: HELP Version: 1.24.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: e94e543fcd083d630b41cf32397582b0 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson source.ver: src/contrib/HELP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HELP_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HELP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HELP_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HELP_1.24.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R Package: HEM Version: 1.38.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 7431006e66ab76f50cfd4b66b34f3fff NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ source.ver: src/contrib/HEM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HEM_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HEM_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HEM_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HEM_1.38.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HEM/inst/doc/HEM.R Package: hiAnnotator Version: 1.0.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, plyr, BSgenome, ggplot2, scales Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: caee1cf35f2e77d5e16baa0751377de0 NeedsCompilation: no Title: Functions for annotating GRanges objects. Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr source.ver: src/contrib/hiAnnotator_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/hiAnnotator_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/hiAnnotator_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/hiAnnotator_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hiAnnotator_1.0.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R htmlDocs: vignettes/hiAnnotator/inst/doc/Intro.html htmlTitles: "Introduction" dependsOnMe: hiReadsProcessor Package: HilbertVis Version: 1.24.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 481534947e778e306575a5d99465747a NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert source.ver: src/contrib/HilbertVis_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HilbertVis_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HilbertVis_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HilbertVis_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HilbertVis_1.24.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR Package: HilbertVisGUI Version: 1.24.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) Archs: i386, x64 MD5sum: d90a54ed4444e6549c51723ef5dae450 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4 source.ver: src/contrib/HilbertVisGUI_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HilbertVisGUI_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HilbertVisGUI_1.24.0.zip vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.R Package: hiReadsProcessor Version: 1.0.0 Depends: Biostrings, GenomicAlignments, xlsx, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, plyr Suggests: knitr, testthat, BiocGenerics, rSFFreader License: GPL-3 MD5sum: d1d5fc655f6dc8d6bdcf4b2428342610 NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data. Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, JRE, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr source.ver: src/contrib/hiReadsProcessor_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/hiReadsProcessor_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/hiReadsProcessor_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/hiReadsProcessor_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hiReadsProcessor_1.0.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R htmlDocs: vignettes/hiReadsProcessor/inst/doc/Tutorial.html htmlTitles: "Introduction" Package: HiTC Version: 1.10.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix Suggests: BiocStyle License: Artistic-2.0 MD5sum: 50829814dd3e4822864bd383f3959ba3 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing Author: Nicolas Servant Maintainer: Nicolas Servant source.ver: src/contrib/HiTC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HiTC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HiTC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HiTC_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HiTC_1.10.0.tgz vignettes: vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hight-Throughput Chromosome Conformation Capture analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiTC.R Package: HMMcopy Version: 1.8.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: 283ba0cfabe5d33b32dc9f69811ab9a4 NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai , Gavin Ha , Sohrab Shah source.ver: src/contrib/HMMcopy_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HMMcopy_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HMMcopy_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HMMcopy_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HMMcopy_1.8.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R Package: hopach Version: 2.26.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 53d62dfb6a706c63bf7743134517e03f NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ source.ver: src/contrib/hopach_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/hopach_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/hopach_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/hopach_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hopach_2.26.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest suggestsMe: BiocCaseStudies Package: hpar Version: 1.8.1 Depends: R (>= 2.15) Suggests: org.Hs.eg.db, GO.db, knitr License: Artistic-2.0 MD5sum: 7d1359c0c730f5649e259a3934c995dd NeedsCompilation: no Title: Human Protein Atlas in R Description: A simple interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto Maintainer: Laurent Gatto source.ver: src/contrib/hpar_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/hpar_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/hpar_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/hpar_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hpar_1.8.1.tgz vignettes: vignettes/hpar/inst/doc/hpar.pdf vignetteTitles: hpar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R Package: HTqPCR Version: 1.20.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: caa02b2417c06e6b293f864e830d8488 NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software source.ver: src/contrib/HTqPCR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HTqPCR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HTqPCR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HTqPCR_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HTqPCR_1.20.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR Package: HTSanalyzeR Version: 2.18.0 Depends: R (>= 2.15), igraph, methods Imports: graph, igraph, GSEABase, BioNet, cellHTS2, AnnotationDbi, biomaRt, RankProd Suggests: KEGG.db, GO.db, org.Dm.eg.db, GOstats, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, snow License: Artistic-2.0 MD5sum: 822586081e590ddf652e91253360381a NeedsCompilation: no Title: Gene set over-representation, enrichment and network analyses for high-throughput screens Description: This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over-representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package. biocViews: CellBasedAssays, MultipleComparison Author: Xin Wang , Camille Terfve , John C. Rose , Florian Markowetz Maintainer: Xin Wang source.ver: src/contrib/HTSanalyzeR_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HTSanalyzeR_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HTSanalyzeR_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HTSanalyzeR_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HTSanalyzeR_2.18.0.tgz vignettes: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.pdf vignetteTitles: Main vignette:Gene set enrichment and network analysis of high-throughput RNAi screen data using HTSanalyzeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.R importsMe: phenoTest suggestsMe: RTN Package: HTSeqGenie Version: 3.16.1 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), IRanges (>= 1.21.39), GenomicRanges (>= 1.7.12), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7) Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: 87a6a9c7dbbdbdb86949c646d9a48e29 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder source.ver: src/contrib/HTSeqGenie_3.16.1.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R Package: htSeqTools Version: 1.12.0 Depends: R (>= 2.12.2), methods, BiocGenerics (>= 0.1.0), Biobase, IRanges, methods, MASS, BSgenome, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.11) Enhances: multicore License: GPL (>=2) MD5sum: 41874e70231fb5e596304a3a7bcab3a6 NeedsCompilation: no Title: Quality Control, Visualization and Processing for High-Throughput Sequencing data Description: We provide efficient, easy-to-use tools for High-Throughput Sequencing (ChIP-seq, RNAseq etc.). These include MDS plots (analogues to PCA), detecting inefficient immuno-precipitation or over-amplification artifacts, tools to identify and test for genomic regions with large accumulation of reads, and visualization of coverage profiles. biocViews: Sequencing, QualityControl Author: Evarist Planet, Camille Stephan-Otto, Oscar Reina, Oscar Flores, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/htSeqTools_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/htSeqTools_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/htSeqTools_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/htSeqTools_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/htSeqTools_1.12.0.tgz vignettes: vignettes/htSeqTools/inst/doc/htSeqTools.pdf vignetteTitles: Manual for the htSeqTools library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/htSeqTools/inst/doc/htSeqTools.R Package: HTSFilter Version: 1.6.0 Depends: methods, Biobase (>= 2.16.0), R (>= 2.10.0) Imports: DESeq (>= 1.8.3), edgeR (>= 2.6.12), DESeq2 (>= 1.2.8), GenomicRanges, IRanges Suggests: EDASeq (>= 1.2.0) License: Artistic-2.0 MD5sum: 5c8bad86badf41b7494537d74a8b4120 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression Author: Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic Maintainer: Andrea Rau source.ver: src/contrib/HTSFilter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HTSFilter_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HTSFilter_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HTSFilter_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HTSFilter_1.6.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R Package: HybridMTest Version: 1.10.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 3985fe331b6e300c4a8452521b22c6dc NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana source.ver: src/contrib/HybridMTest_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/HybridMTest_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/HybridMTest_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/HybridMTest_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/HybridMTest_1.10.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R Package: hyperdraw Version: 1.18.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: a0b85086cdef259ebd2833ffc5d34ac2 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz source.ver: src/contrib/hyperdraw_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/hyperdraw_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/hyperdraw_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/hyperdraw_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hyperdraw_1.18.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR Package: hypergraph Version: 1.38.1 Depends: R (>= 2.1.0), methods, utils, graph License: Artistic-2.0 MD5sum: 49f9696e3ea6d5444a7de38787acd0c8 NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/hypergraph_1.38.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/hypergraph_1.38.1.zip win64.binary.ver: bin/windows64/contrib/3.1/hypergraph_1.38.1.zip mac.binary.ver: bin/macosx/contrib/3.1/hypergraph_1.38.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/hypergraph_1.38.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.10.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 10da947298b5d5338d40341dc03bf453 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei source.ver: src/contrib/iASeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iASeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iASeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iASeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iASeq_1.10.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R Package: iBBiG Version: 1.10.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 80191bcf99ca56ca89a22e85eed17c19 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ source.ver: src/contrib/iBBiG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iBBiG_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iBBiG_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iBBiG_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iBBiG_1.10.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R Package: ibh Version: 1.14.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 08bec973dd3da06ee09e179788275df5 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz source.ver: src/contrib/ibh_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ibh_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ibh_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ibh_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ibh_1.14.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R Package: iBMQ Version: 1.6.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 62db75df5025043664217bcf5b6f9cb6 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP source.ver: src/contrib/iBMQ_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iBMQ_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iBMQ_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iBMQ_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iBMQ_1.6.0.tgz vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R Package: Icens Version: 1.38.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: feb36ee03e0994819940bf37057d20f3 NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Icens_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Icens_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Icens_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Icens_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Icens_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iChip Version: 1.20.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: aa25f1056142a7ff073e1a9c838edf7f NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: This package uses hidden Ising models to identify enriched genomic regions in ChIP-chip data. It can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iChip_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iChip_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iChip_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iChip_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iChip_1.20.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R Package: iClusterPlus Version: 1.2.0 Depends: R (>= 2.15.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 3ac5179b87cdcd1902286b4d9b5d51d0 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen source.ver: src/contrib/iClusterPlus_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iClusterPlus_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iClusterPlus_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iClusterPlus_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iClusterPlus_1.2.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iClusterPlus/inst/doc/iClusterPlus.R Package: IdeoViz Version: 1.0.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 0cba5ddd9f4f2d6df37f35c9a90c7578 NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai source.ver: src/contrib/IdeoViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IdeoViz_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IdeoViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IdeoViz_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IdeoViz_1.0.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R Package: idiogram Version: 1.42.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 2a0c5b436dba380aa811ff9d663c3cea NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/idiogram_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/idiogram_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/idiogram_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/idiogram_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/idiogram_1.42.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependsOnMe: reb Package: IdMappingAnalysis Version: 1.10.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: ca0800308c646e26e1494fada3b8ce67 NeedsCompilation: no Title: ID Mapping Analysis Description: Identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingAnalysis_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IdMappingAnalysis_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IdMappingAnalysis_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IdMappingAnalysis_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IdMappingAnalysis_1.10.0.tgz vignettes: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.pdf vignetteTitles: Critically comparing identifier maps retrieved from bioinformatics annotation resources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.R Package: IdMappingRetrieval Version: 1.12.0 Depends: R.oo, XML, RCurl, rChoiceDialogs, ENVISIONQuery Imports: biomaRt, ENVISIONQuery, DAVIDQuery, AffyCompatible, R.methodsS3, R.oo, utils License: GPL-2 MD5sum: 6f9c41143c324568a1c8fef136cc5f67 NeedsCompilation: no Title: ID Mapping Data Retrieval Description: Data retrieval for identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingRetrieval_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IdMappingRetrieval_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IdMappingRetrieval_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IdMappingRetrieval_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IdMappingRetrieval_1.12.0.tgz vignettes: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.pdf vignetteTitles: Collection and subsequent fast retrieval of identifier mapping related information from various online sources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.R Package: illuminaio Version: 0.8.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: ee4b26c216daedf1d776ff419459cf8c NeedsCompilation: yes Title: Parsing Illumina microarray output files Description: Tools for parsing Illuminas microarray output files, including IDAT. biocViews: Infrastructure, DataImport Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio source.ver: src/contrib/illuminaio_0.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/illuminaio_0.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/illuminaio_0.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/illuminaio_0.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/illuminaio_0.8.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/EncryptedFormat.R, vignettes/illuminaio/inst/doc/illuminaio.R importsMe: beadarray, crlmm, methylumi, minfi suggestsMe: limma Package: imageHTS Version: 1.16.0 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: 1a36bf4f45ead35f996f3bb825bfbef9 NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry source.ver: src/contrib/imageHTS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/imageHTS_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/imageHTS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/imageHTS_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/imageHTS_1.16.0.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependsOnMe: phenoDist Package: IMPCdata Version: 1.0.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 13d8a52dd9561b859880e579d56f5e93 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason source.ver: src/contrib/IMPCdata_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IMPCdata_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IMPCdata_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IMPCdata_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IMPCdata_1.0.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R Package: impute Version: 1.40.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: e9331bb65814dd43f36cd6f484a53808 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan source.ver: src/contrib/impute_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/impute_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/impute_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/impute_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/impute_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, FEM, HCsnip importsMe: ChAMP, MSnbase, Rnits suggestsMe: BioNet Package: INPower Version: 1.2.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 64078d94af309d9113436b5829be1e86 NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler source.ver: src/contrib/INPower_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/INPower_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/INPower_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/INPower_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/INPower_1.2.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R Package: inSilicoDb Version: 2.2.1 Depends: R (>= 3.0.0), rjson, Biobase, RCurl Suggests: limma License: GPL-2 MD5sum: 1b9b1f7088eb0a9ecbd2152149c78f15 NeedsCompilation: no Title: Access to the InSilico Database Description: Access expert curated and normalized microarray eSet datasets from the InSilico Database. biocViews: Microarray, DataImport Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: https://insilicodb.com source.ver: src/contrib/inSilicoDb_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/inSilicoDb_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/inSilicoDb_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/inSilicoDb_2.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/inSilicoDb_2.2.1.tgz vignettes: vignettes/inSilicoDb/inst/doc/inSilicoDb.pdf, vignettes/inSilicoDb/inst/doc/inSilicoDb2.pdf vignetteTitles: Using the inSilicoDb package, Using the inSilicoDb v2 package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoDb/inst/doc/inSilicoDb.R, vignettes/inSilicoDb/inst/doc/inSilicoDb2.R suggestsMe: inSilicoMerging Package: inSilicoMerging Version: 1.10.1 Depends: R (>= 2.11.1), Biobase Suggests: BiocGenerics, inSilicoDb License: GPL-2 MD5sum: 9d8673824481473be7026ad45f505da5 NeedsCompilation: no Title: Collection of Merging Techniques for Gene Expression Data Description: Collection of techniques to remove inter-study bias when combining gene expression data originating from different studies. biocViews: Microarray Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: http://insilicodb.com/ source.ver: src/contrib/inSilicoMerging_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/inSilicoMerging_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.1/inSilicoMerging_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.1/inSilicoMerging_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/inSilicoMerging_1.10.1.tgz vignettes: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.pdf vignetteTitles: Using the inSilicoMerging package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.R Package: intansv Version: 1.6.2 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: 0471b3b43c95decb23863e8504c5d4b5 NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao source.ver: src/contrib/intansv_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/intansv_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/intansv_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/intansv_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/intansv_1.6.2.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R Package: interactiveDisplay Version: 1.4.1 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase, shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr,GenomicRanges, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: 36a45f5a8f0810d3b977cd6f1da9d801 NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplay_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/interactiveDisplay_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/interactiveDisplay_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/interactiveDisplay_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/interactiveDisplay_1.4.1.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R htmlDocs: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.html htmlTitles: "Using interactiveDisplay for Bioconductor object visualization and modification" dependsOnMe: metagenomeSeq importsMe: AnnotationHub Package: interactiveDisplayBase Version: 1.4.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudio License: Artistic-2.0 MD5sum: 15ca81b26758e44eb4603cf4effbe079 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplayBase_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/interactiveDisplayBase_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/interactiveDisplayBase_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/interactiveDisplayBase_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/interactiveDisplayBase_1.4.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R htmlDocs: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html htmlTitles: "Using interactiveDisplayBase for Bioconductor object visualization and modification" importsMe: interactiveDisplay Package: inveRsion Version: 1.14.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 3d0a3d26953e5c87dfc7daf906def9bf NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres source.ver: src/contrib/inveRsion_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/inveRsion_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/inveRsion_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/inveRsion_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/inveRsion_1.14.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R Package: iontree Version: 1.12.0 Depends: methods, rJava, RSQLite, XML Suggests: iontreeData License: GPL-2 MD5sum: c931cfb7ea0bd3ea6c87a6b1d5bfcd81 NeedsCompilation: no Title: Data management and analysis of ion trees from ion-trap mass spectrometry Description: Ion fragmentation provides structural information for metabolite identification. This package provides utility functions to manage and analyse MS2/MS3 fragmentation data from ion trap mass spectrometry. It was designed for high throughput metabolomics data with many biological samples and a large numer of ion trees collected. Tests have been done with data from low-resolution mass spectrometry but could be readily extended to precursor ion based fragmentation data from high resoultion mass spectrometry. biocViews: Metabolomics, MassSpectrometry Author: Mingshu Cao Maintainer: Mingshu Cao source.ver: src/contrib/iontree_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iontree_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iontree_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iontree_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iontree_1.12.0.tgz vignettes: vignettes/iontree/inst/doc/iontree_doc.pdf vignetteTitles: MSn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iontree/inst/doc/iontree_doc.R Package: iPAC Version: 1.10.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 396aab8d35777469d5435e87fcb41ea1 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/iPAC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iPAC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iPAC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iPAC_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iPAC_1.10.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R Package: IPPD Version: 1.14.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 58931d2566d2793c7ee9eea147e8b9ad NeedsCompilation: yes Title: Isotopic peak pattern deconvolution for Protein Mass Spectrometry by template matching Description: The package provides functionality to extract isotopic peak patterns from raw mass spectra. This is done by fitting a large set of template basis functions to the raw spectrum using either nonnegative least squares or least absolute deviation fittting. The package offers a flexible function which tries to estimate model parameters in a way tailored to the peak shapes in the data. The package also provides functionality to process LCMS runs. biocViews: Proteomics Author: Martin Slawski , Rene Hussong , Andreas Hildebrandt , Matthias Hein Maintainer: Martin Slawski source.ver: src/contrib/IPPD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IPPD_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IPPD_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IPPD_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IPPD_1.14.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IPPD/inst/doc/IPPD.R Package: IRanges Version: 2.0.1 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.5) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, BSgenome.Celegans.UCSC.ce2, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 1a83531d2e5a6202d310abf548a06383 NeedsCompilation: yes Title: Infrastructure for manipulating intervals on sequences Description: The package provides efficient low-level and highly reusable S4 classes for storing ranges of integers, RLE vectors (Run-Length Encoding), and, more generally, data that can be organized sequentially (formally defined as Vector objects), as well as views on these Vector objects. Efficient list-like classes are also provided for storing big collections of instances of the basic classes. All classes in the package use consistent naming and share the same rich and consistent "Vector API" as much as possible. biocViews: Infrastructure, DataRepresentation Author: H. Pages, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/IRanges_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/IRanges_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/IRanges_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/IRanges_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IRanges_2.0.1.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf, vignettes/IRanges/inst/doc/RleTricks.pdf vignetteTitles: An Introduction to IRanges, Rle Tips and Tricks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R, vignettes/IRanges/inst/doc/RleTricks.R dependsOnMe: AnnotationHub, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, bsseq, bumphunter, CAFE, casper, CexoR, ChIPpeakAnno, chipseq, chroGPS, cn.mops, CSAR, customProDB, DASiR, deepSNV, DESeq2, DEXSeq, DirichletMultinomial, DNaseR, epigenomix, exomeCopy, GenomeInfoDb, genomes, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, girafe, groHMM, Gviz, HiTC, HMMcopy, htSeqTools, IdeoViz, methyAnalysis, MotifDb, motifRG, nucleR, oneChannelGUI, OTUbase, pepStat, PING, proBAMr, PSICQUIC, RefNet, rfPred, rGADEM, RIPSeeker, rMAT, Rsamtools, scsR, segmentSeq, SGSeq, SomatiCA, SplicingGraphs, TEQC, TitanCNA, triform, triplex, TSSi, VariantTools, XVector importsMe: AllelicImbalance, annmap, ArrayExpressHTS, ballgown, BayesPeak, beadarray, Biostrings, biovizBase, BiSeq, BitSeq, BSgenome, CAGEr, charm, chipenrich, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, cleaver, CNEr, CNVrd2, cobindR, compEpiTools, copynumber, csaw, customProDB, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, DOQTL, easyRNASeq, EDASeq, facopy, fastseg, flipflop, flowQ, FunciSNP, gCMAPWeb, GenomicAlignments, GenomicInteractions, genoset, ggbio, GGtools, girafe, gmapR, GOTHiC, gwascat, h5vc, HTSeqGenie, HTSFilter, intansv, M3D, MEDIPS, methVisual, methyAnalysis, methylPipe, MethylSeekR, methylumi, minfi, MinimumDistance, mosaics, MotIV, MSnbase, NarrowPeaks, nucleR, oligoClasses, Pbase, pdInfoBuilder, PICS, plethy, polyester, prebs, Pviz, qpgraph, QuasR, R453Plus1Toolbox, Rariant, REDseq, regionReport, Repitools, ReportingTools, rGADEM, rMAT, rnaSeqMap, Rolexa, Rqc, rSFFreader, RSVSim, RTN, rtracklayer, SCAN.UPC, SeqArray, seqplots, SeqVarTools, ShortRead, SNPchip, SomatiCA, SomaticSignatures, spliceR, SplicingGraphs, TFBSTools, tracktables, triform, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, XVector suggestsMe: BaseSpaceR, BiocGenerics, HilbertVis, HilbertVisGUI, MiRaGE, S4Vectors, STAN Package: iSeq Version: 1.18.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 912321b883f20175b51e01329ec613d2 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: This package uses Bayesian hidden Ising models to identify IP-enriched genomic regions from ChIP-seq data. It can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iSeq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iSeq_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iSeq_1.18.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R Package: isobar Version: 1.12.2 Depends: R (>= 2.10.0), Biobase, stats, methods, plyr Imports: distr Suggests: MSnbase, OrgMassSpecR, XML, biomaRt, ggplot2, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, distr, DBI, MASS License: LGPL-2 MD5sum: 5f9d1133a516d0d303689ef52bd5beaa NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: http://www.ms-isobar.org source.ver: src/contrib/isobar_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/isobar_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.1/isobar_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.1/isobar_1.12.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/isobar_1.12.2.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, isobar package for iTRAQ and TMT protein quantification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar.R Package: IsoGeneGUI Version: 2.0.0 Depends: tcltk Imports: Rcpp, tkrplot, multtest, relimp, gdata, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, xlsx, ff, Biobase Suggests: RUnit License: GPL-2 MD5sum: 580882abda4a124c59b03cac3988a9ae NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/software/isogenegui source.ver: src/contrib/IsoGeneGUI_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/IsoGeneGUI_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/IsoGeneGUI_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/IsoGeneGUI_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/IsoGeneGUI_2.0.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R Package: ITALICS Version: 2.26.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 52b346fb7c16977de9e221a2fe92c588 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr source.ver: src/contrib/ITALICS_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ITALICS_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ITALICS_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ITALICS_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ITALICS_2.26.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R Package: iterativeBMA Version: 1.24.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: ddfcc1bb07d95aaebcf2d959ff17f433 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html source.ver: src/contrib/iterativeBMA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iterativeBMA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iterativeBMA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iterativeBMA_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iterativeBMA_1.24.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R Package: iterativeBMAsurv Version: 1.24.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 21183f0929540181511a303bb65b0932 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected source.ver: src/contrib/iterativeBMAsurv_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/iterativeBMAsurv_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/iterativeBMAsurv_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/iterativeBMAsurv_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/iterativeBMAsurv_1.24.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R Package: jmosaics Version: 1.6.0 Depends: R (>= 2.15.2), mosaics License: GPL (>= 2) MD5sum: 5fece0ee6dd2614e74469bb9911f17ee NeedsCompilation: no Title: Joint analysis of multiple ChIP-Seq data sets Description: jmosaics detects enriched regions of ChIP-seq data sets jointly. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Xin Zeng Maintainer: Xin Zeng source.ver: src/contrib/jmosaics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/jmosaics_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/jmosaics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/jmosaics_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/jmosaics_1.6.0.tgz vignettes: vignettes/jmosaics/inst/doc/jmosaics.pdf vignetteTitles: jMOSAiCS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/jmosaics/inst/doc/jmosaics.R Package: joda Version: 1.14.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: b2ecdad4905cbd977702da2dab47dddc NeedsCompilation: no Title: JODA algorithm for quantifying gene deregulation using knowledge Description: Package 'joda' implements three steps of an algorithm called JODA. The algorithm computes gene deregulation scores. For each gene, its deregulation score reflects how strongly an effect of a certain regulator's perturbation on this gene differs between two different cell populations. The algorithm utilizes regulator knockdown expression data as well as knowledge about signaling pathways in which the regulators are involved (formalized in a simple matrix model). biocViews: Microarray, Pathways, GraphAndNetwork, StatisticalMethod, NetworkInference Author: Ewa Szczurek Maintainer: Ewa Szczurek URL: http://www.bioconductor.org source.ver: src/contrib/joda_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/joda_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/joda_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/joda_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/joda_1.14.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/joda/inst/doc/JodaVignette.R Package: KCsmart Version: 2.24.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 09ef13e3aa774d3e80afd264bcfa4589 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde source.ver: src/contrib/KCsmart_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/KCsmart_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/KCsmart_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/KCsmart_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/KCsmart_2.24.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R Package: kebabs Version: 1.0.5 Depends: R (>= 3.1.0), Biostrings (>= 2.33.14), kernlab Imports: methods, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.5.8), S4Vectors (>= 0.2.4), e1071, LiblineaR LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, apcluster, Biobase, BiocGenerics License: GPL (>= 2.1) Archs: i386, x64 MD5sum: c36a7c5a28483ce5a259bf6726f6ae16 NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ source.ver: src/contrib/kebabs_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/kebabs_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/kebabs_1.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/kebabs_1.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/kebabs_1.0.5.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R, vignettes/kebabs/inst/doc/KeBABS.R Package: KEGGgraph Version: 1.24.0 Depends: R (>= 2.10), methods, XML (>= 2.3-0), graph Imports: methods, XML, graph Suggests: Rgraphviz, RBGL, RUnit, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: dbbba3c75e835a11e48de62394ff354c NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization Author: Jitao David Zhang, with inputs from Paul Shannon Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com source.ver: src/contrib/KEGGgraph_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/KEGGgraph_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/KEGGgraph_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/KEGGgraph_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/KEGGgraph_1.24.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: EnrichmentBrowser, pathview, ROntoTools, SPIA importsMe: clipper, DEGraph, NCIgraph suggestsMe: DEGraph, GenomicRanges Package: keggorthology Version: 2.18.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: e60352937c27391eedcdf3eb7b412db1 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/keggorthology_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/keggorthology_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/keggorthology_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/keggorthology_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/keggorthology_2.18.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces Package: KEGGprofile Version: 1.8.2 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: 9be94e35819db6e73985db3a8f1cb3cd NeedsCompilation: no Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways Author: Shilin Zhao, Yu Shyr Maintainer: Shilin Zhao source.ver: src/contrib/KEGGprofile_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/KEGGprofile_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.1/KEGGprofile_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.1/KEGGprofile_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/KEGGprofile_1.8.2.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGprofile/inst/doc/KEGGprofile.R suggestsMe: FGNet Package: KEGGREST Version: 1.6.4 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 38287291e2a29a8d65164940d2089678 NeedsCompilation: no Title: Client-side REST access to KEGG Description: A package that provides a client interface to the KEGG REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/KEGGREST_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/KEGGREST_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.1/KEGGREST_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.1/KEGGREST_1.6.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/KEGGREST_1.6.4.tgz vignettes: vignettes/KEGGREST/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R htmlDocs: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html htmlTitles: "Accessing the KEGG REST API" dependsOnMe: PAPi, ROntoTools importsMe: EnrichmentBrowser, gage, mmnet, pathview Package: lapmix Version: 1.32.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: d91d920799612fd935bb9ff5c5865630 NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch source.ver: src/contrib/lapmix_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/lapmix_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/lapmix_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/lapmix_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/lapmix_1.32.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R Package: LBE Version: 1.34.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 8e7308754a944850e0302c635bf68713 NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso source.ver: src/contrib/LBE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LBE_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LBE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LBE_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LBE_1.34.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R Package: les Version: 1.16.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: c2223ffb00cd8358939d3ee53a2d444f NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring URL: http://julian-gehring.github.com/les/ source.ver: src/contrib/les_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/les_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/les_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/les_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/les_1.16.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI Package: limma Version: 3.22.7 Depends: R (>= 2.3.0), methods Suggests: statmod (>= 1.2.2), splines, locfit, MASS, ellipse, Biobase, affy, vsn, AnnotationDbi, org.Hs.eg.db, GO.db, illuminaio, BiasedUrn License: GPL (>=2) Archs: i386, x64 MD5sum: 250556c0a8b24ee6c9488965a11d67eb NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Genetics, Bayesian, Clustering, Regression, TimeCourse, Microarray, microRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: Gordon Smyth [cre,aut], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Natalie Thorne [ctb], Davis McCarthy [ctb], Di Wu [ctb], Yifang Hu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma source.ver: src/contrib/limma_3.22.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/limma_3.22.7.zip win64.binary.ver: bin/windows64/contrib/3.1/limma_3.22.7.zip mac.binary.ver: bin/macosx/contrib/3.1/limma_3.22.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/limma_3.22.7.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limma/inst/doc/intro.R dependsOnMe: a4Base, AffyExpress, attract, birta, CALIB, cghMCR, codelink, convert, COPDSexualDimorphism, Cormotif, coRNAi, DiffBind, DMRcate, DrugVsDisease, edgeR, ExiMiR, FEM, gCMAP, HTqPCR, limmaGUI, maigesPack, marray, metagenomeSeq, metaseqR, MLSeq, MmPalateMiRNA, nem, PADOG, qpcrNorm, qusage, Ringo, Rnits, snapCGH, SSPA, tRanslatome, TurboNorm, wateRmelon importsMe: affycoretools, affylmGUI, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, attract, ballgown, beadarray, betr, bumphunter, CALIB, CancerMutationAnalysis, casper, ChAMP, charm, ChIPpeakAnno, compcodeR, csaw, EnrichmentBrowser, erccdashboard, explorase, flowBin, GeneSelectMMD, GeneSelector, GGBase, GOsummaries, HTqPCR, iChip, lmdme, LVSmiRNA, maSigPro, minfi, missMethyl, MmPalateMiRNA, monocle, OLIN, PAA, PADOG, PECA, pepStat, phenoTest, PhenStat, Ringo, RNAinteract, RNAither, RTN, RTopper, SimBindProfiles, snapCGH, systemPipeR, timecourse, ToPASeq, tweeDEseq, vsn suggestsMe: ABarray, ADaCGH2, beadarraySNP, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, dyebias, ELBOW, gage, GeneSelector, GEOquery, GSRI, GSVA, Heatplus, inSilicoDb, isobar, les, lumi, methylumi, MLP, npGSEA, oligo, oneChannelGUI, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, sva Package: limmaGUI Version: 1.42.0 Depends: limma, tcltk Suggests: statmod, R2HTML, xtable, tkrplot License: LGPL MD5sum: fc05f4f964a7be245d8cc6414820bf3f NeedsCompilation: no Title: GUI for limma package Description: A Graphical User Interface for the limma Microarray package biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, GUI Author: James Wettenhall Division of Genetics and Bioinformatics, WEHI Maintainer: Keith Satterley URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/limmaGUI_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/limmaGUI_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/limmaGUI_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/limmaGUI_1.42.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/extract.R, vignettes/limmaGUI/inst/doc/limmaGUI.R Package: LiquidAssociation Version: 1.20.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: a3aa4c280d60cbbb808089be0e2faec6 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho source.ver: src/contrib/LiquidAssociation_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LiquidAssociation_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LiquidAssociation_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LiquidAssociation_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LiquidAssociation_1.20.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation Package: lmdme Version: 1.8.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: da782491c8f7f36ed29fe2433476bc50 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 source.ver: src/contrib/lmdme_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/lmdme_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/lmdme_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/lmdme_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/lmdme_1.8.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R Package: LMGene Version: 2.22.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 7c61dbabd55bfb7831dc440d68c7a84c NeedsCompilation: no Title: LMGene Software for Data Transformation and Identification of Differentially Expressed Genes in Gene Expression Arrays Description: LMGene package for analysis of microarray data using a linear model and glog data transformation biocViews: Microarray, DifferentialExpression, Preprocessing Author: David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu Maintainer: Blythe Durbin-Johnson URL: http://dmrocke.ucdavis.edu/software.html source.ver: src/contrib/LMGene_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LMGene_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LMGene_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LMGene_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LMGene_2.22.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LMGene/inst/doc/LMGene.R Package: logicFS Version: 1.36.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 210a5b71f58173b383136f44933eaa8a NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender Maintainer: Holger Schwender source.ver: src/contrib/logicFS_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/logicFS_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/logicFS_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/logicFS_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/logicFS_1.36.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio Package: logitT Version: 1.24.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: cd8722c7e5c1cde2900b383ca37d2f50 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org source.ver: src/contrib/logitT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/logitT_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/logitT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/logitT_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/logitT_1.24.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R Package: lol Version: 1.14.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: c50f9d690620ef6251f8d078fed11d3d NeedsCompilation: no Title: Lots Of Lasso Description: Various optimization methods for Lasso inference with matrix warpper biocViews: StatisticalMethod Author: Yinyin Yuan Maintainer: Yinyin Yuan source.ver: src/contrib/lol_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/lol_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/lol_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/lol_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/lol_1.14.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lol/inst/doc/lol.R Package: LPE Version: 1.40.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 3b735f7ab653b98171a9bc4ca59ad145 NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ source.ver: src/contrib/LPE_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LPE_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LPE_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LPE_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LPE_1.40.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray Package: LPEadj Version: 1.26.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 9b607c088b7ecd2d9987b1592222892f NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie source.ver: src/contrib/LPEadj_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LPEadj_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LPEadj_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LPEadj_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LPEadj_1.26.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R Package: lpNet Version: 1.6.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: 3d8d7605fab42837bf7be3d8c1ffbecf NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet takes perturbation data as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: Network Author: Bettina Knapp, Johanna Mazur, Lars Kaderali Maintainer: Bettina Knapp source.ver: src/contrib/lpNet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/lpNet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/lpNet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/lpNet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/lpNet_1.6.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R Package: lumi Version: 2.18.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, Biobase (>= 2.5.5), lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 8aad038145005905c77abd327cd02d60 NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Pan Du source.ver: src/contrib/lumi_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/lumi_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/lumi_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/lumi_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/lumi_2.18.0.tgz vignettes: vignettes/lumi/inst/doc/IlluminaAnnotation.pdf, vignettes/lumi/inst/doc/lumi_VST_evaluation.pdf, vignettes/lumi/inst/doc/lumi.pdf, vignettes/lumi/inst/doc/methylationAnalysis.pdf vignetteTitles: Resolve the inconsistency of Illumina identifiers through nuID, Evaluation of VST algorithm in lumi package, Using lumi A package processing Illumina Microarray, Analyze Illumina Infinium methylation microarray data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lumi/inst/doc/IlluminaAnnotation.R, vignettes/lumi/inst/doc/lumi_VST_evaluation.R, vignettes/lumi/inst/doc/lumi.R, vignettes/lumi/inst/doc/methylationAnalysis.R dependsOnMe: arrayMvout, wateRmelon importsMe: ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, methylumi, tigre Package: LVSmiRNA Version: 1.16.0 Depends: R (>= 3.1.0), methods, splines Imports: BiocGenerics, stats4, graphics, stats, utils, MASS, Biobase, quantreg, limma, affy, SparseM, vsn Enhances: parallel,snow, Rmpi License: GPL-2 Archs: i386, x64 MD5sum: 503b24e4829217b86c857feb54ea2ba1 NeedsCompilation: yes Title: LVS normalization for Agilent miRNA data Description: Normalization of Agilent miRNA arrays. biocViews: Microarray,AgilentChip,OneChannel,Preprocessing Author: Stefano Calza, Suo Chen, Yudi Pawitan Maintainer: Stefano Calza source.ver: src/contrib/LVSmiRNA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/LVSmiRNA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/LVSmiRNA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/LVSmiRNA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/LVSmiRNA_1.16.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf vignetteTitles: LVSmiRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R Package: M3D Version: 1.1.4 Depends: R (>= 3.0.0) Imports: GenomicRanges, IRanges, BiSeq Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: Artistic License 2.0 MD5sum: a690deaa5b72faa0283a4a3d3c4d8b5a NeedsCompilation: no Title: Identifies differentially methylated regions across testing groups. Description: This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maximum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles. biocViews: DNAMethylation, DifferentialMethylation, Coverage, CpGIsland Author: Tom Mayo Maintainer: Tom Mayo VignetteBuilder: knitr source.ver: src/contrib/M3D_1.1.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/M3D_1.1.4.zip win64.binary.ver: bin/windows64/contrib/3.1/M3D_1.1.4.zip mac.binary.ver: bin/macosx/contrib/3.1/M3D_1.1.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/M3D_1.1.4.tgz vignettes: vignettes/M3D/inst/doc/M3D_vignette.pdf vignetteTitles: An Introduction to the M$^3$D method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3D/inst/doc/M3D_vignette.R Package: maanova Version: 1.36.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 3d007aeea2be7cb63ea176d1de18edee NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill source.ver: src/contrib/maanova_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maanova_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maanova_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maanova_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maanova_1.36.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maanova/inst/doc/maanova.R Package: macat Version: 1.40.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 9be2ac188acba29011df3fde5266b69c NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling source.ver: src/contrib/macat_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/macat_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/macat_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/macat_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/macat_1.40.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R Package: maCorrPlot Version: 1.36.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: b26f5d8aff60f67e88c44e52625ed2c2 NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 source.ver: src/contrib/maCorrPlot_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maCorrPlot_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maCorrPlot_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maCorrPlot_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maCorrPlot_1.36.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R Package: made4 Version: 1.40.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 4f3e207d8eed51349b04cb0d0ade9c10 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include between group analysis and coinertia analysis. It contains functions that require ADE4. biocViews: Clustering, Classification, MultipleComparison Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ source.ver: src/contrib/made4_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/made4_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/made4_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/made4_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/made4_1.40.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R dependsOnMe: bgafun importsMe: omicade4 Package: maigesPack Version: 1.30.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) Archs: i386, x64 MD5sum: 391d703573d4898e6f5a4b5f760ab70c NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ source.ver: src/contrib/maigesPack_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maigesPack_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maigesPack_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maigesPack_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maigesPack_1.30.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R Package: MAIT Version: 1.0.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Enhances: rgl License: GPL-2 MD5sum: 42403dcd83d5e53212568e37223972a4 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Francesc Fernandez-Albert source.ver: src/contrib/MAIT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MAIT_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MAIT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MAIT_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MAIT_1.0.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: MAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R Package: makecdfenv Version: 1.42.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 3221716adfd0403c545a9e136fbcabcd NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald source.ver: src/contrib/makecdfenv_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/makecdfenv_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/makecdfenv_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/makecdfenv_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/makecdfenv_1.42.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs Package: MANOR Version: 1.38.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: ed62dc56344f9df30d52b1533a704f19 NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation Author: Pierre Neuvial , Philippe Hupe Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html source.ver: src/contrib/MANOR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MANOR_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MANOR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MANOR_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MANOR_1.38.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R Package: manta Version: 1.12.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: cd0fd668f7a3614a574e83c17f5e20d4 NeedsCompilation: no Title: Microbial Assemblage Normalized Transcript Analysis Description: Tools for robust comparative metatranscriptomics. biocViews: DifferentialExpression, RNASeq, Genetics, GeneExpression, Sequencing, QualityControl, DataImport, Visualization Author: Ginger Armbrust, Adrian Marchetti Maintainer: Chris Berthiaume , Adrian Marchetti URL: http://manta.ocean.washington.edu/ source.ver: src/contrib/manta_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/manta_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/manta_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/manta_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/manta_1.12.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/manta/inst/doc/manta.R Package: MantelCorr Version: 1.36.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 82adc116ebba7cebe8272456b2bf9dda NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer source.ver: src/contrib/MantelCorr_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MantelCorr_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MantelCorr_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MantelCorr_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MantelCorr_1.36.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R Package: maPredictDSC Version: 1.4.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 0944e4f30e46c9bce18601141b681e24 NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC source.ver: src/contrib/maPredictDSC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maPredictDSC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maPredictDSC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maPredictDSC_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maPredictDSC_1.4.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R Package: marray Version: 1.44.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 363d3e388932e6bfc2f6ee48db57080e NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ source.ver: src/contrib/marray_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/marray_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/marray_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/marray_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/marray_1.44.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, FEM, maigesPack, MineICA, nnNorm, OLIN, stepNorm, TurboNorm importsMe: arrayQuality, ChAMP, methylPipe, nnNorm, OLIN, OLINgui, piano, plrs, sigaR, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz Package: maSigPro Version: 1.38.0 Depends: R (>= 2.3.1), stats, Biobase, MASS Imports: Biobase, graphics, grDevices, limma, Mfuzz, stats, utils, MASS License: GPL (>= 2) MD5sum: 5608f23417018680ea365eff74bdb607 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Microarray Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray experiments. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ source.ver: src/contrib/maSigPro_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maSigPro_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maSigPro_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maSigPro_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maSigPro_1.38.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maSigPro/inst/doc/maSigPro.R suggestsMe: oneChannelGUI Package: maskBAD Version: 1.10.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe License: GPL version 2 or newer MD5sum: f640a60148c117feccc632eb3f82774a NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann source.ver: src/contrib/maskBAD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/maskBAD_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/maskBAD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/maskBAD_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/maskBAD_1.10.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R Package: MassArray Version: 1.18.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: b1645f0082977835f508eac0b2433be9 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson source.ver: src/contrib/MassArray_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MassArray_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MassArray_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MassArray_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MassArray_1.18.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R Package: massiR Version: 1.2.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: f5bdef27648a996b71b2707e27c45acf NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry source.ver: src/contrib/massiR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/massiR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/massiR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/massiR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/massiR_1.2.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R Package: MassSpecWavelet Version: 1.32.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: bad3d2a70208d751f11a7ea6aa1a56a1 NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du source.ver: src/contrib/MassSpecWavelet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MassSpecWavelet_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MassSpecWavelet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MassSpecWavelet_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MassSpecWavelet_1.32.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq suggestsMe: xcms Package: matchBox Version: 1.8.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: fd6da25e5ef350a2a71b82f2dcd6197b NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta source.ver: src/contrib/matchBox_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/matchBox_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/matchBox_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/matchBox_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/matchBox_1.8.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R Package: MBAmethyl Version: 1.0.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: e4c247f7f82f36e6f3d8c219561d4299 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang source.ver: src/contrib/MBAmethyl_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MBAmethyl_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MBAmethyl_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MBAmethyl_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MBAmethyl_1.0.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R Package: MBASED Version: 1.0.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges Suggests: BiocStyle License: Artistic-2.0 MD5sum: 7cc24f36e6ade9d8e639101d6a3f5ffd NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba source.ver: src/contrib/MBASED_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MBASED_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MBASED_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MBASED_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MBASED_1.0.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R Package: MBCB Version: 1.20.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: c0a1a4908c2d321700ca5cdd30540d54 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu source.ver: src/contrib/MBCB_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MBCB_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MBCB_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MBCB_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MBCB_1.20.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R Package: mBPCR Version: 1.20.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: 9576b783df0959ea32740ac261a37262 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: Estimates the DNA copy number profile using mBPCR to detect regions with copy number changes biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR source.ver: src/contrib/mBPCR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mBPCR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mBPCR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mBPCR_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mBPCR_1.20.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R Package: mcaGUI Version: 1.14.0 Depends: lattice, MASS, proto, foreign, gWidgets(>= 0.0-36), gWidgetsRGtk2(>= 0.0-53), OTUbase, vegan, bpca Enhances: iplots, reshape, ggplot2, cairoDevice, OTUbase License: GPL (>= 2) MD5sum: 46ccbe651786ea38639085ae099aac99 NeedsCompilation: no Title: Microbial Community Analysis GUI Description: Microbial community analysis GUI for R using gWidgets. biocViews: GUI, Visualization, Clustering, Sequencing Author: Wade K. Copeland, Vandhana Krishnan, Daniel Beck, Matt Settles, James Foster, Kyu-Chul Cho, Mitch Day, Roxana Hickey, Ursel M.E. Schutte, Xia Zhou, Chris Williams, Larry J. Forney, Zaid Abdo, Poor Man's GUI (PMG) base code by John Verzani with contributions by Yvonnick Noel Maintainer: Wade K. Copeland URL: http://www.ibest.uidaho.edu/ibest/index.php source.ver: src/contrib/mcaGUI_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mcaGUI_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mcaGUI_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mcaGUI_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mcaGUI_1.14.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCRestimate Version: 2.22.0 Depends: R (>= 2.7.2), golubEsets (>= 1.4.6) Imports: e1071 (>= 1.5-12), pamr (>= 1.22), randomForest (>= 3.9-6), RColorBrewer (>= 0.1-3), Biobase (>= 2.5.5), graphics, grDevices, stats, utils Suggests: xtable (>= 1.2-1), ROC (>= 1.8.0), genefilter (>= 1.12.0), gpls (>= 1.6.0) License: GPL (>= 2) MD5sum: bb7e945c001b55f797ecfed021c770e2 NeedsCompilation: no Title: Misclassification error estimation with cross-validation Description: This package includes a function for combining preprocessing and classification methods to calculate misclassification errors biocViews: Classification Author: Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth Maintainer: Marc Johannes source.ver: src/contrib/MCRestimate_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MCRestimate_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MCRestimate_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MCRestimate_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MCRestimate_2.22.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCRestimate/inst/doc/UsingMCRestimate.R Package: mdqc Version: 1.28.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 70e1010f7737ac996912124f13a719f2 NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue source.ver: src/contrib/mdqc_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mdqc_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mdqc_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mdqc_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mdqc_1.28.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout Package: MeasurementError.cor Version: 1.38.0 License: LGPL MD5sum: 065e23bc958cf8a94d186e065d4448a2 NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding source.ver: src/contrib/MeasurementError.cor_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MeasurementError.cor_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MeasurementError.cor_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MeasurementError.cor_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MeasurementError.cor_1.38.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MEDIPS Version: 1.16.0 Depends: R (>= 3.0), BSgenome, DNAcopy Imports: Biostrings, BSgenome, Rsamtools, graphics, gtools, IRanges, methods, stats, utils, GenomicRanges, edgeR, GenomicFeatures, DNAcopy, biomaRt, rtracklayer Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg19, MEDIPSData License: GPL (>=2) MD5sum: ded766ce157d564042bc2c86975d8ad5 NeedsCompilation: no Title: (MeD)IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples as well as saturation and correlation analyses. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich Maintainer: Lukas Chavez source.ver: src/contrib/MEDIPS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MEDIPS_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MEDIPS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MEDIPS_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MEDIPS_1.16.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R Package: MEDME Version: 1.26.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) Archs: i386, x64 MD5sum: f970cee707ccd49cbd4fa2437efda394 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola source.ver: src/contrib/MEDME_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MEDME_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MEDME_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MEDME_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MEDME_1.26.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R Package: MEIGOR Version: 1.0.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: 0104a0aa1c9bc617dc08997f6454df4f NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose Egea source.ver: src/contrib/MEIGOR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MEIGOR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MEIGOR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MEIGOR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MEIGOR_1.0.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R Package: MergeMaid Version: 2.38.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: 09621c94ca8c9e4a889bf74b7aa1c0f5 NeedsCompilation: no Title: Merge Maid Description: The functions in this R extension are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be 'list','matrix', or 'ExpressionSet'. The main function is 'mergeExprs' which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function 'intcor' calculate the correlation coefficients. Other functions use the output from 'modelOutcome' to graphically display the results and cross-validate associations of gene expression data with survival. biocViews: Microarray, DifferentialExpression, Visualization Author: Xiaogang Zhong Leslie Cope Elizabeth Garrett Giovanni Parmigiani Maintainer: Xiaogang Zhong URL: http://astor.som.jhmi.edu/MergeMaid source.ver: src/contrib/MergeMaid_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MergeMaid_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MergeMaid_2.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MergeMaid_2.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MergeMaid_2.38.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MergeMaid/inst/doc/MergeMaid.R importsMe: metaArray, XDE suggestsMe: oneChannelGUI Package: MeSHDbi Version: 1.2.7 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.16.10), RSQLite, Biobase Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 28ad378a46da8080f1769ad14e2120c6 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (org.MeSH.XXX.db). loadMeSHDbiPkg import sqlite file and generate org.MeSH.XXX.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki source.ver: src/contrib/MeSHDbi_1.2.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/MeSHDbi_1.2.7.zip win64.binary.ver: bin/windows64/contrib/3.1/MeSHDbi_1.2.7.zip mac.binary.ver: bin/macosx/contrib/3.1/MeSHDbi_1.2.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MeSHDbi_1.2.7.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeSHDbi/inst/doc/MeSHDbi.R dependsOnMe: meshr Package: meshr Version: 1.2.7 Depends: R (>= 3.0.1), fdrtool, Category, BiocGenerics, methods, cummeRbund, org.Hs.eg.db, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, org.MeSH.Hsa.db, org.MeSH.Aca.db, org.MeSH.Atu.K84.db, org.MeSH.Bsu.168.db, org.MeSH.Syn.db, S4Vectors License: Artistic-2.0 MD5sum: 159571b02e67db3ebe1e4fe513ffa865 NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons Author: Itoshi Nikaido, Koki Tsuyuzaki, Gota Morota Maintainer: Koki Tsuyuzaki source.ver: src/contrib/meshr_1.2.7.tar.gz win.binary.ver: bin/windows/contrib/3.1/meshr_1.2.6.zip win64.binary.ver: bin/windows64/contrib/3.1/meshr_1.2.6.zip mac.binary.ver: bin/macosx/contrib/3.1/meshr_1.2.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/meshr_1.2.6.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R Package: messina Version: 1.2.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) Archs: i386, x64 MD5sum: 6f245aa6d0a10edb9c526ed8df7f868b NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems. Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr source.ver: src/contrib/messina_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/messina_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/messina_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/messina_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/messina_1.2.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R Package: metaArray Version: 1.44.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: 829ac47d99c95976e46f8ef5d0130f8c NeedsCompilation: yes Title: Integration of Microarray Data for Meta-analysis Description: 1) Data transformation for meta-analysis of microarray Data: Transformation of gene expression data to signed probability scale (MCMC/EM methods) 2) Combined differential expression on raw scale: Weighted Z-score after stabilizing mean-variance relation within platform biocViews: Microarray, DifferentialExpression Author: Debashis Ghosh Hyungwon Choi Maintainer: Hyungwon Choi source.ver: src/contrib/metaArray_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metaArray_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metaArray_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metaArray_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metaArray_1.44.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaArray/inst/doc/metaArray.R suggestsMe: oneChannelGUI Package: Metab Version: 1.0.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: c6d03e46e6785e710f5022bd55bbcff8 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/Metab_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Metab_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Metab_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Metab_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Metab_1.0.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R Package: metabomxtr Version: 1.0.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest Suggests: xtable License: GPL-2 MD5sum: e7267b8f4d2d2dc8f5a908e5b8c6ad5c NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions. Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski source.ver: src/contrib/metabomxtr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metabomxtr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metabomxtr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metabomxtr_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metabomxtr_1.0.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf vignetteTitles: metabomxtr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R Package: metagene Version: 1.0.0 Depends: rtracklayer, GenomicRanges, GenomicAlignments, ggplot2 Suggests: RUnit, BiocGenerics, knitr Enhances: parallel, biomaRt License: Artistic-2.0 MD5sum: e10f2cb3bba2d6cadfa49313378292cd NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Pre-calculated features (such as transcription start sites of protein coding gene or enhancer) are available. Bam files are used to increase the resolution. Multiple combination of group of features and or group of bam files can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr source.ver: src/contrib/metagene_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metagene_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metagene_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metagene_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metagene_1.0.0.tgz vignettes: vignettes/metagene/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene/inst/doc/metagene.R htmlDocs: vignettes/metagene/inst/doc/metagene.html htmlTitles: "A package to produce Metafeature plots" Package: metagenomeSeq Version: 1.8.3 Depends: R(>= 3.0), Biobase, limma, methods, interactiveDisplay(>= 1.3.24), RColorBrewer Imports: parallel, matrixStats, gplots Suggests: annotate, BiocGenerics, biom, knitr, gss, RUnit, vegan License: Artistic-2.0 MD5sum: 27b5adfaca797139cbac92834815ac89 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: DifferentialExpression, Metagenomics, Visualization Author: Joseph Nathaniel Paulson, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: http://cbcb.umd.edu/software/metagenomeSeq VignetteBuilder: knitr source.ver: src/contrib/metagenomeSeq_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/metagenomeSeq_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.1/metagenomeSeq_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.1/metagenomeSeq_1.8.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metagenomeSeq_1.8.3.tgz vignettes: vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R suggestsMe: interactiveDisplay Package: metahdep Version: 1.24.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: 682715d174cb7bd522480a8fe8771f9f NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens source.ver: src/contrib/metahdep_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metahdep_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metahdep_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metahdep_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metahdep_1.24.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R Package: metaMS Version: 1.2.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 5d62c134a5acee4ec21c4a3236704f9b NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: MassSpectrometry, Metabolomics Author: Ron Wehrens [aut, cre] (author of GC-MS part), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline) Maintainer: Ron Wehrens source.ver: src/contrib/metaMS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metaMS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metaMS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metaMS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metaMS_1.2.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R Package: metaSeq Version: 1.6.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 84e786284f601862f60f4e9dc5d88ee6 NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki source.ver: src/contrib/metaSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/metaSeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/metaSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/metaSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metaSeq_1.6.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R Package: metaseqR Version: 1.4.13 Depends: R (>= 2.13.0), EDASeq, DESeq, limma, qvalue Imports: edgeR, NOISeq, baySeq, NBPSeq, biomaRt, utils, gplots, corrplot, vsn, brew, rjson, log4r Suggests: BiocGenerics, GenomicRanges, rtracklayer, Rsamtools, MADAM, survcomp, VennDiagram, knitr, zoo, RUnit, BiocInstaller, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: 51b98ebd7954993ac7279a79d9ffae46 NeedsCompilation: no Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms. Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr source.ver: src/contrib/metaseqR_1.4.13.tar.gz win.binary.ver: bin/windows/contrib/3.1/metaseqR_1.4.13.zip win64.binary.ver: bin/windows64/contrib/3.1/metaseqR_1.4.13.zip mac.binary.ver: bin/macosx/contrib/3.1/metaseqR_1.4.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/metaseqR_1.4.13.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR/inst/doc/metaseqr-pdf.R Package: methVisual Version: 1.18.0 Depends: R (>= 2.11.0), Biostrings(>= 2.4.8), plotrix,gsubfn, grid,sqldf Imports: Biostrings, ca, graphics, grDevices, grid, gridBase, IRanges, stats, utils License: GPL (>= 2) MD5sum: 95e0d3e2d6630881238628cde908b618 NeedsCompilation: no Title: Methods for visualization and statistics on DNA methylation data Description: The package 'methVisual' allows the visualization of DNA methylation data after bisulfite sequencing. biocViews: DNAMethylation, Clustering, Classification Author: A. Zackay, C. Steinhoff Maintainer: Arie Zackay source.ver: src/contrib/methVisual_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/methVisual_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/methVisual_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/methVisual_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/methVisual_1.18.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methVisual/inst/doc/methVisual.R Package: methyAnalysis Version: 1.8.0 Depends: R (>= 2.10), grid, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.5.5), org.Hs.eg.db Imports: lumi, methylumi, Gviz, genoset, GenomicRanges, IRanges, rtracklayer, GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 87b82bb143d88f8920a22fc52277b4f4 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A new class is defined to keep the chromosome location information together with the data. The current version of the package mainly focus on analyzing the Illumina Infinium methylation array data, but most methods can be generalized to other methylation array or sequencing data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Pan Du source.ver: src/contrib/methyAnalysis_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/methyAnalysis_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/methyAnalysis_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/methyAnalysis_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/methyAnalysis_1.8.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi Package: MethylAid Version: 1.0.2 Depends: R (>= 3.0) Imports: methods, matrixStats, BiocParallel, shiny, ggplot2, RColorBrewer, minfi, IlluminaHumanMethylation450kmanifest, FDb.InfiniumMethylation.hg19 Suggests: BiocStyle, RUnit, BiocGenerics, minfiData, knitr License: GPL (>= 2) MD5sum: 3eda0898908ff1b7763f641742e7ec69 NeedsCompilation: no Title: Visual and interactive quality control of large Illumina 450k data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, Visualization, GUI Author: Maarten van Iterson, Elmar. Tobi, Roderick Slieker, Wouter den Hollander, Rene Luijk and Bas Heijmans Maintainer: M. van Iterson URL: http://shiny.bioexp.nl/MethylAid VignetteBuilder: knitr source.ver: src/contrib/MethylAid_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/MethylAid_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/MethylAid_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/MethylAid_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MethylAid_1.0.2.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and interactive quality control of large Illumina 450k data sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R Package: MethylMix Version: 1.0.0 Depends: R (>= 3.1.1) Imports: foreach,parallel,doParallel,RColorBrewer,optimx,RPMM Suggests: BiocStyle License: GPL-2 MD5sum: 0cef8ea293987e57ae3bb93677d942f2 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes. Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray, DifferentialExpression, Pathways, Network Author: Olivier Gevaert Maintainer: Olivier Gevaert source.ver: src/contrib/MethylMix_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MethylMix_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MethylMix_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MethylMix_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MethylMix_1.0.0.tgz vignettes: vignettes/MethylMix/inst/doc/MethylMix.pdf vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/MethylMix.R Package: methylMnM Version: 1.4.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: 08def01fbc73b29c2de14e1993953a75 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou source.ver: src/contrib/methylMnM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/methylMnM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/methylMnM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/methylMnM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/methylMnM_1.4.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R Package: methylPipe Version: 1.0.5 Depends: R (>= 3.1.1), methods, grDevices, graphics, stats, utils, GenomicRanges, Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: i386, x64 MD5sum: b65e48234860b02403debb4f63879ea4 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/methylPipe_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/methylPipe_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/methylPipe_1.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/methylPipe_1.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/methylPipe_1.0.5.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R importsMe: compEpiTools Package: MethylSeekR Version: 1.6.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: e4f01d25a38569887a2ca851e69d2dcb NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger source.ver: src/contrib/MethylSeekR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MethylSeekR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MethylSeekR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MethylSeekR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MethylSeekR_1.6.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe Package: methylumi Version: 2.12.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, IlluminaHumanMethylation27k.db (>= 1.4.4), IlluminaHumanMethylation450k.db, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, FDb.InfiniumMethylation.hg19, TCGAMethylation450k, TxDb.Hsapiens.UCSC.hg19.knownGene, IlluminaHumanMethylation450kanno.ilmn12.hg19, Homo.sapiens, knitr License: GPL-2 MD5sum: 3843b17840193eb131bffe28474f9e5c NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr source.ver: src/contrib/methylumi_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/methylumi_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/methylumi_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/methylumi_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/methylumi_2.12.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: wateRmelon importsMe: asmn, ffpe, lumi, methyAnalysis, missMethyl Package: Mfuzz Version: 2.26.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: fea44c6c4c27122a47c3883b2abc731e NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://itb.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/ source.ver: src/contrib/Mfuzz_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Mfuzz_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Mfuzz_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Mfuzz_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Mfuzz_2.26.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle importsMe: maSigPro Package: MGFM Version: 1.0.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 91591ebc6613d176b52027940b217c76 NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani source.ver: src/contrib/MGFM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MGFM_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MGFM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MGFM_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MGFM_1.0.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R Package: mgsa Version: 1.14.2 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 8f856dc9792725941f2ac2060b7afa97 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer source.ver: src/contrib/mgsa_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/mgsa_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.1/mgsa_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.1/mgsa_1.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mgsa_1.14.2.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R suggestsMe: gCMAP Package: MiChip Version: 1.20.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 70e8359be6132cacd531f4d26881e264 NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake source.ver: src/contrib/MiChip_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MiChip_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MiChip_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MiChip_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MiChip_1.20.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R Package: microRNA Version: 1.24.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) Suggests: Biostrings (>= 2.11.32) Enhances: Rlibstree License: Artistic-2.0 MD5sum: af58e28a8da35b650c236fe80c3b061f NeedsCompilation: no Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" source.ver: src/contrib/microRNA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/microRNA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/microRNA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/microRNA_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/microRNA_1.24.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIMOSA Version: 1.2.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 482f0c37040454591ba0c2235b0ea342 NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr source.ver: src/contrib/MIMOSA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MIMOSA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MIMOSA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MIMOSA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MIMOSA_1.2.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R Package: MineICA Version: 1.6.0 Depends: R (>= 2.10), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE, methods Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX Enhances: doMC License: GPL-2 MD5sum: c0d3f9f0795093eae503b19297fe19fb NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton source.ver: src/contrib/MineICA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MineICA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MineICA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MineICA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MineICA_1.6.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R Package: minet Version: 3.24.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 65647589971faf751f87add69ed501b7 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com source.ver: src/contrib/minet_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/minet_3.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/minet_3.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/minet_3.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/minet_3.24.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: RTN suggestsMe: CNORfeeder, predictionet Package: minfi Version: 1.12.0 Depends: methods, BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), lattice, GenomicRanges, Biostrings, utils, bumphunter (>= 1.1.9) Imports: S4Vectors, IRanges, beanplot, RColorBrewer, nor1mix, siggenes, limma, preprocessCore, illuminaio, matrixStats, mclust, genefilter, nlme, reshape, MASS, quadprog Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, minfiData (>= 0.4.1), FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest License: Artistic-2.0 MD5sum: 1b7aa5897cb539c4f83c8d3c0efb35f4 NeedsCompilation: no Title: Analyze Illumina's 450k methylation arrays Description: Tools for analyzing and visualizing Illumina's 450k array data biocViews: DNAMethylation, Microarray, TwoChannel, DataImport, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Ayree [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/minfi source.ver: src/contrib/minfi_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/minfi_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/minfi_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/minfi_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/minfi_1.12.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.pdf vignetteTitles: Minfi Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: ChAMP, CopyNumber450k, DMRcate, methylumi, shinyMethyl importsMe: MethylAid, methylumi, missMethyl, quantro Package: MinimumDistance Version: 1.10.2 Depends: R (>= 3.01) Imports: oligoClasses, S4Vectors, VanillaICE (>= 1.28.1), Biobase, DNAcopy, BiocGenerics, ff, foreach, matrixStats, IRanges, lattice, GenomicRanges (>= 1.18.1), GenomeInfoDb, data.table, grid Suggests: human610quadv1bCrlmm (>= 1.0.3), SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: a796c559b37ceefc0a88bbfad881d714 NeedsCompilation: no Title: A package for de novo CNV detection in case-parent trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert B Scharpf source.ver: src/contrib/MinimumDistance_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/MinimumDistance_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.1/MinimumDistance_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.1/MinimumDistance_1.10.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MinimumDistance_1.10.2.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R Package: MiPP Version: 1.38.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 15e6339aac1f6058c7166bfb4297c250 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ source.ver: src/contrib/MiPP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MiPP_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MiPP_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MiPP_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MiPP_1.38.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiPP/inst/doc/MiPP.R Package: MiRaGE Version: 1.8.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: AnnotationDbi, BiocGenerics Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: c1b9c9aaf25645e5637802f2fc87017c NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi source.ver: src/contrib/MiRaGE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MiRaGE_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MiRaGE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MiRaGE_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MiRaGE_1.8.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R Package: miRNApath Version: 1.26.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: a20713a56147133f2c4223e600240253 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward source.ver: src/contrib/miRNApath_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/miRNApath_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/miRNApath_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/miRNApath_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/miRNApath_1.26.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R Package: miRNAtap Version: 1.0.0 Depends: R (>= 3.0.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db License: GPL-2 MD5sum: 953538d3ffaaf0b2f7cd9a48f6357f36 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: Maciej Pajak source.ver: src/contrib/miRNAtap_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/miRNAtap_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/miRNAtap_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/miRNAtap_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/miRNAtap_1.0.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R Package: Mirsynergy Version: 1.2.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 59271da7a898b118d71e70a906f69976 NeedsCompilation: no Title: Mirsynergy Description: Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion. biocViews: Clustering Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/Mirsynergy.html VignetteBuilder: knitr source.ver: src/contrib/Mirsynergy_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Mirsynergy_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Mirsynergy_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Mirsynergy_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Mirsynergy_1.2.0.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mirsynergy/inst/doc/Mirsynergy.R Package: missMethyl Version: 1.0.0 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod Suggests: minfiData, BiocStyle, edgeR, tweeDEseqCountData License: GPL-2 MD5sum: da1d3b19e3d6bfb6fdadd451c993077b NeedsCompilation: no Title: Analysis of methylation array data Description: Normalisation and testing for differential variability for data from Illumina's Infinium HumanMethylation450 array. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic source.ver: src/contrib/missMethyl_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/missMethyl_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/missMethyl_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/missMethyl_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/missMethyl_1.0.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.pdf vignetteTitles: missMethyl: analysing data from Illumina's HumanMethylation450 array hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R Package: mitoODE Version: 1.4.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: c74f2a705edfcff353dec5105080defe NeedsCompilation: yes Title: Implementation of the differential equation model described in "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" Description: The package contains the methods to fit a cell-cycle model on cell count data and the code to reproduce the results shown in our paper "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" by Pau, G., Walter, T., Neumann, B., Heriche, J.-K., Ellenberg, J., & Huber, W., BMC Bioinformatics (2013), 14(1), 308. doi:10.1186/1471-2105-14-308 biocViews: ExperimentData, TimeCourse, CellBasedAssays, Preprocessing Author: Gregoire Pau Maintainer: Gregoire Pau SystemRequirements: source.ver: src/contrib/mitoODE_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mitoODE_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mitoODE_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mitoODE_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mitoODE_1.4.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoODE/inst/doc/mitoODE-introduction.R Package: MLInterfaces Version: 1.46.0 Depends: R (>= 2.9), Biobase, methods, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, rda, genefilter Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, gbm, mlbench, hgu95av2.db, som, RColorBrewer, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, BiocGenerics Enhances: parallel License: LGPL MD5sum: 8387d050aaaaab1611f39f12ba66c50b NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: Uniform interfaces to machine learning code for data in Bioconductor containers biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey source.ver: src/contrib/MLInterfaces_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MLInterfaces_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MLInterfaces_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MLInterfaces_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MLInterfaces_1.46.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies Package: MLP Version: 1.14.0 Depends: AnnotationDbi, affy, plotrix, gplots, gmodels, gdata, gtools Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, KEGG.db, annotate, Rgraphviz, GOstats, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 6deac9dca1a23fe84009c3c5af8a3da4 NeedsCompilation: no Title: MLP Description: Mean Log P Analysis biocViews: Genetics Author: Nandini Raghavan, Tobias Verbeke, An De Bondt with contributions by Javier Cabrera, Dhammika Amaratunga, Tine Casneuf and Willem Ligtenberg Maintainer: Tobias Verbeke source.ver: src/contrib/MLP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MLP_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MLP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MLP_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MLP_1.14.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R suggestsMe: a4 Package: MLSeq Version: 1.2.0 Depends: R (>= 3.0.0), caret, DESeq2, Biobase, limma, randomForest, edgeR Imports: methods Suggests: knitr, e1071, kernlab, earth, ellipse, fastICA, gam, ipred, klaR, MASS, mda, mgcv, mlbench, nnet, party, pls, pROC, proxy, RANN, spls, affy License: GPL(>=2) MD5sum: b711ac9e85470c1b685475a7e18b2d8b NeedsCompilation: no Title: Machine learning interface for RNA-Seq data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART, to RNA-Seq data. biocViews: Classification, Clustering Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk Maintainer: Gokmen Zararsiz VignetteBuilder: knitr source.ver: src/contrib/MLSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MLSeq_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MLSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MLSeq_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MLSeq_1.2.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: MLSeq hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R Package: MMDiff Version: 1.6.0 Depends: R (>= 2.14.0),GenomicRanges,parallel,DiffBind,GMD,Rsamtools Imports: GenomicRanges,IRanges,Biobase Suggests: MMDiffBamSubset License: Artistic-2.0 MD5sum: 2a025628f59f51f2498919cafd06c5b2 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant difference between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, MultipleComparison Author: Gabriele Schweikert Maintainer: Gabriele Schweikert source.ver: src/contrib/MMDiff_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MMDiff_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MMDiff_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MMDiff_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MMDiff_1.6.0.tgz vignettes: vignettes/MMDiff/inst/doc/MMDiff.pdf vignetteTitles: Analysing ChIP-Seq data with the "MMDiff" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff/inst/doc/MMDiff.R Package: mmnet Version: 1.4.0 Depends: R (>= 2.14), igraph, biom Imports: Biobase, RJSONIO, stringr, reshape2, ggplot2, KEGGREST, plyr, XML, RCurl, flexmix, Matrix, methods, tools Suggests: RCytoscape, graph, knitr License: GPL (>= 2) MD5sum: 783e65b41c07eea8a4f6bd5d1f654fe9 NeedsCompilation: no Title: A metagenomic pipeline for systems biology Description: This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome. biocViews: GraphsAndNetwork, Sequencing, Pathways, Microbiome, SystemsBiology Author: Yang Cao, Fei Li Maintainer: Yang Cao , Fei Li VignetteBuilder: knitr source.ver: src/contrib/mmnet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mmnet_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mmnet_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mmnet_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mmnet_1.4.0.tgz vignettes: vignettes/mmnet/inst/doc/mmnet.pdf vignetteTitles: mmnet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mmnet/inst/doc/mmnet.R Package: MmPalateMiRNA Version: 1.16.0 Depends: R (>= 2.13.0), methods, Biobase, xtable, limma, statmod, lattice, vsn Imports: limma, lattice, Biobase Suggests: GOstats, graph, Category, org.Mm.eg.db, microRNA, targetscan.Mm.eg.db, RSQLite, DBI, AnnotationDbi, clValid, class, cluster, multtest, RColorBrewer, latticeExtra License: GPL-3 MD5sum: 2e8e040e91042ec73d7ac87bd1de4e32 NeedsCompilation: no Title: Murine Palate miRNA Expression Analysis Description: R package compendium for the analysis of murine palate miRNA two-color expression data. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, Clustering, GO, Pathways, ReportWriting, SequenceMatching Author: Guy Brock , Partha Mukhopadhyay , Vasyl Pihur , Robert M. Greene , and M. Michele Pisano Maintainer: Guy Brock source.ver: src/contrib/MmPalateMiRNA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MmPalateMiRNA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MmPalateMiRNA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MmPalateMiRNA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MmPalateMiRNA_1.16.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.R Package: monocle Version: 1.0.0 Depends: R (>= 2.7.0), HSMMSingleCell, Biobase, ggplot2(>= 0.9.3.1), splines, VGAM (>= 0.9-4), igraph(>= 0.7.0), plyr Imports: BiocGenerics, cluster, combinat, fastICA, grid, irlba, matrixStats, methods, parallel, reshape2, stats, utils, limma Suggests: knitr, Hmisc License: Artistic-2.0 MD5sum: 51d4374d6d42720132e7d38a6cd0de8d NeedsCompilation: no Title: Analysis tools for single-cell expression experiments. Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr source.ver: src/contrib/monocle_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/monocle_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/monocle_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/monocle_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/monocle_1.0.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments. hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R Package: MoPS Version: 1.0.0 Imports: Biobase License: GPL-3 MD5sum: 5be3b7e70be8a4c652efea6da2c0ef49 NeedsCompilation: no Title: MoPS - Model-based Periodicity Screening Description: Identification and characterization of periodic fluctuations in time-series data. biocViews: GeneRegulation,Classification,TimeCourse,Regression Author: Philipp Eser, Achim Tresch Maintainer: Philipp Eser source.ver: src/contrib/MoPS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MoPS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MoPS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MoPS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MoPS_1.0.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoPS/inst/doc/MoPS.R Package: mosaics Version: 2.0.1 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 141b1e8ab1da5d13313cbb405bef4186 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS, a statistical framework to analyze one-sample or two-sample ChIP-seq data. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl source.ver: src/contrib/mosaics_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/mosaics_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/mosaics_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/mosaics_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mosaics_2.0.1.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependsOnMe: jmosaics Package: MotifDb Version: 1.8.0 Depends: R (>= 2.15.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer Suggests: RUnit, MotIV, seqLogo License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: a160b031534f12e250c5f4feab9fe39c NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 2000 annotated position frequency matrices from five public source, for multiple organisms biocViews: MotifAnnotation Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/MotifDb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MotifDb_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MotifDb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MotifDb_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MotifDb_1.8.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R importsMe: rTRMui suggestsMe: motifStack, PWMEnrich, rTRM, vtpnet Package: motifRG Version: 1.10.0 Depends: R (>= 2.15), Biostrings (>= 2.26), IRanges, seqLogo, parallel, methods, grid, graphics, BSgenome, XVector, BSgenome.Hsapiens.UCSC.hg19 Imports: Biostrings,IRanges,seqLogo,parallel,methods,grid,graphics,XVector License: Artistic-2.0 MD5sum: abcb7247a7b9150ebff775ca3e6f615a NeedsCompilation: no Title: A package for discriminative motif discovery, designed for high throughput sequencing dataset Description: Tools for discriminative motif discovery using regression methods biocViews: Transcription,MotifDiscovery Author: Zizhen Yao Maintainer: Zizhen Yao source.ver: src/contrib/motifRG_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/motifRG_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/motifRG_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/motifRG_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/motifRG_1.10.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifRG/inst/doc/motifRG.R Package: motifStack Version: 1.10.2 Depends: R (>= 2.15.1), methods, grImport, grid, MotIV, ade4 Imports: XML Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle License: GPL (>= 2) MD5sum: 2b62005a3bdc83c14346a44f8090015a NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/motifStack_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/motifStack_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.1/motifStack_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.1/motifStack_1.10.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/motifStack_1.10.2.tgz vignettes: vignettes/motifStack/inst/doc/motifStack.pdf vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack.R dependsOnMe: dagLogo Package: MotIV Version: 1.22.0 Depends: R (>= 2.10), BiocGenerics (>= 0.1.0) Imports: graphics, grid, methods, IRanges (>= 1.13.5), Biostrings (>= 1.24.0), lattice, rGADEM, stats, utils Suggests: rtracklayer License: GPL-2 Archs: i386, x64 MD5sum: 208756732b56c5b2d7be5d1ccae6ba71 NeedsCompilation: yes Title: Motif Identification and Validation Description: This package makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs. biocViews: Microarray, ChIPchip, ChIPSeq, GenomicSequence, MotifAnnotation Author: Eloi Mercier, Raphael Gottardo Maintainer: Eloi Mercier , Raphael Gottardo source.ver: src/contrib/MotIV_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MotIV_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MotIV_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MotIV_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MotIV_1.22.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MotIV/inst/doc/MotIV.R dependsOnMe: motifStack suggestsMe: MotifDb Package: MPFE Version: 1.0.0 License: GPL (>= 3) MD5sum: 3e6f97e6500ee9aed1f39f522355d020 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data. Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/MPFE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MPFE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MPFE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MPFE_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MPFE_1.0.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R Package: mQTL.NMR Version: 1.0.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: d78726f9dd2d7d0c369b3c26ec3db64e NeedsCompilation: yes Title: Metabolomic Quantitative Trait Locus Mapping for 1H NMR data Description: mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts. biocViews: Cheminformatics Author: Lyamine Hedjazi and Jean-Baptiste Cazier Maintainer: Lyamine Hedjazi URL: http://www.ican-institute.org/tools/ source.ver: src/contrib/mQTL.NMR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mQTL.NMR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mQTL.NMR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mQTL.NMR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mQTL.NMR_1.0.0.tgz vignettes: vignettes/mQTL.NMR/inst/doc/mQTLUse.pdf vignetteTitles: How to use the mQTL.NMR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mQTL.NMR/inst/doc/mQTLUse.R Package: MSGFgui Version: 1.0.2 Depends: mzR Imports: shiny (>= 0.11), mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), xlsx, tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: dd7addba117c5701ef6ee0e7590cd5b6 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/MSGFgui_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/MSGFgui_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/MSGFgui_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/MSGFgui_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MSGFgui_1.0.2.tgz vignettes: vignettes/MSGFgui/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R htmlDocs: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html htmlTitles: "Using MSGFgui" Package: MSGFplus Version: 1.0.5 Depends: methods Imports: mzID Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: d61cb13deba817ec2ac368c0ad5f8117 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using MS-GF+ biocViews: MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr source.ver: src/contrib/MSGFplus_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/MSGFplus_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/MSGFplus_1.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/MSGFplus_1.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MSGFplus_1.0.5.tgz vignettes: vignettes/MSGFplus/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R htmlDocs: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html htmlTitles: "Using MSGFplus" importsMe: MSGFgui Package: msmsEDA Version: 1.4.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: a1cd387b00cd67563fdebfb9f63f3b2a NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori source.ver: src/contrib/msmsEDA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/msmsEDA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/msmsEDA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/msmsEDA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/msmsEDA_1.4.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests Package: msmsTests Version: 1.4.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: bdbff0d556a44392752a15bc5298a644 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font source.ver: src/contrib/msmsTests_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/msmsTests_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/msmsTests_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/msmsTests_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/msmsTests_1.4.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R suggestsMe: MSnID Package: MSnbase Version: 1.14.2 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR, BiocParallel Imports: plyr, IRanges, preprocessCore, vsn, grid, reshape2, stats4, affy, impute, pcaMethods, mzID (>= 1.1.5), MALDIquant (>= 1.9.8), digest, lattice, ggplot2 Suggests: testthat, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.0.7), msdata, roxygen2, rgl License: Artistic-2.0 MD5sum: 820f08fb2d5268b5c8bced8bf3da9d37 NeedsCompilation: no Title: MSnbase: Base Functions and Classes for MS-based Proteomics Description: Basic plotting, data manipulation and processing of MS-based Proteomics data biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake and Sebastian Gibb. Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/MSnbase_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/MSnbase_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.1/MSnbase_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.1/MSnbase_1.14.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MSnbase_1.14.2.tgz vignettes: vignettes/MSnbase/inst/doc/MSnbase-demo.pdf, vignettes/MSnbase/inst/doc/MSnbase-development.pdf, vignettes/MSnbase/inst/doc/MSnbase-io.pdf vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase development, MSnbase IO capabilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/MSnbase-demo.R, vignettes/MSnbase/inst/doc/MSnbase-development.R, vignettes/MSnbase/inst/doc/MSnbase-io.R dependsOnMe: msmsEDA, msmsTests, MSstats, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter importsMe: MSnID, Pbase suggestsMe: isobar, qcmetrics, rpx Package: MSnID Version: 1.0.1 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, reshape2, methods, iterators, data.table, Biobase, mzR Suggests: BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0737695da0cd8ba027f6a904cc22e1bb NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications. Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk source.ver: src/contrib/MSnID_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/MSnID_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/MSnID_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/MSnID_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MSnID_1.0.1.tgz vignettes: vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/msnid_vignette.R Package: MSstats Version: 2.4.0 Depends: R (>= 3.0), Rcpp, MSnbase, reshape Imports: lme4,marray,limma,gplots,ggplot2, preprocessCore License: Artistic-2.0 MD5sum: 0951751fe949a1f43e09cd9ada08d1c5 NeedsCompilation: no Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: MassSpectrometry, Proteomics, Software Author: Meena Choi , Ching-Yun Chang , Olga Vitek Maintainer: Meena Choi source.ver: src/contrib/MSstats_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MSstats_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MSstats_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MSstats_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MSstats_2.4.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.pdf vignetteTitles: Protein quantification in LC-MS,, SRM,, DIA experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R Package: Mulcom Version: 1.16.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: fdad08223ac8e093221bb4c7f0c93e10 NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella source.ver: src/contrib/Mulcom_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Mulcom_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Mulcom_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Mulcom_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Mulcom_1.16.0.tgz vignettes: vignettes/Mulcom/inst/doc/MulcomVignette.pdf vignetteTitles: Mulcom Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mulcom/inst/doc/MulcomVignette.R Package: MultiMed Version: 1.0.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 2c6036ec4cc476e3d2ea803a333ec249 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements permutation method with joint correction for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Joshua N. Sampson Maintainer: Simina M. Boca source.ver: src/contrib/MultiMed_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MultiMed_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MultiMed_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MultiMed_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MultiMed_1.0.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R Package: multiscan Version: 1.26.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: b56ba4fc6b77a382825e57c20b342385 NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker source.ver: src/contrib/multiscan_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/multiscan_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/multiscan_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/multiscan_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/multiscan_1.26.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R Package: multtest Version: 2.22.0 Depends: R (>= 2.10), methods, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: 701df4914167bc0bbeaa18c66c7636a3 NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard source.ver: src/contrib/multtest_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/multtest_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/multtest_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/multtest_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/multtest_2.22.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, aCGH, BicARE, iPAC, KCsmart, LMGene, PREDA, rain, REDseq, SAGx, siggenes, webbioc importsMe: ABarray, aCGH, adSplit, anota, ChIPpeakAnno, GeneSelector, globaltest, IsoGeneGUI, metabomxtr, OCplus, phyloseq, REDseq, RTopper, synapter, webbioc suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GeneSelector, GGtools, GOstats, GSEAlm, maigesPack, MmPalateMiRNA, oneChannelGUI, pcot2, PECA, topGO, xcms Package: MVCClass Version: 1.40.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 84ee543de66b52ad20d48e1c8163c614 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen source.ver: src/contrib/MVCClass_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/MVCClass_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/MVCClass_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/MVCClass_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/MVCClass_1.40.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MVCClass/inst/doc/MVCClass.R dependsOnMe: BioMVCClass Package: mvGST Version: 1.0.0 Depends: R(>= 2.10.0), GO.db, Rgraphviz Imports: gProfileR, stringr, topGO, GOstats, annotate, AnnotationDbi, graph Suggests: hgu133plus2.db, org.Hs.eg.db License: GPL-3 MD5sum: 330ece130fd0b1a865d85b8317033486 NeedsCompilation: no Title: Multivariate and directional gene set testing Description: mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile. biocViews: Microarray, OneChannel, RNASeq, DifferentialExpression, GO, Pathways, GeneSetEnrichment, GraphAndNetwork Author: John R. Stevens and Dennis S. Mecham Maintainer: John R. Stevens source.ver: src/contrib/mvGST_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mvGST_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mvGST_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mvGST_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mvGST_1.0.0.tgz vignettes: vignettes/mvGST/inst/doc/mvGST.pdf vignetteTitles: mvGST Tutorial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mvGST/inst/doc/mvGST.R Package: mygene Version: 1.0.1 Depends: R (>= 3.0.0), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf Suggests: BiocStyle License: Artistic-2.0 MD5sum: 5f133e60245191a9de3d72d3872576c3 NeedsCompilation: no Title: Access MyGene.Info services Description: MyGene.Info provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Chunlei Wu Maintainer: Adam Mark, Chunlei Wu source.ver: src/contrib/mygene_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/mygene_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/mygene_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/mygene_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mygene_1.0.1.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R Package: mzID Version: 1.4.1 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 3b3c5c2e51aa1d3e634bc634d8f65d80 NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: DataImport, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen, Vladislav A Petyuk with contributions from Laurent Gatto and Sebastian Gibb. Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/mzID_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/mzID_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/mzID_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/mzID_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mzID_1.4.1.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR Package: mzR Version: 2.0.0 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase LinkingTo: Rcpp Suggests: msdata, RUnit, faahKO, mzID, BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: a9563c59509c85c1c53e4b74cc4cafe9 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou Maintainer: Bernd Fischer , Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: GNU make, NetCDF, zlib VignetteBuilder: knitr source.ver: src/contrib/mzR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/mzR_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/mzR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/mzR_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/mzR_2.0.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.pdf vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSGFgui, MSnbase, TargetSearch, xcms importsMe: MSnID, Pbase suggestsMe: qcmetrics Package: NarrowPeaks Version: 1.10.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Archs: i386, x64 MD5sum: 2443d61ba6ef96fcc76b5ddc57c1200d NeedsCompilation: yes Title: Shape-based Analysis of Variation in ChIP-Seq using Functional PCA Description: The package applies a functional version of principal component analysis (FPCA) to: (1) Process data in wiggle track format (WIG) commonly produced by ChIP-Seq peak callers by applying FPCA over a set of read-enriched regions (ChIP-Seq peaks). This is done in order to shorten the genomic locations accounting for a given proportion of variation among the enrichment-score profiles. The function 'narrowpeaks' allows splitting and trimming binding sites in close proximity to each other, narrowing down the length of the putative transcription factor binding sites while preserving the information present in the variability of the dataset and capturing major sources of variation. (2) Analyse differential variation between multiple ChIP-Seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. biocViews: Visualization, ChIPSeq, Transcription, Genetics, Sequencing, Sequencing Author: Pedro Madrigal , Pawel Krajewski Maintainer: Pedro Madrigal source.ver: src/contrib/NarrowPeaks_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NarrowPeaks_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NarrowPeaks_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NarrowPeaks_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NarrowPeaks_1.10.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette. Shape-based splitting and trimming ChIP-Seq peaks using functional Principal Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.R Package: ncdfFlow Version: 2.12.0 Depends: R (>= 2.14.0), flowCore, flowViz, RcppArmadillo, BH Imports: Biobase,flowCore,flowViz,methods,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH Suggests: testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 7c1e4404056e226431d9f20e2cbffe2c NeedsCompilation: yes Title: ncdfFlow: A package that provides ncdf based storage for flow cytometry data. Description: Provides netCDF storage based methods and functions for manipulation of flow cytometry data. biocViews: FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: hdf5 (>= 1.8.0) source.ver: src/contrib/ncdfFlow_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ncdfFlow_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ncdfFlow_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ncdfFlow_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ncdfFlow_2.12.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R Package: NCIgraph Version: 1.14.0 Depends: graph, R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCytoscape, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: d1e8de7afcba12373887586f28924438 NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob source.ver: src/contrib/NCIgraph_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NCIgraph_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NCIgraph_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NCIgraph_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NCIgraph_1.14.0.tgz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph Package: neaGUI Version: 1.4.0 Depends: tcltk Imports: hwriter Suggests: AnnotationDbi, org.Hs.eg.db, KEGG.db, GO.db, reactome.db, RUnit, GOstats,hwriter License: GPL-2 MD5sum: 395d45259c5c51805bb8c05342cc276a NeedsCompilation: no Title: An R package to perform the network enrichment analysis (NEA). Description: neaGUI is an easy to use R package developed to perform the network enrichment analysis (NEA) proposed by Alexeyenko et al. (2012). The NEA method extends the overlap statistics in GSEA to network links between genes in the experimental set and those in the functional categories by exploiting biological information in terms of gene interaction network. The neaGUI requires the following R packages: tcltk, KEGG.db, GO.db, reactome.db, org.Hs.eg.db, AnnotationDbi, and hwriter. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Woojoo Lee, Yudi Pawitan Maintainer: Setia Pramana source.ver: src/contrib/neaGUI_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/neaGUI_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/neaGUI_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/neaGUI_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/neaGUI_1.4.0.tgz vignettes: vignettes/neaGUI/inst/doc/neaGUI_vignette.pdf vignetteTitles: neaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/neaGUI/inst/doc/neaGUI_vignette.R importsMe: EnrichmentBrowser Package: nem Version: 2.40.0 Depends: R (>= 2.0), e1071 (>= 1.5), graph (>= 1.24), plotrix, limma, cluster (>= 1.11), statmod, Hmisc, Rgraphviz Imports: boot, e1071, graph, graphics, grDevices, methods, RBGL (>= 1.8.1), RColorBrewer, stats, utils, Rgraphviz Suggests: Biobase (>= 1.10) Enhances: doMC, Rglpk License: GPL (>= 2) Archs: i386, x64 MD5sum: 4f39b15fc56c58322bcbf427060b9491 NeedsCompilation: yes Title: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies Description: The package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input (1.) a set of pathway components, which were perturbed, and (2.) phenotypic readout of these perturbations (e.g. gene expression, protein expression). The output is a directed graph representing the phenotypic hierarchy. biocViews: Microarray, GraphAndNetwork, Pathways, DecisionTree Author: Holger Froehlich, Florian Markowetz, Achim Tresch, Theresa Niederberger, Christian Bender, Matthias Maneck, Claudio Lottaz, Tim Beissbarth Maintainer: Holger Froehlich URL: http://www.bioconductor.org source.ver: src/contrib/nem_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/nem_2.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/nem_2.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/nem_2.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/nem_2.40.0.tgz vignettes: vignettes/nem/inst/doc/nem.pdf vignetteTitles: Nested Effects Models - An example in Drosophila immune response hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nem/inst/doc/nem.R dependsOnMe: lpNet suggestsMe: rBiopaxParser Package: netbiov Version: 1.0.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 63980025c48a274e579c22b49b82d048 NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com source.ver: src/contrib/netbiov_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/netbiov_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/netbiov_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/netbiov_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/netbiov_1.0.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R Package: NetPathMiner Version: 1.2.0 Depends: R (>= 3.0.2), igraph (>= 0.6) Suggests: rBiopaxParser (>= 2.1), RCurl, RCytoscape License: GPL (>= 2) Archs: i386, x64 MD5sum: ec7e3e1615c650e8479a33c6e87c82a1 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) source.ver: src/contrib/NetPathMiner_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NetPathMiner_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NetPathMiner_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NetPathMiner_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NetPathMiner_1.2.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.pdf vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R Package: netresponse Version: 1.16.0 Depends: R (>= 2.15.1), dmt, igraph, infotheo, ggplot2, graph, mclust, methods, minet, parallel, qvalue, RColorBrewer, reshape, Rgraphviz License: GPL (>=2) Archs: i386, x64 MD5sum: e5a3a1561fd3917dfa91f36ba83b0189 NeedsCompilation: yes Title: netresponse: functional network analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse source.ver: src/contrib/netresponse_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/netresponse_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/netresponse_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/netresponse_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/netresponse_1.16.0.tgz vignettes: vignettes/netresponse/inst/doc/netresponse.pdf vignetteTitles: netresponse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netresponse/inst/doc/netresponse.R Package: NetSAM Version: 1.6.0 Depends: R (>= 2.15.1), methods, igraph (>= 0.6-1), seriation (>= 1.0-6), graph (>= 1.34.0) Imports: methods Suggests: RUnit, BiocGenerics License: LGPL MD5sum: a38a8e650bee9e7bc92eba191f43620e NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. biocViews: Visualization, Network Author: Jing Wang Maintainer: Bing Zhang source.ver: src/contrib/NetSAM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NetSAM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NetSAM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NetSAM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NetSAM_1.6.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R Package: networkBMA Version: 1.8.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1) LinkingTo: Rcpp, RcppArmadillo, RcppEigen License: GPL (>= 2) Archs: i386, x64 MD5sum: 0d1edea05328c22d5cf7343f6647f3a3 NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung source.ver: src/contrib/networkBMA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/networkBMA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/networkBMA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/networkBMA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/networkBMA_1.8.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R Package: NGScopy Version: 1.0.0 Depends: R (>= 3.1.0) Imports: methods, parallel, Xmisc (>= 0.2.1), rbamtools (>= 2.6.0), changepoint (>= 1.1.5) Suggests: RUnit, NGScopyData, GenomicRanges License: GPL (>=2) MD5sum: d015999e150210078091360cec62bc83 NeedsCompilation: no Title: NGScopy: Detection of copy number variations in next generation sequencing Description: NGScopy provides a quantitative caller for detecting copy number variations in next generation sequencing (NGS), including whole genome sequencing (WGS), whole exome sequencing (WES) and targeted panel sequencing (TPS). The caller can be parallelized by chromosomes to use multiple processors/cores on one computer. biocViews: CopyNumberVariation, DNASeq, TargetedResequencing, ExomeSeq, WholeGenome, Sequencing Author: Xiaobei Zhao [aut, cre, cph] Maintainer: Xiaobei Zhao source.ver: src/contrib/NGScopy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NGScopy_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NGScopy_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NGScopy_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NGScopy_1.0.0.tgz vignettes: vignettes/NGScopy/inst/doc/NGScopy-vignette.pdf vignetteTitles: NGScopy: Detection of copy number variations in next generation sequencing (User's Guide) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NGScopy/inst/doc/NGScopy-vignette.R Package: nnNorm Version: 2.30.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: cc21b93df93838603a6f5a5e937f1d97 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ source.ver: src/contrib/nnNorm_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/nnNorm_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/nnNorm_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/nnNorm_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/nnNorm_2.30.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R Package: NOISeq Version: 2.8.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1) License: Artistic-2.0 MD5sum: 8457339648104f73012ba81efa11c6f4 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona source.ver: src/contrib/NOISeq_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NOISeq_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NOISeq_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NOISeq_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NOISeq_2.8.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: metaseqR suggestsMe: compcodeR Package: nondetects Version: 1.2.0 Depends: R (>= 3.0.2), Biobase (>= 2.22.0) Imports: utils, methods, HTqPCR (>= 1.16.0) Suggests: BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL (>= 2) MD5sum: 4ddaed4223b6a0912279e3a01245e9d1 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall Maintainer: Matthew N. McCall source.ver: src/contrib/nondetects_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/nondetects_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/nondetects_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/nondetects_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/nondetects_1.2.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.pdf vignetteTitles: nondetects - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R Package: NormqPCR Version: 1.12.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: df2567e537e5284430fd56176b8809b1 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html source.ver: src/contrib/NormqPCR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NormqPCR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NormqPCR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NormqPCR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NormqPCR_1.12.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R Package: npGSEA Version: 1.2.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 35c97ed1bb4973272be9a0f93f3a55ba NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson source.ver: src/contrib/npGSEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/npGSEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/npGSEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/npGSEA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/npGSEA_1.2.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R Package: NTW Version: 1.16.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: c201296cb62e301893adc41ee962dd87 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu source.ver: src/contrib/NTW_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NTW_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NTW_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NTW_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NTW_1.16.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R Package: nucleR Version: 1.14.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Biobase (>= 2.15.1), ShortRead, parallel Imports: methods, BiocGenerics, S4Vectors, IRanges, Biobase, ShortRead, GenomicRanges, stats Enhances: htSeqTools License: LGPL (>= 3) MD5sum: 83e59d80798e53a21a18db0ffe7e18f5 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and Next Generation Sequencing Experiments biocViews: ChIPSeq, Microarray, Sequencing, Genetics Author: Oscar Flores, David Rossell Maintainer: Oscar Flores source.ver: src/contrib/nucleR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/nucleR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/nucleR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/nucleR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/nucleR_1.14.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: Quick analysis of nucleosome positioning experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R Package: nudge Version: 1.32.0 Imports: stats License: GPL-2 MD5sum: d0768d6f5b48d1b0122350dfe12c92e1 NeedsCompilation: no Title: Normal Uniform Differential Gene Expression detection Description: Package for normalizing microarray data in single and multiple replicate experiments and fitting a normal-uniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample) biocViews: Microarray, TwoChannel, DifferentialExpression Author: N. Dean and A. E. Raftery Maintainer: N. Dean source.ver: src/contrib/nudge_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/nudge_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/nudge_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/nudge_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/nudge_1.32.0.tgz vignettes: vignettes/nudge/inst/doc/nudge.vignette.pdf vignetteTitles: nudge Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nudge/inst/doc/nudge.vignette.R Package: NuPoP Version: 1.16.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 2f1947c4e9a1354855bacb7ae00f3aab NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification Author: Ji-Ping Wang ; Liqun Xi Maintainer: Ji-Ping Wang source.ver: src/contrib/NuPoP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/NuPoP_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/NuPoP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/NuPoP_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/NuPoP_1.16.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP-intro.pdf vignetteTitles: An R package for Nucleosome positioning prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP-intro.R Package: occugene Version: 1.26.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 75a816300eadf80a3baca23f05737653 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will source.ver: src/contrib/occugene_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/occugene_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/occugene_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/occugene_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/occugene_1.26.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R Package: OCplus Version: 1.40.0 Depends: R (>= 2.1.0), akima Imports: multtest (>= 1.7.3), graphics, grDevices, stats License: LGPL MD5sum: 796717e845976d9730f37ecd81b4b898 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner source.ver: src/contrib/OCplus_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OCplus_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OCplus_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OCplus_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OCplus_1.40.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R Package: oligo Version: 1.30.0 Depends: R (>= 2.15.0), BiocGenerics (>= 0.3.2), oligoClasses (>= 1.25.4), Biobase (>= 2.17.8), Biostrings (>= 2.25.12) Imports: affyio (>= 1.25.0), affxparser (>= 1.29.11), BiocGenerics (>= 0.3.2), DBI (>= 0.2-5), ff, graphics, methods, preprocessCore (>= 1.19.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: hapmap100kxba, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, RUnit Enhances: ff, doMC, doMPI License: LGPL (>= 2) Archs: i386, x64 MD5sum: 395d57279ed4294f247aa4b0c966f2bd NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays. Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry. Contributors: Ben Bolstad, Vincent Carey, Wolfgang Huber, Harris Jaffee, Jim MacDonald, Matt Settles Maintainer: Benilton Carvalho source.ver: src/contrib/oligo_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/oligo_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/oligo_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/oligo_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/oligo_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, SCAN.UPC, waveTiling importsMe: charm, cn.farms, frma, ITALICS suggestsMe: BiocGenerics, fastseg, frmaTools Package: oligoClasses Version: 1.28.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 1.13.30), GenomicRanges, Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocInstaller, utils, S4Vectors Suggests: RSQLite, hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 8cf2d7c6f6bdff1de2a46be1d3d55c60 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf source.ver: src/contrib/oligoClasses_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/oligoClasses_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/oligoClasses_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/oligoClasses_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/oligoClasses_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, waveTiling importsMe: affycoretools, ArrayTV, charm, frma, ITALICS, MinimumDistance, pdInfoBuilder, SNPchip, VanillaICE suggestsMe: BiocGenerics Package: OLIN Version: 1.44.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 953459694d99c8f6a2105b216ffc8dbd NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN/index.html source.ver: src/contrib/OLIN_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OLIN_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OLIN_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OLIN_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OLIN_1.44.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack Package: OLINgui Version: 1.40.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 2f391a22c4ba5a2ee6ccc3afe4e0e646 NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN/index.html source.ver: src/contrib/OLINgui_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OLINgui_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OLINgui_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OLINgui_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OLINgui_1.40.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R Package: omicade4 Version: 1.6.2 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: d0fbcb6da784a4b5fc8ca36b69cdb695 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: Multiple co-inertia analysis of omics datasets biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng source.ver: src/contrib/omicade4_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/omicade4_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/omicade4_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/omicade4_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/omicade4_1.6.2.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R Package: OmicCircos Version: 1.4.0 Depends: R (>= 2.14.0),methods,GenomicRanges Suggests: knitr License: GPL-2 MD5sum: 8ae6a38423490c3c8e70963a18dc6fa1 NeedsCompilation: no Title: High-quality circular visualization of omic data Description: OmicCircos is an R application and package for generating high-quality circular maps for omic data biocViews: Visualization, StatisticalMethod, Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu VignetteBuilder: knitr source.ver: src/contrib/OmicCircos_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OmicCircos_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OmicCircos_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OmicCircos_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OmicCircos_1.4.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R Package: OncoSimulR Version: 1.0.0 Depends: R (>= 3.1.0) Imports: Rcpp (>= 0.11.1), parallel, data.table, graph, Rgraphviz LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree License: GPL (>= 3) Archs: i386, x64 MD5sum: 68a81a0dc87803358659f3cf358f3bab NeedsCompilation: yes Title: Simulation of cancer progresion with order restrictions Description: Functions for simulating and plotting cancer progression data, including drivers and passengers, and allowing for order restrictions. Simulations use continuous-time models (based on Bozic et al., 2010 and McFarland et al., 2013) and fitness functions account for possible restrictions in the order of accumulation of mutations. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte. Maintainer: Ramon Diaz-Uriarte SystemRequirements: C++11 VignetteBuilder: knitr source.ver: src/contrib/OncoSimulR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OncoSimulR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OncoSimulR_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OncoSimulR_1.0.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.pdf vignetteTitles: OncoSimulR Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R Package: oneChannelGUI Version: 1.32.0 Depends: Biobase, affylmGUI, tkrplot, tkWidgets, IRanges, Rsamtools (>= 1.13.1), Biostrings, siggenes, chimera Suggests: annotate, genefilter, maSigPro, pamr, pdmclass, ChIPpeakAnno, chipseq, BSgenome, Rgraphviz, affy ,annaffy, affyPLM, multtest, ssize, sizepower, RankProd, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, edgeR, metaArray, MergeMaid, biomaRt, GenomeGraphs,AffyCompatible, rtracklayer, Genominator, EDASeq, limma, DESeq, DEXSeq, goseq, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, ragene10sttranscriptcluster.db, GOstats, AnnotationDbi, preprocessCore, baySeq, HuExExonProbesetLocation, MoExExonProbesetLocation, RaExExonProbesetLocation, snow, RmiR, RmiR.Hs.miRNA, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn4, R.utils, cummeRbund License: Artistic-2.0 MD5sum: a332aee6cedc432b35b266eed588cc1b NeedsCompilation: no Title: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops. Description: This package was developed to simplify the use of Bioconductor tools for beginners having limited or no experience in writing R code. This library provides a graphical interface for microarray gene and exon level analysis as well as miRNA/mRNA-seq data analysis. biocViews: Sequencing, RNASeq, Microarray, OneChannel, DataImport, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, GUI, MultipleComparison Author: Raffale A Calogero, Bioinformatics and Genomics Unit, Molecular Biotechnology Center, Torino (Italy) Maintainer: Raffaele A Calogero URL: http://www.bioinformatica.unito.it/oneChannelGUI/ source.ver: src/contrib/oneChannelGUI_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/oneChannelGUI_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/oneChannelGUI_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/oneChannelGUI_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/oneChannelGUI_1.32.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ontoCAT Version: 1.18.0 Depends: rJava, methods License: Apache License 2.0 MD5sum: 4f506bdbf9c66ebd5a1a1a163f3307aa NeedsCompilation: no Title: Ontology traversal and search Description: The ontoCAT R package provides a simple interface to ontologies described in widely used standard formats, stored locally in the filesystem or accessible online. The full version of ontoCAT R package also supports searching for ontology terms across multiple ontologies and in major ontology repositories, as well as a number of advanced ontology navigation functions: www.ontocat.org/wiki/r biocViews: Classification, DataRepresentation Author: Natalja Kurbatova, Tomasz Adamusiak, Pavel Kurnosov, Morris Swertz, Misha Kapushevsky Maintainer: Natalja Kurbatova source.ver: src/contrib/ontoCAT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ontoCAT_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ontoCAT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ontoCAT_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ontoCAT_1.18.0.tgz vignettes: vignettes/ontoCAT/inst/doc/ontoCAT.pdf vignetteTitles: ontoCAT package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ontoCAT/inst/doc/ontoCAT.R suggestsMe: RMassBank Package: openCyto Version: 1.4.0 Depends: flowWorkspace(>= 3.11.10) Imports: methods,Biobase,gtools,flowCore,flowViz,ncdfFlow(>= 2.11.34),flowStats(>= 3.23.7),flowClust,MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 011bf16862f4d7a420e0eae9a0465bf4 NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/openCyto_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/openCyto_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/openCyto_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/openCyto_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/openCyto_1.4.0.tgz vignettes: vignettes/openCyto/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R htmlDocs: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html htmlTitles: "How to write a csv gating template", "An Introduction to the openCyto package" Package: oposSOM Version: 1.1.1 Depends: R (>= 3.0) Imports: som, fastICA, scatterplot3d, pixmap, fdrtool, ape, igraph, KernSmooth, parallel, biomaRt, Biobase License: GPL (>=2) MD5sum: f4f1068c9768c117659e48134b0e32c9 NeedsCompilation: no Title: Comprehensive analysis of transciptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Wirth and Martin Kalcher Maintainer: Henry Wirth URL: http://som.izbi.uni-leipzig.de source.ver: src/contrib/oposSOM_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/oposSOM_1.1.1.zip win64.binary.ver: bin/windows64/contrib/3.1/oposSOM_1.1.1.zip mac.binary.ver: bin/macosx/contrib/3.1/oposSOM_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/oposSOM_1.1.1.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: oposSOM Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R Package: OrderedList Version: 1.38.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: 11f4b748d1641ab0e5585668e2592642 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/index.shtml source.ver: src/contrib/OrderedList_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OrderedList_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OrderedList_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OrderedList_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OrderedList_1.38.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R Package: OrganismDbi Version: 1.8.1 Depends: R (>= 2.14.0), methods, AnnotationDbi (>= 1.16.10), GenomicFeatures (>= 1.17.13) Imports: BiocGenerics, graph, RBGL, AnnotationDbi, stats Suggests: Homo.sapiens, Rattus.norvegicus, RUnit License: Artistic-2.0 MD5sum: 7f3adb6c581c042155d86d7ab83ba8a4 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages. Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Herve Pages, Martin Morgan, Valerie Obenchain Maintainer: Biocore Data Team source.ver: src/contrib/OrganismDbi_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/OrganismDbi_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/OrganismDbi_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/OrganismDbi_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OrganismDbi_1.8.1.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R importsMe: epivizr, ggbio Package: OSAT Version: 1.14.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 145ff5cd2912ce6dc317ab69664de6b5 NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 source.ver: src/contrib/OSAT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OSAT_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OSAT_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OSAT_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OSAT_1.14.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R Package: OTUbase Version: 1.16.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 434a801b28a22a3e90ab70513a6cba27 NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck source.ver: src/contrib/OTUbase_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OTUbase_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OTUbase_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OTUbase_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OTUbase_1.16.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependsOnMe: mcaGUI Package: OutlierD Version: 1.30.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: 585f7e98c43924b34312e8f499514ac5 NeedsCompilation: no Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/OutlierD_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/OutlierD_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/OutlierD_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/OutlierD_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/OutlierD_1.30.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutlierD/inst/doc/OutlierD.R Package: PAA Version: 1.0.0 Depends: R (>= 3.0.3), Rcpp (>= 0.10.6.1) Imports: e1071, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 6b2b66d1e6a7c8fe7d30ef37d9ea3dca NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After pre-processing (background correction, batch filtering, normalization) univariate feature pre-selection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.medizinisches-proteom-center.de/PAA SystemRequirements: C++ software package Random Jungle source.ver: src/contrib/PAA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PAA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PAA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PAA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PAA_1.0.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R Package: PADOG Version: 1.8.0 Depends: R (>= 2.14.0), KEGGdzPathwaysGEO, graphics, limma, AnnotationDbi, Biobase, methods, nlme, GSA,KEGG.db Imports: graphics, limma, hgu133plus2.db, hgu133a.db, KEGG.db, AnnotationDbi, Biobase, methods, nlme Suggests: parallel License: GPL (>= 2) MD5sum: be55e26bca8429eb4fcf77a24b05b73d NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca source.ver: src/contrib/PADOG_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PADOG_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PADOG_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PADOG_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PADOG_1.8.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R Package: paircompviz Version: 1.4.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 956fdeaeb9bc912b5208c5866af907e0 NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda source.ver: src/contrib/paircompviz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/paircompviz_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/paircompviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/paircompviz_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/paircompviz_1.4.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R Package: PAnnBuilder Version: 1.30.1 Depends: R (>= 2.7.0), methods, utils, RSQLite, Biobase (>= 1.17.0), AnnotationDbi (>= 1.3.12) Imports: methods, utils, Biobase, DBI, RSQLite, AnnotationDbi Suggests: org.Hs.ipi.db License: LGPL (>= 2.0) MD5sum: 6016403d44baf1a4ae69f1a3207a3d4a NeedsCompilation: no Title: Protein annotation data package builder Description: Processing annotation data from public data repositories and building protein-centric annotation data packages. biocViews: Annotation, Proteomics Author: Li Hong lihong@sibs.ac.cn Maintainer: Li Hong URL: http://www.biosino.org/PAnnBuilder source.ver: src/contrib/PAnnBuilder_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/PAnnBuilder_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.1/PAnnBuilder_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.1/PAnnBuilder_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PAnnBuilder_1.30.1.tgz vignettes: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.pdf vignetteTitles: Using the PAnnBuilder Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.R Package: panp Version: 1.36.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 007a0f13adaa59b31daf31de27e321af NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren source.ver: src/contrib/panp_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/panp_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/panp_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/panp_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/panp_1.36.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R Package: PANR Version: 1.12.1 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils Suggests: snow, RedeR License: Artistic-2.0 MD5sum: d511b06f8888d1ba9e0d28b0cd29ed9b NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang source.ver: src/contrib/PANR_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/PANR_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/PANR_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/PANR_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PANR_1.12.1.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R suggestsMe: RedeR Package: PAPi Version: 1.6.1 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: 43585adb960274f116ae989de359fc1f NeedsCompilation: no Title: Predict metabolic pathway activity based on metabolomics data Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics. biocViews: MassSpectrometry, Metabolomics Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/PAPi_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/PAPi_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/PAPi_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/PAPi_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PAPi_1.6.1.tgz vignettes: vignettes/PAPi/inst/doc/PAPi.pdf, vignettes/PAPi/inst/doc/PAPiPackage.pdf vignetteTitles: PAPi.pdf, Applying PAPi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAPi/inst/doc/PAPiPackage.R Package: parody Version: 1.24.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: bc7edc197449d10ccc2d86e5fcd65a87 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics biocViews: MultipleComparison Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parody_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/parody_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/parody_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/parody_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/parody_1.24.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout, flowQ Package: pathifier Version: 1.4.0 Imports: R.oo, princurve License: Artistic-1.0 MD5sum: cd7069cf730429d4d777b6386e593501 NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky source.ver: src/contrib/pathifier_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pathifier_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pathifier_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pathifier_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pathifier_1.4.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R Package: PathNet Version: 1.6.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: ae0315c69204e710eb0b06902f35295a NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Jason B. Smith source.ver: src/contrib/PathNet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PathNet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PathNet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PathNet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PathNet_1.6.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R Package: pathRender Version: 1.34.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods Suggests: ALL, hgu95av2.db License: LGPL MD5sum: f3772e4d1cf5082c899ec0f273298469 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Li Long URL: http://www.bioconductor.org source.ver: src/contrib/pathRender_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pathRender_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pathRender_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pathRender_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pathRender_1.34.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R Package: pathview Version: 1.6.0 Depends: R (>= 2.10), KEGGgraph, org.Hs.eg.db Imports: Rgraphviz, graph, png, AnnotationDbi, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 70ba9c8a1891822d13e5a68f950249b7 NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: http://pathview.r-forge.r-project.org/ source.ver: src/contrib/pathview_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pathview_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pathview_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pathview_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pathview_1.6.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R importsMe: CompGO suggestsMe: clusterProfiler, gage Package: paxtoolsr Version: 1.2.11 Depends: R (>= 3.1.1), rJava (>= 0.9-4), XML, RCurl, rjson, plyr Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, igraph, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma License: LGPL-3 MD5sum: 31d240c7a9cb862701b0bae693ffd7d3 NeedsCompilation: no Title: PaxtoolsR: Access Pathways from Multiple Databases through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network Author: Augustin Luna Maintainer: Augustin Luna URL: https://bitbucket.org/cbio_mskcc/paxtoolsr SystemRequirements: Java (>= 1.5) VignetteBuilder: knitr source.ver: src/contrib/paxtoolsr_1.2.11.tar.gz win.binary.ver: bin/windows/contrib/3.1/paxtoolsr_1.2.11.zip win64.binary.ver: bin/windows64/contrib/3.1/paxtoolsr_1.2.11.zip mac.binary.ver: bin/macosx/contrib/3.1/paxtoolsr_1.2.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/paxtoolsr_1.2.11.tgz vignettes: vignettes/paxtoolsr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R htmlDocs: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html htmlTitles: "Using PaxtoolsR" Package: Pbase Version: 0.4.0 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings, IRanges, S4Vectors, mzID, mzR (>= 1.99.1), MSnbase (>= 1.13.5), Pviz Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.UCSC.hg19, biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, BiocStyle License: GPL-3 MD5sum: 7db90c0532f7e157c4190f42db38a90c NeedsCompilation: no Title: Manipulating and exploring protein and proteomics data Description: A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment. biocViews: Infrastructure, Proteomics, MassSpectrometry, Visualization, DataImport, DataRepresentation Author: Laurent Gatto [aut], Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb , Laurent Gatto URL: https://github.com/ComputationalProteomicsUnit/Pbase VignetteBuilder: knitr source.ver: src/contrib/Pbase_0.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Pbase_0.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Pbase_0.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Pbase_0.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Pbase_0.4.0.tgz vignettes: vignettes/Pbase/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/mapping.R, vignettes/Pbase/inst/doc/Pbase-data.R htmlDocs: vignettes/Pbase/inst/doc/mapping.html, vignettes/Pbase/inst/doc/Pbase-data.html htmlTitles: "mapping", "Pbase-data" Package: pcaGoPromoter Version: 1.10.0 Depends: R (>= 2.14.0) , ellipse Imports: Biobase (>= 2.10.0) , AnnotationDbi Suggests: Rgraphviz, GO.db, hgu133plus2.db, mouse4302.db, rat2302.db, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, Biostrings, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, serumStimulation, parallel License: GPL (>= 2) MD5sum: 760ef3f5cbbe941cbe1db560755509ae NeedsCompilation: no Title: pcaGoPromoter is used to analyze DNA micro array data Description: This package contains functions to ease the analyses of DNA micro arrays. It utilizes principal component analysis as the initial multivariate analysis, followed by functional interpretation of the principal component dimensions with overrepresentation analysis for GO terms and regulatory interpretations using overrepresentation analysis of predicted transcription factor binding sites with the primo algorithm. biocViews: GeneExpression, Microarray, GO , Visualization Author: Morten Hansen, Jorgen Olsen Maintainer: Morten Hansen source.ver: src/contrib/pcaGoPromoter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pcaGoPromoter_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pcaGoPromoter_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pcaGoPromoter_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pcaGoPromoter_1.10.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.R Package: pcaMethods Version: 1.56.0 Depends: Biobase, methods, Rcpp (>= 0.8.7) Imports: BiocGenerics, MASS LinkingTo: Rcpp Suggests: matrixStats, lattice License: GPL (>= 3) Archs: i386, x64 MD5sum: 5be349f5923cf72e049c2da6dec95ce2 NeedsCompilation: yes Title: A collection of PCA methods. Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig SystemRequirements: Rcpp source.ver: src/contrib/pcaMethods_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pcaMethods_1.56.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pcaMethods_1.56.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pcaMethods_1.56.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pcaMethods_1.56.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq importsMe: CompGO, MSnbase, SomaticSignatures Package: pcot2 Version: 1.34.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: a1821b406e0b076f955fb9afafa5f565 NeedsCompilation: no Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression Author: Sarah Song, Mik Black Maintainer: Sarah Song source.ver: src/contrib/pcot2_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pcot2_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pcot2_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pcot2_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pcot2_1.34.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcot2/inst/doc/pcot2.R Package: PCpheno Version: 1.28.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 6e0af67c87f8262d44d12cbddf0624f2 NeedsCompilation: no Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/PCpheno_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PCpheno_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PCpheno_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PCpheno_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PCpheno_1.28.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCpheno/inst/doc/PCpheno.R Package: pdInfoBuilder Version: 1.30.6 Depends: R (>= 3.1.0), methods, Biobase (>= 2.25.0), RSQLite (>= 1.0.0), affxparser (>= 1.37.2), oligo (>= 1.29.1) Imports: Biostrings (>= 2.33.14), BiocGenerics (>= 0.11.5), DBI (>= 0.3.1), IRanges (>= 1.99.31), oligoClasses (>= 1.27.2), S4Vectors (>= 0.2.5) License: Artistic-2.0 Archs: i386, x64 MD5sum: 1c4636d0bc048e9eadef6e27be5f9ebb NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho source.ver: src/contrib/pdInfoBuilder_1.30.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/pdInfoBuilder_1.30.6.zip win64.binary.ver: bin/windows64/contrib/3.1/pdInfoBuilder_1.30.6.zip mac.binary.ver: bin/macosx/contrib/3.1/pdInfoBuilder_1.30.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pdInfoBuilder_1.30.6.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.R, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R Package: pdmclass Version: 1.38.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), fibroEset, mda License: Artistic-2.0 MD5sum: 2cf492b5e7cfe518e0019f0822e85fc6 NeedsCompilation: no Title: Classification of Microarray Samples using Penalized Discriminant Methods Description: This package can be used to classify microarray data using one of three penalized regression methods; partial least squares, principal components regression, or ridge regression. biocViews: Classification Author: James W. MacDonald, Debashis Ghosh, based in part on pls code of Mike Denham Maintainer: James W. MacDonald source.ver: src/contrib/pdmclass_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pdmclass_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pdmclass_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pdmclass_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pdmclass_1.38.0.tgz vignettes: vignettes/pdmclass/inst/doc/pdmclass.pdf vignetteTitles: pdmclass Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdmclass/inst/doc/pdmclass.R suggestsMe: oneChannelGUI Package: PECA Version: 1.2.0 Imports: limma, affy, genefilter, preprocessCore Suggests: SpikeIn, ROCR, multtest License: GPL (>= 2) MD5sum: 2430a8bdc9164b18fcdc0a178512f8e3 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Preprocessing, Microarray, DifferentialExpression, GeneExpression Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi source.ver: src/contrib/PECA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PECA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PECA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PECA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PECA_1.2.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R Package: pepStat Version: 1.0.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: c3e43446da2735ece2ab717105f55c41 NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr source.ver: src/contrib/pepStat_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pepStat_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pepStat_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pepStat_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pepStat_1.0.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R Package: pepXMLTab Version: 1.0.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 537e21dfdac1e9e284021eb64ea184a1 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/pepXMLTab_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pepXMLTab_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pepXMLTab_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pepXMLTab_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pepXMLTab_1.0.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R Package: PGSEA Version: 1.40.0 Depends: R (>= 2.10), GO.db, KEGG.db, AnnotationDbi, annaffy, methods, Biobase (>= 2.5.5) Suggests: GSEABase, GEOquery, org.Hs.eg.db, hgu95av2.db, limma License: GPL-2 MD5sum: 967034456ca685b954cd3037e7aae6aa NeedsCompilation: no Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema source.ver: src/contrib/PGSEA_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PGSEA_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PGSEA_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PGSEA_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PGSEA_1.40.0.tgz vignettes: vignettes/PGSEA/inst/doc/PGSEA.pdf, vignettes/PGSEA/inst/doc/PGSEA2.pdf vignetteTitles: HOWTO: PGSEA, HOWTO: PGSEA Example Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGSEA/inst/doc/PGSEA.R, vignettes/PGSEA/inst/doc/PGSEA2.R dependsOnMe: GeneExpressionSignature Package: phenoDist Version: 1.14.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: b2b081d85a8f9741bfa2e724e1be220a NeedsCompilation: no Title: Phenotypic distance measures Description: PhenoDist is designed for measuring phenotypic distance in image-based high-throughput screening, in order to identify strong phenotypes and to group treatments into functional clusters. biocViews: CellBasedAssays Author: Xian Zhang, Gregoire Pau, Wolfgang Huber, Michael Boutros Maintainer: Xian Zhang URL: http://www.dkfz.de/signaling, http://www.embl.de/research/units/genome_biology/huber/ source.ver: src/contrib/phenoDist_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/phenoDist_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/phenoDist_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/phenoDist_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/phenoDist_1.14.0.tgz vignettes: vignettes/phenoDist/inst/doc/phenoDist.pdf vignetteTitles: Introduction to phenoDist hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoDist/inst/doc/phenoDist.R Package: phenoTest Version: 1.14.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, gridExtra Imports: survival, limma, Hmisc, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, SNPchip, hgu133a.db, HTSanalyzeR, ellipse Suggests: GSEABase, KEGG.db, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: 58010530a7612aed373a2b58508c47a1 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet source.ver: src/contrib/phenoTest_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/phenoTest_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/phenoTest_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/phenoTest_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/phenoTest_1.14.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R Package: PhenStat Version: 2.0.1 Depends: R (>= 2.3.0) Imports: methods, car, nlme, nortest, vcd, limma Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: 97f59d507fdd604c4fbfec016cf69cde NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason Maintainer: Natasha Karp source.ver: src/contrib/PhenStat_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/PhenStat_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/PhenStat_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/PhenStat_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PhenStat_2.0.1.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf, vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf vignetteTitles: PhenStat Vignette, PhenStatUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R Package: phyloseq Version: 1.10.0 Depends: R (>= 3.1.0) Imports: ade4 (>= 1.6.2), ape (>= 3.1.1), biom (>= 0.3.9), Biostrings (>= 2.28.0), cluster (>= 1.14.4), data.table (>= 1.9.2), DESeq2 (>= 1.4.0), foreach (>= 1.4.2), ggplot2 (>= 1.0.0), igraph (>= 0.7.0), methods (>= 3.1.0), multtest (>= 2.16.0), plyr (>= 1.8), reshape2 (>= 1.2.2), scales (>= 0.2.3), vegan (>= 2.0.10) Suggests: genefilter (>= 1.42.0), testthat (>= 0.8), knitr (>= 1.3) Enhances: doParallel (>= 1.0.1) License: AGPL-3 MD5sum: 5782d836c3b159f054b2f3d6815bf395 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data. Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr source.ver: src/contrib/phyloseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/phyloseq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/phyloseq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/phyloseq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/phyloseq_1.10.0.tgz vignettes: vignettes/phyloseq/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R htmlDocs: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html htmlTitles: "phyloseq analysis vignette", "phyloseq basics vignette", "phyloseq and DESeq2 on Colorectal Cancer Data" Package: piano Version: 1.6.2 Depends: R (>= 2.14.0) Imports: Biobase, gplots, igraph, relations, marray Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall License: GPL (>=2) MD5sum: f468a4ebf6f087b1b6860681a91ec200 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo and Intawat Nookaew Maintainer: Leif Varemo URL: http://www.sysbio.se/piano source.ver: src/contrib/piano_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/piano_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/piano_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/piano_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/piano_1.6.2.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf vignetteTitles: Piano - Platform for Integrative Analysis of Omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R Package: pickgene Version: 1.38.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 201b0d2686d1d341195298a137c0bfda NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen source.ver: src/contrib/pickgene_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pickgene_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pickgene_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pickgene_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pickgene_1.38.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pickgene/inst/doc/pickgene.R Package: PICS Version: 2.10.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.1.3) Imports: methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments Suggests: ShortRead, rtracklayer, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 106aa4037f4c93e9c592b41797ddb12d NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPSeq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud source.ver: src/contrib/PICS_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PICS_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PICS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PICS_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PICS_2.10.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING Package: PING Version: 2.10.0 Depends: R(>= 2.15.0), chipseq, IRanges, GenomicRanges Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: f51fb63ce237066ed83a36b943b08c87 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo, Maintainer: Renan Sauteraud source.ver: src/contrib/PING_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PING_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PING_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PING_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PING_2.10.0.tgz vignettes: vignettes/PING/inst/doc/PING-PE.pdf, vignettes/PING/inst/doc/PING.pdf vignetteTitles: Using PING with paired-end sequencing data, The PING users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PING/inst/doc/PING-PE.R, vignettes/PING/inst/doc/PING.R Package: pint Version: 1.16.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: db166a34980a0e8bb37b3fdc39f65eac NeedsCompilation: no Title: Pairwise INTegration of functional genomics data Description: Pairwise data integration for functional genomics, including tools for DNA/RNA/miRNA dependency screens. biocViews: aCGH, GeneExpression, Genetics, DifferentialExpression, Microarray Author: Olli-Pekka Huovilainen and Leo Lahti Maintainer: Olli-Pekka Huovilainen URL: https://github.com/antagomir/pint source.ver: src/contrib/pint_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pint_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pint_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pint_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pint_1.16.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pint/inst/doc/depsearch.R Package: pkgDepTools Version: 1.32.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: 8ffc2a1974a655b2b50d363098245014 NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon Maintainer: Seth Falcon source.ver: src/contrib/pkgDepTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pkgDepTools_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pkgDepTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pkgDepTools_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pkgDepTools_1.32.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R Package: plateCore Version: 1.24.0 Depends: R (>= 2.10), flowCore, flowViz, lattice, latticeExtra Imports: Biobase, flowCore, graphics, grDevices, lattice, MASS, methods, robustbase, stats, utils, flowStats Suggests: gplots License: Artistic-2.0 MD5sum: 1e807e8d830991c50c37197a87c2240d NeedsCompilation: no Title: Statistical tools and data structures for plate-based flow cytometry Description: Provides basic S4 data structures and routines for analyzing plate based flow cytometry data. biocViews: FlowCytometry, Infrastructure, CellBasedAssays Author: Errol Strain, Florian Hahne, and Perry Haaland Maintainer: Errol Strain URL: http://www.bioconductor.org source.ver: src/contrib/plateCore_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/plateCore_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/plateCore_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/plateCore_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plateCore_1.24.0.tgz vignettes: vignettes/plateCore/inst/doc/plateCoreVig.pdf vignetteTitles: An R Package for Analysis of High Throughput Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plateCore/inst/doc/plateCoreVig.R Package: plethy Version: 1.4.3 Depends: R (>= 3.1.0), methods, BiocGenerics, S4Vectors Imports: Streamer, DBI, RSQLite (>= 1.0.0), IRanges, reshape2, plyr, RColorBrewer,ggplot2 Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 5a51fef49ff71a582953efe07b69da2c NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly source.ver: src/contrib/plethy_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/plethy_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.1/plethy_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.1/plethy_1.4.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plethy_1.4.3.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R Package: plgem Version: 1.38.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: 3200d9a80da14a8595a2c9841ea11677 NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it source.ver: src/contrib/plgem_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/plgem_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/plgem_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/plgem_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plgem_1.38.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R Package: plier Version: 1.36.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 442a4fb4fbdf4c3a44b819a10a996cb9 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller source.ver: src/contrib/plier_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/plier_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/plier_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/plier_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plier_1.36.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.26.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: f43fd09b2ca271896ec868eb6c69d378 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/PLPE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PLPE_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PLPE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PLPE_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PLPE_1.26.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R Package: plrs Version: 1.6.0 Depends: R (>= 2.10), Biobase Imports: BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils Suggests: mvtnorm, methods License: GPL (>=2.0) MD5sum: 7925ddf91f7f0ef62bbd82b004d84be0 NeedsCompilation: no Title: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression Description: The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS). biocViews: Regression Author: Gwenael G.R. Leday Maintainer: Gwenael G.R. Leday to source.ver: src/contrib/plrs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/plrs_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/plrs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/plrs_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plrs_1.6.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plrs/inst/doc/plrs_vignette.R Package: plw Version: 1.26.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: d64fe255272928d144cb58efb9a2cea2 NeedsCompilation: yes Title: Probe level Locally moderated Weighted t-tests. Description: Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). biocViews: Microarray, OneChannel, TwoChannel, DifferentialExpression Author: Magnus Astrand Maintainer: Magnus Astrand source.ver: src/contrib/plw_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/plw_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/plw_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/plw_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/plw_1.26.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plw/inst/doc/HowToPLW.R Package: polyester Version: 1.0.2 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: b42a9ae9f48e1d4ff5cd6a52010ba6f9 NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Jeffrey T. Leek Maintainer: Alyssa Frazee , Jeff Leek VignetteBuilder: knitr source.ver: src/contrib/polyester_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/polyester_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/polyester_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/polyester_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/polyester_1.0.2.tgz vignettes: vignettes/polyester/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R htmlDocs: vignettes/polyester/inst/doc/polyester.html htmlTitles: "The Polyester package for simulating RNA-seq reads" Package: Polyfit Version: 1.0.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: add7dacf2acb3cb876544d46ba47ca93 NeedsCompilation: no Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/Polyfit_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Polyfit_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Polyfit_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Polyfit_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Polyfit_1.0.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polyfit/inst/doc/polyfit.R Package: ppiStats Version: 1.32.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: 4d51e540ff3b29a22d62fefc622eda4c NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ppiStats_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ppiStats_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ppiStats_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ppiStats_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ppiStats_1.32.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML Package: prada Version: 1.42.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS, tcltk License: LGPL Archs: i386, x64 MD5sum: 267fe6e70b9693f457162e580c563063 NeedsCompilation: yes Title: Data analysis for cell-based functional assays Description: Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy). biocViews: CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne source.ver: src/contrib/prada_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/prada_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/prada_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/prada_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/prada_1.42.0.tgz vignettes: vignettes/prada/inst/doc/norm2.pdf, vignettes/prada/inst/doc/prada2cellHTS.pdf vignetteTitles: Removal of contaminants from FACS data, Combining prada output and cellHTS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prada/inst/doc/norm2.R, vignettes/prada/inst/doc/prada2cellHTS.R dependsOnMe: cellHTS, domainsignatures, RNAither importsMe: cellHTS2 Package: prebs Version: 1.6.0 Depends: R (>= 2.14.0), GenomicAlignments, affy Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: 782439e6ca7ebc5c44ac089cdf47531a NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela source.ver: src/contrib/prebs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/prebs_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/prebs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/prebs_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/prebs_1.6.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R Package: PREDA Version: 1.12.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, samr, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 370660b72be64162bb23b898418639a7 NeedsCompilation: no Title: Position RElated Data Anlysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari source.ver: src/contrib/PREDA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PREDA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PREDA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PREDA_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PREDA_1.12.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAclasses.R, vignettes/PREDA/inst/doc/PREDAtutorial.R Package: predictionet Version: 1.12.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 116c916bde8d7f4991f4f9cc284f46c8 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg source.ver: src/contrib/predictionet_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/predictionet_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/predictionet_1.12.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R Package: preprocessCore Version: 1.28.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 16baa1918d6e5abc516ae2265f311b06 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines biocViews: Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad source.ver: src/contrib/preprocessCore_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/preprocessCore_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/preprocessCore_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/preprocessCore_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/preprocessCore_1.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, CopyNumber450k, cqn, crlmm, RefPlus importsMe: affy, AffyTiling, ChAMP, charm, cn.farms, ExiMiR, frma, frmaTools, lumi, MBCB, minfi, MSnbase, MSstats, oligo, PECA, waveTiling suggestsMe: oneChannelGUI Package: proBAMr Version: 1.0.1 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1d0fd235dbe3d12d77989be964730937 NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data. Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/proBAMr_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/proBAMr_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/proBAMr_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/proBAMr_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/proBAMr_1.0.1.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R Package: PROcess Version: 1.42.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 539132d66dd784ed27631fe22ef1b3f9 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li source.ver: src/contrib/PROcess_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PROcess_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PROcess_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PROcess_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PROcess_1.42.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R Package: procoil Version: 1.16.0 Depends: R (>= 2.12.0) Imports: methods, stats, graphics Suggests: Biostrings License: GPL (>= 2) MD5sum: 19e47281377ade93c9edfb99c754221a NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The procoil package allows to predict whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. The predict function not only computes the prediction itself, but also a profile which allows to determine the strengths to which the individual residues are indicative for either class. Profiles can also be plotted and exported to files. biocViews: Proteomics, Classification Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil source.ver: src/contrib/procoil_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/procoil_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/procoil_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/procoil_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/procoil_1.16.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R Package: ProCoNA Version: 1.4.1 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: 51876eb7d0b953a1c6dcbe466907c0f4 NeedsCompilation: no Title: Protein co-expression network analysis (ProCoNA). Description: Protein co-expression network construction using peptide level data, with statisical analysis. (Journal of Clinical Bioinformatics 2013, 3:11 doi:10.1186/2043-9113-3-11) biocViews: GraphAndNetwork, Software, Proteomics Author: David L Gibbs Maintainer: David L Gibbs source.ver: src/contrib/ProCoNA_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ProCoNA_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ProCoNA_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ProCoNA_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ProCoNA_1.4.1.tgz vignettes: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.pdf vignetteTitles: De Novo Peptide Network Example hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.R Package: pRoloc Version: 1.6.2 Depends: R (>= 2.15), MSnbase (>= 1.13.3), MLInterfaces (>= 1.37.1), methods, Rcpp (>= 0.10.3), BiocParallel Imports: mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, BiocGenerics, stats, RColorBrewer, scales, MASS, knitr, mvtnorm LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, pRolocdata, roxygen2, synapter, xtable License: GPL-2 Archs: i386, x64 MD5sum: ca7754114aa4233e4ad2039f034dc563 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: This package implements pattern recognition techniques on quantitiative mass spectrometry data to infer protein sub-cellular localisation. biocViews: Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow source.ver: src/contrib/pRoloc_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/pRoloc_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/pRoloc_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/pRoloc_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pRoloc_1.6.2.tgz vignettes: vignettes/pRoloc/inst/doc/HUPO_2011_poster.pdf, vignettes/pRoloc/inst/doc/HUPO_2014_poster.pdf, vignettes/pRoloc/inst/doc/pRoloc-ml.pdf, vignettes/pRoloc/inst/doc/pRoloc-tutorial.pdf vignetteTitles: pRoloc -- A unifying bioinformatics framework for organelle proteomics, A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data, Machine learning techniques available in pRoloc, pRoloc tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/HUPO_2011_poster.R, vignettes/pRoloc/inst/doc/HUPO_2014_poster.R, vignettes/pRoloc/inst/doc/pRoloc-ml.R, vignettes/pRoloc/inst/doc/pRoloc-tutorial.R dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.0.2 Depends: R (>= 3.1.0), pRoloc (>= 1.5.12), MSnbase (>= 1.13.11), methods Imports: pRolocdata, shiny (>= 0.9.1), tools (>= 3.1.0) Suggests: RUnit, BiocGenerics, knitr, knitrBootstrap, bibtex, knitcitations (>= 1.0-1) License: GPL-2 MD5sum: 22fbb9e4fdd548628a0a604fb88bf70a NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Thomas Naake and Laurent Gatto Maintainer: Laurent Gatto , Thomas Naake URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow source.ver: src/contrib/pRolocGUI_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/pRolocGUI_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/pRolocGUI_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/pRolocGUI_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pRolocGUI_1.0.2.tgz vignettes: vignettes/pRolocGUI/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R htmlDocs: vignettes/pRolocGUI/inst/doc/pRolocGUI.html htmlTitles: "pRolocVis and pRolocComp application" Package: PROMISE Version: 1.18.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: ebfad24bb7cd8ead722d131488bd12f2 NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao source.ver: src/contrib/PROMISE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PROMISE_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PROMISE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PROMISE_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PROMISE_1.18.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R Package: prot2D Version: 1.4.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: 05bcc2bf289a91b200fc98158783d803 NeedsCompilation: no Title: Statistical Tools for volume data from 2D Gel Electrophoresis Description: The purpose of this package is to analyze (i.e. Normalize and select significant spots) data issued from 2D GEl experiments biocViews: DifferentialExpression, MultipleComparison, Proteomics Author: Sebastien Artigaud Maintainer: Sebastien Artigaud source.ver: src/contrib/prot2D_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/prot2D_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/prot2D_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/prot2D_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/prot2D_1.4.0.tgz vignettes: vignettes/prot2D/inst/doc/prot2D.pdf vignetteTitles: prot2D hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prot2D/inst/doc/prot2D.R Package: proteinProfiles Version: 1.6.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: e2b1c700a0a5cd18543435bfdab404f3 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring source.ver: src/contrib/proteinProfiles_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/proteinProfiles_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/proteinProfiles_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/proteinProfiles_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/proteinProfiles_1.6.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R Package: proteoQC Version: 1.2.0 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, Rcpp (>= 0.11.1) LinkingTo: Rcpp Suggests: RforProteomics (>= 1.0.16), knitr, BiocStyle, rpx, R.utils, RUnit,BiocGenerics License: LGPL-2 Archs: i386, x64 MD5sum: daef03e77b7c6662284e0f0686bffc88 NeedsCompilation: yes Title: An R package for proteomics data quality control Description: This package creates a HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. biocViews: Proteomics, MassSpectrometry, QualityControl, Visualization, ReportWriting Author: Bo Wen , Laurent Gatto Maintainer: Bo Wen VignetteBuilder: knitr source.ver: src/contrib/proteoQC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/proteoQC_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/proteoQC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/proteoQC_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/proteoQC_1.2.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.pdf vignetteTitles: proteoQC tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R Package: PSEA Version: 1.0.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 752e3024bd0abc58a447675048a4ee65 NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn source.ver: src/contrib/PSEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PSEA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PSEA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PSEA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PSEA_1.0.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA.R Package: PSICQUIC Version: 1.4.5 Depends: R (>= 2.15.0), methods, IRanges, biomaRt, BiocGenerics, httr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 5232871f5665f0bbed8a30811e9d9083 NeedsCompilation: no Title: Protemics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/PSICQUIC_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/PSICQUIC_1.4.5.zip win64.binary.ver: bin/windows64/contrib/3.1/PSICQUIC_1.4.5.zip mac.binary.ver: bin/macosx/contrib/3.1/PSICQUIC_1.4.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PSICQUIC_1.4.5.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependsOnMe: RefNet Package: puma Version: 3.8.0 Depends: R (>= 3.0), Biobase (>= 2.5.5), affy (>= 1.41.3), graphics, grDevices, methods, stats, utils, mclust, oligo, Imports: Biobase (>= 2.5.5), affy (>= 1.41.3), affyio Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL Archs: i386, x64 MD5sum: 3b5bf3349b3dd87ac86e7e06fe64e280 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma source.ver: src/contrib/puma_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/puma_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/puma_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/puma_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/puma_3.8.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: tigre Package: pvac Version: 1.14.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 444181b6c3000378d0b9309c0f0bee82 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel source.ver: src/contrib/pvac_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pvac_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pvac_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pvac_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pvac_1.14.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R Package: pvca Version: 1.6.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 347cfd4eba80013c61ca9b82415b7334 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI source.ver: src/contrib/pvca_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/pvca_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/pvca_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/pvca_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/pvca_1.6.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R Package: Pviz Version: 1.0.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 9b957d8fbe1efd2d5febb95f61fe4cff NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr source.ver: src/contrib/Pviz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Pviz_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Pviz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Pviz_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Pviz_1.0.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R importsMe: Pbase suggestsMe: pepStat Package: PWMEnrich Version: 4.2.0 Depends: methods, grid, BiocGenerics, Biostrings, Imports: seqLogo, gdata, evd Suggests: MotifDb, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background License: LGPL (>= 2) MD5sum: ad0a2eb127b31de35ec9929b00d8e41b NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Robert Stojnic source.ver: src/contrib/PWMEnrich_4.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/PWMEnrich_4.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/PWMEnrich_4.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/PWMEnrich_4.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/PWMEnrich_4.2.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R suggestsMe: rTRM Package: qcmetrics Version: 1.4.1 Depends: R (>= 2.10) Imports: Biobase, methods, knitr, tools, Nozzle.R1, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, yaqcaffy, MAQCsubsetAFX, RforProteomics, AnnotationDbi, mzR, hgu133plus2cdf License: GPL-2 MD5sum: 92c65b13d900938a1dbf6e8eef997865 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: Software, Bioinformatics, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualisation, ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr source.ver: src/contrib/qcmetrics_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/qcmetrics_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/qcmetrics_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/qcmetrics_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qcmetrics_1.4.1.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R Package: QDNAseq Version: 1.2.4 Depends: R (>= 2.15.0) Imports: graphics, methods, stats, utils, matrixStats (>= 0.9.4), R.utils (>= 1.28.4), Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), Rsamtools (>= 1.10.0) Suggests: R.cache (>= 0.9.0), digest, snowfall, BSgenome, GenomeInfoDb License: GPL MD5sum: 1da3dcc1130097993ab4c68f6ccea84e NeedsCompilation: no Title: Quantitative DNA sequencing for chromosomal aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq source.ver: src/contrib/QDNAseq_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/QDNAseq_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.1/QDNAseq_1.2.4.zip mac.binary.ver: bin/macosx/contrib/3.1/QDNAseq_1.2.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/QDNAseq_1.2.4.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R Package: qpcrNorm Version: 1.24.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: abac35678a636633a3c3b2e0c0caf994 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar source.ver: src/contrib/qpcrNorm_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/qpcrNorm_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/qpcrNorm_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/qpcrNorm_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qpcrNorm_1.24.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R suggestsMe: EasyqpcR Package: qpgraph Version: 2.0.5 Depends: R (>= 3.0.0) Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>= 1.44.0), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: i386, x64 MD5sum: 9bac17472b739a20f70b034f4e560027 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Procedures to estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: R. Castelo and A. Roverato Maintainer: Robert Castelo URL: http://functionalgenomics.upf.edu/qpgraph source.ver: src/contrib/qpgraph_2.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/qpgraph_2.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/qpgraph_2.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/qpgraph_2.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qpgraph_2.0.5.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper Package: qrqc Version: 1.20.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, Rsamtools (>= 1.3.28), testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rsamtools License: GPL (>=2) Archs: i386, x64 MD5sum: 6b72c56230388bd0e1e6abebbf0139f1 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc source.ver: src/contrib/qrqc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/qrqc_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/qrqc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/qrqc_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qrqc_1.20.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R Package: QUALIFIER Version: 1.10.0 Depends: R (>= 2.14.0),flowCore,flowViz,ncdfFlow,flowWorkspace, data.table,reshape Imports: MASS,hwriter,lattice,stats4,flowCore,flowViz,methods,flowWorkspace,latticeExtra,grDevices,tools, Biobase,XML Suggests: RSVGTipsDevice License: Artistic-2.0 MD5sum: 7e3236c6dfaf9737cb8c71fea8c1392c NeedsCompilation: no Title: Quality Control of Gated Flow Cytometry Experiments Description: Provides quality control and quality assessment tools for gated flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Mike Jiang,Greg Finak,Raphael Gottardo Maintainer: Mike Jiang source.ver: src/contrib/QUALIFIER_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/QUALIFIER_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/QUALIFIER_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/QUALIFIER_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/QUALIFIER_1.10.0.tgz vignettes: vignettes/QUALIFIER/inst/doc/QUALIFIER.pdf vignetteTitles: Quality assessment for gated Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QUALIFIER/inst/doc/QUALIFIER.R Package: quantro Version: 1.0.0 Depends: R (>= 3.1.1) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: ab5b2bd21fbf95a933229e695dc9a423 NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks and Rafael Irizarry Maintainer: Stephanie Hicks VignetteBuilder: knitr source.ver: src/contrib/quantro_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/quantro_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/quantro_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/quantro_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/quantro_1.0.0.tgz vignettes: vignettes/quantro/inst/doc/quantro-vignette.pdf vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro-vignette.R Package: quantsmooth Version: 1.32.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: ca8b2957f19a16cdffd802ba3508051b NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting source.ver: src/contrib/quantsmooth_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/quantsmooth_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/quantsmooth_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/quantsmooth_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/quantsmooth_1.32.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA Package: QuasR Version: 1.6.2 Depends: parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, zlibbioc, BiocGenerics, S4Vectors, IRanges, BiocInstaller, Biobase, Biostrings, GenomicRanges, BSgenome, Rsamtools (>= 1.13.1), GenomicFeatures (>= 1.17.13), ShortRead (>= 1.19.1), GenomicAlignments LinkingTo: Rsamtools Suggests: Rsamtools, rtracklayer, Gviz, RUnit, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 756c03740e3d4287cf08a148b00efce2 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq Author: Anita Lerch, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler source.ver: src/contrib/QuasR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/QuasR_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/QuasR_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/QuasR_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/QuasR_1.6.2.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.pdf vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R Package: qusage Version: 1.6.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase License: GPL (>= 2) MD5sum: bb2b070b035ca78c92c08be633548c07 NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage source.ver: src/contrib/qusage_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/qusage_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/qusage_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/qusage_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qusage_1.6.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R Package: qvalue Version: 1.43.0 Imports: graphics, stats License: LGPL MD5sum: b68eb19f0c0d7e5fa38700696ad759c6 NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparison Author: Alan Dabney and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/qvalue_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/qvalue_1.43.0.zip win64.binary.ver: bin/windows64/contrib/3.1/qvalue_1.43.0.zip mac.binary.ver: bin/macosx/contrib/3.1/qvalue_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/qvalue_1.43.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, CancerMutationAnalysis, DEGseq, DrugVsDisease, metaseqR, netresponse, SSPA, webbioc importsMe: anota, derfinder, DOSE, EnrichmentBrowser, erccdashboard, msmsTests, Rnits, sRAP, synapter, trigger, webbioc suggestsMe: LBE, maanova, PREDA Package: r3Cseq Version: 1.12.1 Depends: GenomicRanges,Rsamtools,data.table,rtracklayer,VGAM,qvalue,RColorBrewer,sqldf,methods Suggests: BSgenome.Mmusculus.UCSC.mm9.masked,BSgenome.Hsapiens.UCSC.hg18.masked,BSgenome.Hsapiens.UCSC.hg19.masked License: GPL-3 MD5sum: 0ae518ea239469eae52f5e4c7cf0142c NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is an implementation of data analysis for the long-range interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea URL: http://r3cseq.genereg.net source.ver: src/contrib/r3Cseq_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/r3Cseq_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/r3Cseq_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/r3Cseq_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/r3Cseq_1.12.1.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R Package: R453Plus1Toolbox Version: 1.16.0 Depends: R (>= 2.12.0), Biobase, Biostrings, GenomicRanges Imports: BiocGenerics (>= 0.1.3), biomaRt, BSgenome, IRanges, XVector, methods, R2HTML, Rsamtools, ShortRead, VariantAnnotation, xtable, tools, TeachingDemos Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: 47d6036b5d7589f86e35d9a5cfe84c10 NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System. Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein source.ver: src/contrib/R453Plus1Toolbox_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/R453Plus1Toolbox_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/R453Plus1Toolbox_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/R453Plus1Toolbox_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/R453Plus1Toolbox_1.16.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R Package: rain Version: 1.0.1 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 17d8120b840668573c9982fc16d0770d NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben source.ver: src/contrib/rain_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/rain_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/rain_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/rain_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rain_1.0.1.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R Package: rama Version: 1.40.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 90c6755da28731ced629dca9623a54c9 NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo source.ver: src/contrib/rama_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rama_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rama_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rama_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rama_1.40.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge Package: RamiGO Version: 1.12.1 Depends: gsubfn,methods Imports: igraph,RCurl,png,RCytoscape,graph License: Artistic-2.0 MD5sum: 6475cf3e1eb208357ebfc9389f921601 NeedsCompilation: no Title: AmiGO visualize R interface Description: R interface sending requests to AmiGO visualize, retrieving DAG GO trees, parsing GraphViz DOT format files and exporting GML files for Cytoscape. Also uses RCytoscape to interactively display AmiGO trees in Cytoscape. biocViews: GO, Visualization, GraphAndNetwork, Classification, ThirdPartyClient Author: Markus Schroeder, Daniel Gusenleitner, John Quackenbush, Aedin Culhane, Benjamin Haibe-Kains Maintainer: Markus Schroeder source.ver: src/contrib/RamiGO_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/RamiGO_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/RamiGO_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/RamiGO_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RamiGO_1.12.1.tgz vignettes: vignettes/RamiGO/inst/doc/RamiGO.pdf vignetteTitles: RamiGO: An Introduction (HowTo) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RamiGO/inst/doc/RamiGO.R Package: randPack Version: 1.12.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: f43974a284bdbcb856f4fa563a65b460 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman source.ver: src/contrib/randPack_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/randPack_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/randPack_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/randPack_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/randPack_1.12.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R Package: RankProd Version: 2.38.0 Depends: R (>= 1.9.0) Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: a1ec8b58a24bc1f75610512bb33f2bbd NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression Author: Fangxin Hong and Ben Wittner with contribution from Rainer Breitling , Colin Smith , and Florian Battke Maintainer: Fangxin Hong source.ver: src/contrib/RankProd_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RankProd_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RankProd_2.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RankProd_2.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RankProd_2.38.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: RNAither, tRanslatome importsMe: HTSanalyzeR suggestsMe: oneChannelGUI Package: Rariant Version: 1.2.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: IRanges, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, methods, VGAM, dplyr, reshape2, GenomeInfoDb, S4Vectors Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 1156b3420b9bc33ecaf37d9aa88ae018 NeedsCompilation: no Title: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies Description: The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples. biocViews: Sequencing, StatisticalMethod, GenomicVariation, SomaticMutation, VariantDetection, Visualization Author: Julian Gehring, Simon Anders, Bernd Klaus (EMBL Heidelberg) Maintainer: Julian Gehring VignetteBuilder: knitr source.ver: src/contrib/Rariant_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rariant_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rariant_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rariant_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rariant_1.2.0.tgz vignettes: vignettes/Rariant/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R htmlDocs: vignettes/Rariant/inst/doc/Rariant-vignette.html htmlTitles: "Rariant" Package: RbcBook1 Version: 1.34.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 2630e4a4c542a4dcae1f602f643825a1 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey source.ver: src/contrib/RbcBook1_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RbcBook1_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RbcBook1_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RbcBook1_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RbcBook1_1.34.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R Package: RBGL Version: 1.42.0 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: ac0b32369a92ae6e258a46c1b9834f1a NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org source.ver: src/contrib/RBGL_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RBGL_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RBGL_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RBGL_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RBGL_1.42.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, joda, pkgDepTools, RpsiXML importsMe: biocViews, CAMERA, Category, clipper, DEGraph, flowWorkspace, GeneAnswers, GOSim, GOstats, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, Streamer suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, KEGGgraph, rBiopaxParser, VariantTools Package: RBioinf Version: 1.26.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 2b6c4c1a7a65b3cac384024ec4a14f5e NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman source.ver: src/contrib/RBioinf_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RBioinf_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RBioinf_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RBioinf_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RBioinf_1.26.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R Package: rBiopaxParser Version: 2.4.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL License: GPL (>= 2) MD5sum: e5769c0fa1d75e5afa101ff617f4e854 NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer/rBiopaxParser source.ver: src/contrib/rBiopaxParser_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rBiopaxParser_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rBiopaxParser_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rBiopaxParser_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rBiopaxParser_2.4.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R suggestsMe: NetPathMiner Package: Rbowtie Version: 1.6.0 Suggests: parallel License: Artistic-1.0 | file LICENSE MD5sum: 7186f0f7b359bea901cad471f46054bf NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler source.ver: src/contrib/Rbowtie_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rbowtie_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rbowtie_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rbowtie_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rbowtie_1.6.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.pdf vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR suggestsMe: chimera Package: rbsurv Version: 2.24.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 6496ede9b3e05e76e715c52d96b1a4a2 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ source.ver: src/contrib/rbsurv_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rbsurv_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rbsurv_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rbsurv_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rbsurv_2.24.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R Package: Rcade Version: 1.8.0 Depends: R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools Imports: graphics, S4Vectors, rgl, plotrix Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: c5bce0a2b27cddd7a1a4b26e54d9fef5 NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data. Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns source.ver: src/contrib/Rcade_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rcade_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rcade_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rcade_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rcade_1.8.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R Package: RCASPAR Version: 1.12.0 License: GPL (>=3) MD5sum: 8b4294fcf635bc28324e5182694df631 NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid Maintainer: Douaa Mugahid , Lars Kaderali source.ver: src/contrib/RCASPAR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RCASPAR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RCASPAR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RCASPAR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RCASPAR_1.12.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R Package: Rchemcpp Version: 2.4.0 Depends: R (>= 2.15.0) Imports: Rcpp (>= 0.11.1), methods, ChemmineR LinkingTo: Rcpp Suggests: apcluster, kernlab License: GPL (>= 2.1) Archs: i386, x64 MD5sum: 774bd554445bf6d85f15ed2f071c7de4 NeedsCompilation: yes Title: Similarity measures for chemical compounds Description: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. biocViews: Bioinformatics, CellBasedAssays, Clustering, DataImport, Infrastructure, MicrotitrePlateAssay, Proteomics, Software, Visualization Author: Michael Mahr, Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/Rchemcpp source.ver: src/contrib/Rchemcpp_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rchemcpp_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rchemcpp_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rchemcpp_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rchemcpp_2.4.0.tgz vignettes: vignettes/Rchemcpp/inst/doc/Rchemcpp.pdf vignetteTitles: Rchemcpp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rchemcpp/inst/doc/Rchemcpp.R Package: RchyOptimyx Version: 2.6.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: 23427d85accd11eb386dadf02046638e NeedsCompilation: yes Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour source.ver: src/contrib/RchyOptimyx_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RchyOptimyx_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RchyOptimyx_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RchyOptimyx_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RchyOptimyx_2.6.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.R Package: Rcpi Version: 1.2.0 Imports: RCurl, rjson, rcdk, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR Suggests: RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 50bd2ca87b255c5bad919bc3b2e62e33 NeedsCompilation: no Title: Toolkit for Compound-Protein Interaction in Drug Discovery Description: The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, GraphAndNetwork, SystemsBiology Author: Nan Xiao , Dongsheng Cao , Qingsong Xu Maintainer: Nan Xiao URL: https://github.com/road2stat/Rcpi source.ver: src/contrib/Rcpi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rcpi_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rcpi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rcpi_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rcpi_1.2.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.pdf, vignettes/Rcpi/inst/doc/Rcpi.pdf vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcpi/inst/doc/Rcpi-quickref.R, vignettes/Rcpi/inst/doc/Rcpi.R Package: RCytoscape Version: 1.16.0 Depends: R (>= 2.14.0), graph (>= 1.31.0), XMLRPC (>= 0.2.4) Imports: methods, XMLRPC, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: 1dc11ebc92fdb8883a67d4e1a5a56262 NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape Description: Interactvive viewing and exploration of graphs, connecting R to Cytoscape. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCytoscape_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RCytoscape_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RCytoscape_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RCytoscape_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RCytoscape_1.16.0.tgz vignettes: vignettes/RCytoscape/inst/doc/RCytoscape.pdf vignetteTitles: RCytoscape Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCytoscape/inst/doc/RCytoscape.R importsMe: categoryCompare, NCIgraph suggestsMe: clipper, GeneNetworkBuilder, graphite, mmnet, NetPathMiner Package: RDAVIDWebService Version: 1.4.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: 1528ab82c1c752df153bc2724fbf402c NeedsCompilation: no Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ source.ver: src/contrib/RDAVIDWebService_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RDAVIDWebService_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RDAVIDWebService_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RDAVIDWebService_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RDAVIDWebService_1.4.0.tgz vignettes: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.pdf vignetteTitles: RDAVIDWebService: a versatile R interface to DAVID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.R dependsOnMe: CompGO suggestsMe: FGNet Package: Rdisop Version: 1.26.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: d638497069e41af39e5e5cb757d6d0ac NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: MassSpectrometry Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None source.ver: src/contrib/Rdisop_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rdisop_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rdisop_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rdisop_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rdisop_1.26.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Rdisop/inst/doc/Rdisop.R suggestsMe: MSnbase Package: RDRToolbox Version: 1.16.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 6f6626cb2ea42fa777f0ca98871d7b78 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen source.ver: src/contrib/RDRToolbox_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RDRToolbox_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RDRToolbox_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RDRToolbox_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RDRToolbox_1.16.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R Package: ReactomePA Version: 1.10.1 Imports: DOSE, AnnotationDbi, reactome.db, org.Hs.eg.db, igraph, graphite Suggests: clusterProfiler, GOSemSim, ChIPseeker, knitr License: GPL-2 MD5sum: cafbeb8ae64421decd2e0638f045e9e2 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment Author: Guangchuang Yu Maintainer: Guangchuang Yu VignetteBuilder: knitr source.ver: src/contrib/ReactomePA_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ReactomePA_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ReactomePA_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ReactomePA_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ReactomePA_1.10.1.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.pdf vignetteTitles: ReactomePA - an R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R suggestsMe: ChIPseeker, clusterProfiler, DOSE, GOSemSim Package: ReadqPCR Version: 1.12.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: 4fe9ae15e3ff1e45a396f5baeb1d99f6 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html source.ver: src/contrib/ReadqPCR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ReadqPCR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ReadqPCR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ReadqPCR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ReadqPCR_1.12.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR Package: reb Version: 1.44.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: 7985dff2af3132957b0f7e5cd2ac6c08 NeedsCompilation: yes Title: Regional Expression Biases Description: A set of functions to dentify regional expression biases biocViews: Microarray, CopyNumberVariation, Visualization Author: Kyle A. Furge and Karl Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/reb_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/reb_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/reb_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/reb_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/reb_1.44.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reb/inst/doc/reb.R Package: RedeR Version: 1.14.10 Depends: R (>= 2.15), methods, igraph Imports: RCurl, XML Suggests: PANR, pvclust License: GPL (>= 2) MD5sum: 7a0db6038f2038ab71e956e7fabdf5ef NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 6) source.ver: src/contrib/RedeR_1.14.10.tar.gz win.binary.ver: bin/windows/contrib/3.1/RedeR_1.14.10.zip win64.binary.ver: bin/windows64/contrib/3.1/RedeR_1.14.10.zip mac.binary.ver: bin/macosx/contrib/3.1/RedeR_1.14.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RedeR_1.14.10.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.pdf vignetteTitles: Main vignette: interactive visualization and manipulation of nested networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R importsMe: RTN suggestsMe: PANR Package: REDseq Version: 1.12.0 Depends: R (>= 2.15.0), BiocGenerics (>= 0.1.0), BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: BiocGenerics, AnnotationDbi, Biostrings, ChIPpeakAnno, graphics, IRanges (>= 1.13.5), multtest, stats, utils License: GPL (>=2) MD5sum: d50e6d181f2df06a34f5701ea1cc8012 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu and Thomas Fazzio Maintainer: Lihua Julie Zhu source.ver: src/contrib/REDseq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/REDseq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/REDseq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/REDseq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/REDseq_1.12.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R Package: RefNet Version: 1.2.1 Depends: R (>= 2.15.0), methods, IRanges, PSICQUIC, AnnotationHub, RCurl, shiny Imports: BiocGenerics Suggests: RUnit, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 9149af654dd543ee818bfc9d25d2ff1c NeedsCompilation: no Title: A queryable collection of molecular interactions, from many sources Description: Molecular interactions with metadata, some archived, some dynamically obtained biocViews: GraphAndNetwork Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/RefNet_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/RefNet_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.1/RefNet_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.1/RefNet_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RefNet_1.2.1.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefNet/inst/doc/RefNet.R Package: RefPlus Version: 1.36.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 0ea369fc02353a706afb61433ae419ee NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang source.ver: src/contrib/RefPlus_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RefPlus_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RefPlus_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RefPlus_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RefPlus_1.36.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R Package: regionReport Version: 1.0.5 Depends: R(>= 3.1.1) Imports: derfinder (>= 1.0.0), derfinderPlot (>= 1.0.0), devtools (>= 1.6), GenomeInfoDb, GenomicRanges, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), mgcv, RColorBrewer, rmarkdown (>= 0.3.3) Suggests: biovizBase, Cairo, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 4bba8727b98468d4ec30f07141c323ca NeedsCompilation: no Title: Generate HTML reports for exploring a set of regions Description: Generate HTML reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage Author: Leonardo Collado-Torres [aut, cre], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/regionReport VignetteBuilder: knitr source.ver: src/contrib/regionReport_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/regionReport_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/regionReport_1.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/regionReport_1.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/regionReport_1.0.5.tgz vignettes: vignettes/regionReport/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/regionReport.R htmlDocs: vignettes/regionReport/inst/doc/regionReport.html htmlTitles: "Introduction to regionReport" Package: Repitools Version: 1.12.1 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: S4Vectors, IRanges (>= 1.20.0), GenomeInfoDb, GenomicRanges, GenomicAlignments, BSgenome, gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, parallel, Biostrings, Rsamtools, cluster, rtracklayer Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: 156d8f6bc9e6f0b05a3a26da254c2523 NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson source.ver: src/contrib/Repitools_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/Repitools_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.1/Repitools_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.1/Repitools_1.12.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Repitools_1.12.1.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R Package: ReportingTools Version: 2.6.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, License: Artistic-2.0 MD5sum: a16ccc2cac2df200913a52cf8ef4bd6f NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr source.ver: src/contrib/ReportingTools_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ReportingTools_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ReportingTools_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ReportingTools_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ReportingTools_2.6.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R htmlDocs: vignettes/ReportingTools/inst/doc/knitr.html htmlTitles: "Knitr and ReportingTools" importsMe: affycoretools suggestsMe: GSEABase, npGSEA Package: ReQON Version: 1.12.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: cdfcd8dc0273961b80dce4b92a0b6253 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 source.ver: src/contrib/ReQON_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ReQON_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ReQON_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ReQON_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ReQON_1.12.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R Package: rfPred Version: 1.4.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: 059e79e10d269d11b0a9b619e4fefabb NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to be connected on the Internet or to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet URL: http://www.sbim.fr/rfPred source.ver: src/contrib/rfPred_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rfPred_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rfPred_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rfPred_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rfPred_1.4.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R Package: rGADEM Version: 2.14.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, IRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: i386, x64 MD5sum: c6b3499b63070381ce82e5629a1373c8 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit source.ver: src/contrib/rGADEM_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rGADEM_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rGADEM_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rGADEM_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rGADEM_2.14.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: MotIV Package: RGalaxy Version: 1.10.0 Depends: XML, methods, tools, optparse, digest, Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: d0d38ae7ade5ddf03ad5a8314ae9d8d0 NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/RGalaxy_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RGalaxy_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RGalaxy_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RGalaxy_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RGalaxy_1.10.0.tgz vignettes: vignettes/RGalaxy/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R htmlDocs: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html htmlTitles: "Introduction to RGalaxy" Package: Rgraphviz Version: 2.10.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: 5473f25c75432528b55c14dfc42af22a NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) source.ver: src/contrib/Rgraphviz_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rgraphviz_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rgraphviz_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rgraphviz_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rgraphviz_2.10.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, gaucho, GOFunction, MineICA, mvGST, nem, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE importsMe: apComplex, biocGraph, CompGO, DEGraph, EnrichmentBrowser, facopy, GOFunction, hyperdraw, nem, OncoSimulR, paircompviz, pathview, qpgraph, RchyOptimyx, SplicingGraphs suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, ddgraph, DEGraph, flowCore, GeneNetworkBuilder, geneplotter, GlobalAncova, globaltest, GOstats, GSEABase, KEGGgraph, MLP, NCIgraph, oneChannelGUI, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, ToPASeq, topGO, vtpnet Package: RGSEA Version: 1.0.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: a4a24b505e2dc861fec79d798c26385f NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr source.ver: src/contrib/RGSEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RGSEA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RGSEA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RGSEA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RGSEA_1.0.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R Package: rhdf5 Version: 2.10.0 Depends: methods Imports: zlibbioc Suggests: bit64,BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: c21345778f7f2160a56324b1aeb65be1 NeedsCompilation: yes Title: HDF5 interface to R Description: This R/Bioconductor package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer, Gregoire Pau Maintainer: Bernd Fischer SystemRequirements: GNU make source.ver: src/contrib/rhdf5_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rhdf5_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rhdf5_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rhdf5_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rhdf5_2.10.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.pdf vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GENE.E, GSCA importsMe: GENE.E, h5vc Package: rHVDM Version: 1.32.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: c3061bbe23796e66063a9116d0cba3d2 NeedsCompilation: no Title: Hidden Variable Dynamic Modeling Description: A R implementation of HVDM (Genome Biol 2006, V7(3) R25) biocViews: Microarray, GraphAndNetwork, Transcription, Classification, NetworkInference Author: Martino Barenco Maintainer: Martino Barenco source.ver: src/contrib/rHVDM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rHVDM_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rHVDM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rHVDM_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rHVDM_1.32.0.tgz vignettes: vignettes/rHVDM/inst/doc/rHVDM.pdf vignetteTitles: rHVDM primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rHVDM/inst/doc/rHVDM.R Package: riboSeqR Version: 1.0.5 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Suggests: baySeq, BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 3ec846a6481b10142f3b549742b998fd NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments. Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/riboSeqR_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/riboSeqR_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.1/riboSeqR_1.0.5.zip mac.binary.ver: bin/macosx/contrib/3.1/riboSeqR_1.0.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/riboSeqR_1.0.5.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R Package: Ringo Version: 1.30.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: 1d1889868d5921e0d00242c4ffc5bba4 NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling source.ver: src/contrib/Ringo_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Ringo_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Ringo_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Ringo_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Ringo_1.30.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, Starr importsMe: Repitools Package: RIPSeeker Version: 1.6.0 Depends: R (>= 2.15), methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: 8517757778df067435cf68731d734a7e NeedsCompilation: no Title: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments Description: Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation. biocViews: Sequencing, RIPSeq Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/RIPSeeker_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RIPSeeker_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RIPSeeker_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RIPSeeker_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RIPSeeker_1.6.0.tgz vignettes: vignettes/RIPSeeker/inst/doc/RIPSeeker.pdf vignetteTitles: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPSeeker/inst/doc/RIPSeeker.R Package: Risa Version: 1.8.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: 662214ebe76986d56ee7ac62cf84a733 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran, ISA Team URL: http://isatab.sourceforge.net/ source.ver: src/contrib/Risa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Risa_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Risa_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Risa_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Risa_1.8.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R Package: RLMM Version: 1.28.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 058cfd53d5d1d807d04e19f85759d140 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) source.ver: src/contrib/RLMM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RLMM_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RLMM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RLMM_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RLMM_1.28.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R Package: Rmagpie Version: 1.22.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: dfe979f294dc4409de966e876eec4946 NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ source.ver: src/contrib/Rmagpie_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rmagpie_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rmagpie_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rmagpie_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rmagpie_1.22.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R Package: RMassBank Version: 1.8.1 Depends: Rcpp Imports: XML,RCurl,rjson, rcdk,yaml,mzR,methods Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, ontoCAT, RUnit License: Artistic-2.0 MD5sum: e6ca0a6cddf8edf0ea53a22bc4a63e2f NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel source.ver: src/contrib/RMassBank_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/RMassBank_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/RMassBank_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/RMassBank_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RMassBank_1.8.1.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.pdf, vignettes/RMassBank/inst/doc/RMassBankNonstandard.pdf, vignettes/RMassBank/inst/doc/RMassBankXCMS.pdf vignetteTitles: RMassBank walkthrough, RMassBank non-standard usage, RMassBank using XCMS walkthrough hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R Package: rMAT Version: 3.16.0 Depends: R(>= 2.9.0), BiocGenerics (>= 0.1.3), IRanges (>= 1.13.10), Biobase (>= 2.15.1), affxparser Imports: stats, methods, BiocGenerics, IRanges, Biobase, affxparser, stats4 Suggests: GenomeGraphs, rtracklayer License: Artistic-2.0 MD5sum: f750fd3c86e52c92acdeb6801b6a39b2 NeedsCompilation: yes Title: R implementation from MAT program to normalize and analyze tiling arrays and ChIP-chip data. Description: This package is an R version of the package MAT and contains functions to parse and merge Affymetrix BPMAP and CEL tiling array files (using C++ based Fusion SDK and Bioconductor package affxparser), normalize tiling arrays using sequence specific models, detect enriched regions from ChIP-chip experiments. Note: users should have GSL and GenomeGraphs installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Snow Leopard users can take advantage of increase speed with Grand Central Dispatch! biocViews: Microarray, Preprocessing Author: Charles Cheung and Arnaud Droit and Raphael Gottardo Maintainer: Arnaud Droit and Raphael Gottardo URL: http://www.rglab.org source.ver: src/contrib/rMAT_3.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/rMAT_3.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rMAT_3.16.0.tgz vignettes: vignettes/rMAT/inst/doc/rMAT.pdf vignetteTitles: The rMAT users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rMAT/inst/doc/rMAT.R Package: RmiR Version: 1.22.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: dd0f49b669a192dc6ee5e5dbf9a2f823 NeedsCompilation: no Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero source.ver: src/contrib/RmiR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RmiR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RmiR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RmiR_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RmiR_1.22.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R suggestsMe: oneChannelGUI Package: RNAinteract Version: 1.14.0 Depends: R (>= 2.12.0), abind, locfit, Biobase Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12) License: Artistic-2.0 MD5sum: 2a377c1e72b9a6ea6b35b8292be44cac NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer source.ver: src/contrib/RNAinteract_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RNAinteract_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RNAinteract_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RNAinteract_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RNAinteract_1.14.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R Package: RNAither Version: 2.14.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 00fa8264694ae6fae0bedfee1cf845f3 NeedsCompilation: no Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali source.ver: src/contrib/RNAither_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RNAither_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RNAither_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RNAither_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RNAither_2.14.0.tgz vignettes: vignettes/RNAither/inst/doc/vignetteRNAither.pdf vignetteTitles: RNAither,, an automated pipeline for the statistical analysis of high-throughput RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAither/inst/doc/vignetteRNAither.R Package: rnaSeqMap Version: 2.24.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: aeb1f5ad404cb6dcae76c3d854e8fbee NeedsCompilation: yes Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/rnaSeqMap_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rnaSeqMap_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rnaSeqMap_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rnaSeqMap_2.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rnaSeqMap_2.24.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.R dependsOnMe: ampliQueso Package: RNASeqPower Version: 1.6.0 License: LGPL (>=2) MD5sum: 8415ab665ff2b841df040e01cc5f03c9 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau source.ver: src/contrib/RNASeqPower_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RNASeqPower_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RNASeqPower_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RNASeqPower_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RNASeqPower_1.6.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R Package: Rnits Version: 1.0.0 Depends: R (>= 3.1.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: c1c393fe55fe769709eb78628bacb0da NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr source.ver: src/contrib/Rnits_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rnits_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rnits_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rnits_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rnits_1.0.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R Package: roar Version: 1.2.0 Depends: R (>= 3.0.1) Imports: GenomicRanges, GenomicAlignments(>= 0.99.4), methods, rtracklayer, S4Vectors Suggests: RUnit, BiocGenerics, RNAseqData.HNRNPC.bam.chr14 License: GPL-3 MD5sum: 9ddaacc16111477116486bca37a7901c NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ source.ver: src/contrib/roar_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/roar_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/roar_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/roar_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/roar_1.2.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R Package: ROC Version: 1.42.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 7dd19556eb60f9bd8573527e777101e2 NeedsCompilation: yes Title: utilities for ROC, with uarray focus Description: utilities for ROC, with uarray focus biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org source.ver: src/contrib/ROC_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ROC_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ROC_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ROC_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ROC_1.42.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.pdf vignetteTitles: ROC notes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROC/inst/doc/ROCnotes.R dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate Package: Roleswitch Version: 1.4.1 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: 5fd513132307a6f8772508577ded960a NeedsCompilation: no Title: Infer miRNA-mRNA interactions using paired expression data from a single sample Description: Infer Probabilities of MiRNA-mRNA Interaction Signature (ProMISe) using paired expression data from a single sample. Roleswitch operates in two phases by inferring the probability of mRNA (miRNA) being the targets ("targets") of miRNA (mRNA), taking into account the expression of all of the mRNAs (miRNAs) due to their potential competition for the same miRNA (mRNA). Due to dynamic miRNA repression in the cell, Roleswitch assumes that the total transcribed mRNA levels are higher than the observed (equilibrium) mRNA levels and iteratively updates the total transcription of each mRNA targets based on the above inference. NB: in the paper, we used ProMISe as both the model name and inferred score name. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/roleswitch.html source.ver: src/contrib/Roleswitch_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/Roleswitch_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/Roleswitch_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/Roleswitch_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Roleswitch_1.4.1.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Roleswitch/inst/doc/Roleswitch.R Package: Rolexa Version: 1.22.0 Depends: R (>= 2.9.0), graphics, grDevices, methods, ShortRead Imports: mclust, Biostrings, graphics, grDevices, IRanges, methods, ShortRead, stats Enhances: fork License: GPL-2 MD5sum: a76e737a693d3b87997b5e613ba60ad6 NeedsCompilation: no Title: Statistical analysis of Solexa sequencing data Description: Provides probabilistic base calling, quality checks and diagnostic plots for Solexa sequencing data biocViews: Sequencing, DataImport, Preprocessing, QualityControl Author: Jacques Rougemont, Arnaud Amzallag, Christian Iseli, Laurent Farinelli, Ioannis Xenarios, Felix Naef Maintainer: Jacques Rougemont source.ver: src/contrib/Rolexa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rolexa_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rolexa_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rolexa_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rolexa_1.22.0.tgz vignettes: vignettes/Rolexa/inst/doc/Rolexa-vignette.pdf vignetteTitles: Rolexa hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rolexa/inst/doc/Rolexa-vignette.R Package: rols Version: 1.8.0 Depends: methods Imports: XML, XMLSchema (>= 0.6.0), SSOAP (>= 0.8.0), Biobase, utils Suggests: xtable, GO.db, knitr (>= 1.1.0), BiocStyle License: GPL-2 MD5sum: 0698140e7d90f423fd7fca17fcea56d2 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: This package allows to query EBI's Ontology Lookup Service (OLS) using Simple Object Access Protocol (SOAP). biocViews: Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr source.ver: src/contrib/rols_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rols_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rols_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rols_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rols_1.8.0.tgz vignettes: vignettes/rols/inst/doc/rols.pdf vignetteTitles: The rols interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R suggestsMe: MSnbase Package: ROntoTools Version: 1.6.1 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>= 3) MD5sum: f171524ad20d20949d2d0544cb907a60 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sorin Draghici Maintainer: Calin Voichita source.ver: src/contrib/ROntoTools_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ROntoTools_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ROntoTools_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ROntoTools_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ROntoTools_1.6.1.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R Package: RPA Version: 1.22.0 Depends: R (>= 3.1.1), parallel, affy, methods Suggests: affydata License: BSD_2_clause + file LICENSE MD5sum: a96aab8c882ad9c971c4ca6badd9b9a0 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. Lahti et al. "Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays", TCBB/IEEE, 2011. http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.38 biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA source.ver: src/contrib/RPA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RPA_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RPA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RPA_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RPA_1.22.0.tgz vignettes: vignettes/RPA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RPA/inst/doc/RPA.R Package: RpsiXML Version: 2.8.0 Depends: methods, annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), XML (>= 2.4.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db,hom.Hs.inp.db, hom.Mm.inp.db, hom.Dm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db,Rgraphviz, ppiStats, ScISI License: LGPL-3 MD5sum: 0901313e26bc7ee1e4dec318d62c60a4 NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang, Stefan Wiemann, Marc Carlson, with contributions from Tony Chiang Maintainer: Jitao David Zhang URL: http://www.bioconductor.org source.ver: src/contrib/RpsiXML_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RpsiXML_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RpsiXML_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RpsiXML_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RpsiXML_2.8.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependsOnMe: ScISI importsMe: ScISI Package: rpx Version: 1.2.0 Depends: methods Imports: XML, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, BiocGenerics, RUnit, knitr License: GPL-2 MD5sum: b53bddeaccb2612cbaf740724146fc9b NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: This package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/rpx_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rpx_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rpx_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rpx_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rpx_1.2.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.pdf vignetteTitles: An interface to proteomics data repositories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: proteoQC Package: Rqc Version: 1.0.4 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics, Biostrings, IRanges, methods, reshape2, S4Vectors, knitr (>= 1.7), BiocStyle Suggests: rmarkdown License: GPL (>= 2) MD5sum: 0974a64251696fb79a5b70d353e3fd48 NeedsCompilation: no Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr source.ver: src/contrib/Rqc_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rqc_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.1/Rqc_1.0.4.zip mac.binary.ver: bin/macosx/contrib/3.1/Rqc_1.0.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rqc_1.0.4.tgz vignettes: vignettes/Rqc/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R htmlDocs: vignettes/Rqc/inst/doc/Rqc.html htmlTitles: "Rqc - Quality Control Tool for High-Throughput Sequencing Data" Package: rqubic Version: 1.12.0 Depends: methods, Biobase, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: cd90d42a5fae913c023fc726179e7031 NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Microarray, Clustering Author: Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang source.ver: src/contrib/rqubic_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rqubic_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rqubic_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rqubic_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rqubic_1.12.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R Package: rRDP Version: 1.0.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: dea293b69287844ea72270195ce83dce NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java source.ver: src/contrib/rRDP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rRDP_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rRDP_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rRDP_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rRDP_1.0.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R Package: RRHO Version: 1.6.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 705f17cab778b3162eccdde7b0148855 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt source.ver: src/contrib/RRHO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RRHO_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RRHO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RRHO_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RRHO_1.6.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R Package: Rsamtools Version: 1.18.3 Depends: methods, S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.19), XVector (>= 0.5.3), Biostrings (>= 2.33.11) Imports: utils, BiocGenerics (>= 0.1.3), zlibbioc, bitops LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, KEGG.db, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 6a65dfdc9e42c655cc56a6f2aad2ff8f NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities (see 'LICENCE') for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Herv\'e Pag\`es, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/Rsamtools_1.18.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rsamtools_1.18.3.zip win64.binary.ver: bin/windows64/contrib/3.1/Rsamtools_1.18.3.zip mac.binary.ver: bin/macosx/contrib/3.1/Rsamtools_1.18.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rsamtools_1.18.3.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.pdf vignetteTitles: An introduction to Rsamtools, Using samtools C libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, deepSNV, exomeCopy, exomePeak, GenomicAlignments, GenomicFiles, girafe, methylPipe, oneChannelGUI, qrqc, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, ShortRead, systemPipeR, TEQC, TitanCNA, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, annmap, ArrayExpressHTS, biovizBase, BSgenome, CAGEr, casper, CexoR, ChIPQC, cn.mops, CNVrd2, compEpiTools, csaw, customProDB, deepSNV, derfinder, DEXSeq, DNaseR, DOQTL, easyRNASeq, EDASeq, epigenomix, FourCSeq, FunciSNP, GenomicAlignments, GenomicInteractions, ggbio, GGtools, gmapR, Gviz, gwascat, h5vc, HTSeqGenie, MEDIPS, PICS, QDNAseq, QuasR, R453Plus1Toolbox, Rariant, Repitools, rtracklayer, SGSeq, tracktables, trackViewer, VariantFiltering, VariantTools suggestsMe: AnnotationHub, BaseSpaceR, BiocParallel, biomvRCNS, DiffBind, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, metaseqR, QuasR, seqbias, SigFuge, SplicingGraphs, Streamer Package: rsbml Version: 2.24.1 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: a1026e7615a01f174b97937204855ea6 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) source.ver: src/contrib/rsbml_2.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/rsbml_2.24.1.zip win64.binary.ver: bin/windows64/contrib/3.1/rsbml_2.24.1.zip mac.binary.ver: bin/macosx/contrib/3.1/rsbml_2.24.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rsbml_2.24.1.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR Package: rSFFreader Version: 0.14.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: 7e721bd2b772c6d3877086516b49c948 NeedsCompilation: yes Title: rSFFreader reads in sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers Description: rSFFreader reads sequence, qualities and clip point values from sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers into similar classes as are present for fastq files. biocViews: DataImport, Sequencing Author: Matt Settles , Sam Hunter, Brice Sarver, Ilia Zhbannikov, Kyu-Chul Cho Maintainer: Matt Settles source.ver: src/contrib/rSFFreader_0.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/rSFFreader_0.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rSFFreader_0.14.0.tgz vignettes: vignettes/rSFFreader/inst/doc/rSFFreader.pdf vignetteTitles: An introduction to rSFFreader hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSFFreader/inst/doc/rSFFreader.R suggestsMe: hiReadsProcessor Package: Rsubread Version: 1.16.1 License: GPL-3 MD5sum: 21b6f7776a18aadc20ffce92b1d9ee26 NeedsCompilation: yes Title: Rsubread package: high-performance read alignment, quantification and mutation discovery Description: This R package provides powerful and easy-to-use tools for analyzing next-gen sequencing read data. Functions of this package include quality assessment of sequence reads, read alignment, read summarization, exon-exon junction detection, fusion detection, detection of short and long indels, absolute expression calling and SNP calling. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA/HiSeq/MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM/Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, GeneExpression, GeneRegulation, Genetics, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, Software Author: Wei Shi and Yang Liao with contributions from Jenny Zhiyin Dai and Timothy Triche, Jr. Maintainer: Wei Shi URL: http://bioconductor.org/packages/release/bioc/html/Rsubread.html source.ver: src/contrib/Rsubread_1.16.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.1/Rsubread_1.16.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rsubread_1.16.1.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf vignetteTitles: Rsubread Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R Package: RSVSim Version: 1.6.1 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: adf14f9e5237b65d29754752a4ad5ee8 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen source.ver: src/contrib/RSVSim_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/RSVSim_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/RSVSim_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/RSVSim_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RSVSim_1.6.1.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R Package: rTANDEM Version: 1.6.1 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: d6b55439eb34041b25093ef70a7895ca NeedsCompilation: yes Title: Interfaces the tandem protein identification algorithm in R Description: This package interfaces the tandem protein identification algorithm in R. Identification can be launched in the X!Tandem style, by using as sole parameter the path to a parameter file. But rTANDEM aslo provides extended syntax and functions to streamline launching analyses, as well as function to convert results, parameters and taxonomy to/from R. A related package, shinyTANDEM, provides visualization interface for result objects. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Charles Joly Beauparlant , Rene Paradis , Arnaud Droit Maintainer: Frederic Fournier SystemRequirements: rTANDEM uses expat and pthread libraries. See the README file for details. source.ver: src/contrib/rTANDEM_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/rTANDEM_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/rTANDEM_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/rTANDEM_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rTANDEM_1.6.1.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rTANDEM/inst/doc/rTANDEM.R dependsOnMe: shinyTANDEM importsMe: proteoQC Package: RTCA Version: 1.18.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 1e7138002d7a0b6ce9d8eae50568ed96 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming source.ver: src/contrib/RTCA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RTCA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RTCA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RTCA_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RTCA_1.18.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R Package: RTN Version: 1.4.1 Depends: R (>= 2.15), methods, igraph Imports: RedeR, minet, snow, limma, data.table, ff, car, IRanges Suggests: HTSanalyzeR, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 6a8cdaafa1aea06ecefab4a5a401846a NeedsCompilation: no Title: Reconstruction of transcriptional networks and analysis of master regulators Description: This package provides classes and methods for transcriptional network inference and analysis. Modulators of transcription factor activity are assessed by conditional mutual information, and master regulators are mapped to phenotypes using different strategies, e.g., gene set enrichment, shadow and synergy analyses. Additionally, master regulators can be linked to genetic markers using eQTL/VSE analysis, taking advantage of the haplotype block structure mapped to the human genome in order to explore risk-associated SNPs identified in GWAS studies. biocViews: NetworkInference, NetworkAnalysis, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment,GeneticVariability,SNP Author: Mauro Castro, Xin Wang, Michael Fletcher, Florian Markowetz and Kerstin Meyer Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 source.ver: src/contrib/RTN_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/RTN_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.1/RTN_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.1/RTN_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RTN_1.4.1.tgz vignettes: vignettes/RTN/inst/doc/RTN.pdf vignetteTitles: Main vignette: reconstruction and analysis of transcriptional networks in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R Package: RTopper Version: 1.12.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 168cf12cf6fe0a01d75b89a0d6a90dfc NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni source.ver: src/contrib/RTopper_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RTopper_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RTopper_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RTopper_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RTopper_1.12.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R Package: rtracklayer Version: 1.26.3 Depends: R (>= 2.10), methods, GenomicRanges (>= 1.17.39) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.3), IRanges (>= 1.99.28), XVector (>= 0.5.8), GenomeInfoDb (>= 1.1.19), Biostrings (>= 2.33.14), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.17.8), GenomicAlignments (>= 1.1.16), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 920143f742c911e0fcbd3d21695644fd NeedsCompilation: yes Title: R interface to genome browsers and their annotation tracks Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/rtracklayer_1.26.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/rtracklayer_1.26.3.zip win64.binary.ver: bin/windows64/contrib/3.1/rtracklayer_1.26.3.zip mac.binary.ver: bin/macosx/contrib/3.1/rtracklayer_1.26.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rtracklayer_1.26.3.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, cummeRbund, exomePeak, GenomicFiles, groHMM, metagene, MethylSeekR, RIPSeeker, spliceR importsMe: ballgown, BiSeq, BSgenome, CAGEr, casper, CexoR, ChIPseeker, ChromHeatMap, CNEr, CompGO, customProDB, derfinder, FourCSeq, FunciSNP, GenomicFeatures, GenomicInteractions, ggbio, GGtools, gmapR, GOTHiC, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, MEDIPS, methyAnalysis, MotifDb, proBAMr, Repitools, roar, seqplots, SGSeq, TFBSTools, trackViewer, VariantAnnotation, VariantTools, wavClusteR suggestsMe: biovizBase, compEpiTools, GenomicAlignments, GenomicRanges, goseq, interactiveDisplay, metaseqR, methylumi, MotIV, NarrowPeaks, oneChannelGUI, PICS, PING, QuasR, R453Plus1Toolbox, Ringo, rMAT, RSVSim, triplex, TSSi Package: Rtreemix Version: 1.28.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: 4b7e42899225fcfcee05f32db14f6dd8 NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska source.ver: src/contrib/Rtreemix_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Rtreemix_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Rtreemix_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Rtreemix_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Rtreemix_1.28.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R Package: rTRM Version: 1.4.0 Depends: R (>= 2.10), igraph (>= 0.7), RSQLite Imports: AnnotationDbi Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: e38d3a7c004bd28595ff19e4ba684ffc NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr source.ver: src/contrib/rTRM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rTRM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rTRM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rTRM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rTRM_1.4.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui Package: rTRMui Version: 1.4.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 2dbd3c6c23cada5b61117dd088b9f5d5 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez source.ver: src/contrib/rTRMui_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/rTRMui_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/rTRMui_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/rTRMui_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/rTRMui_1.4.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R Package: RUVnormalize Version: 1.0.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: cf9a2a2bbd0a88a74fa3659d6c3fa9d1 NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob source.ver: src/contrib/RUVnormalize_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RUVnormalize_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RUVnormalize_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RUVnormalize_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RUVnormalize_1.0.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R Package: RUVSeq Version: 1.0.0 Depends: EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq License: Artistic-2.0 MD5sum: 7ff131ccf29702594b567164284e6383 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso and Sandrine Dudoit Maintainer: Davide Risso VignetteBuilder: knitr source.ver: src/contrib/RUVSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/RUVSeq_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/RUVSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/RUVSeq_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/RUVSeq_1.0.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R Package: RWebServices Version: 1.30.0 Depends: SJava (>= 0.85), TypeInfo, methods, tools (>= 2.10.0), R (>= 2.5.0) Imports: RCurl, SJava License: file LICENSE License_restricts_use: no MD5sum: 3111ab74b53c508ced622af55cb6356a NeedsCompilation: yes Title: Expose R functions as web services through Java/Axis/Apache Description: This package provides mechanisms for automatic function prototyping and exposure of R functionality in a web services environment. biocViews: Infrastructure Author: Nianhua Li, MT Morgan Maintainer: Martin Morgan source.ver: src/contrib/RWebServices_1.30.0.tar.gz vignettes: vignettes/RWebServices/inst/doc/EnablingPackages.pdf, vignettes/RWebServices/inst/doc/InstallingAndTesting.pdf, vignettes/RWebServices/inst/doc/LessonsLearned.pdf, vignettes/RWebServices/inst/doc/RelatedWork.pdf, vignettes/RWebServices/inst/doc/RToJava.pdf vignetteTitles: Enabling packages as web services, Installing and testing RWebServices and enabled packages, Lessons learned exposing web services, RelatedWork, From R to Java hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RWebServices/inst/doc/EnablingPackages.R, vignettes/RWebServices/inst/doc/InstallingAndTesting.R, vignettes/RWebServices/inst/doc/LessonsLearned.R, vignettes/RWebServices/inst/doc/RelatedWork.R, vignettes/RWebServices/inst/doc/RToJava.R Package: S4Vectors Version: 0.4.0 Depends: R (>= 3.1.0), methods, utils, stats, stats4, BiocGenerics (>= 0.11.3) Imports: methods, utils, stats, stats4, BiocGenerics Suggests: IRanges, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 4b6dc2842ea39b05d6a60bb013bc4826 NeedsCompilation: yes Title: S4 implementation of vectors and lists Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pages, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/S4Vectors_0.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/S4Vectors_0.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/S4Vectors_0.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/S4Vectors_0.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/S4Vectors_0.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AnnotationHub, Biostrings, BiSeq, BSgenome, bumphunter, CexoR, chipseq, ChIPseqR, CSAR, DESeq2, DirichletMultinomial, DNaseR, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, girafe, groHMM, Gviz, IRanges, meshr, methyAnalysis, MotifDb, OTUbase, plethy, Rsamtools, segmentSeq, SplicingGraphs, triplex, VariantTools, XVector importsMe: AllelicImbalance, AnnotationDbi, AnnotationForge, ballgown, biovizBase, BiSeq, BitSeq, BSgenome, casper, ChIPQC, ChIPseeker, CNEr, compEpiTools, copynumber, csaw, DECIPHER, derfinder, derfinderHelper, DiffBind, easyRNASeq, epivizr, GenomeInfoDb, GenomicAlignments, GenomicTuples, genoset, ggbio, GGtools, gmapR, GOTHiC, gwascat, h5vc, kebabs, methylPipe, methylumi, minfi, MinimumDistance, mygene, NarrowPeaks, nucleR, oligoClasses, Pbase, pdInfoBuilder, polyester, qcmetrics, qpgraph, QuasR, Rariant, Rcade, Repitools, roar, Rqc, rtracklayer, SeqArray, seqplots, SGSeq, ShortRead, SomaticSignatures, TFBSTools, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, XVector suggestsMe: BiocGenerics Package: safe Version: 3.6.1 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: e369ef9eea03c85341295c4f63f63ca7 NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: William T. Barry source.ver: src/contrib/safe_3.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/safe_3.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/safe_3.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/safe_3.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/safe_3.6.1.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R importsMe: EnrichmentBrowser Package: sagenhaft Version: 1.36.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: ae492e05ac4db581af3656c571d86e5e NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth and Lavinia Hyde . Maintainer: Tim Beissbarth URL: http://tagcalling.mbgproject.org source.ver: src/contrib/sagenhaft_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sagenhaft_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sagenhaft_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sagenhaft_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sagenhaft_1.36.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R Package: SAGx Version: 1.40.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 98364b219e8ac0b25c3e9c8bf1aefc6e NeedsCompilation: yes Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html source.ver: src/contrib/SAGx_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SAGx_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SAGx_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SAGx_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SAGx_1.40.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAGx/inst/doc/samroc-ex.R Package: SamSPECTRAL Version: 1.20.0 Depends: R (>= 2.10) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: bbaab512952b3c132c7b0e805602aca6 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data. Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data sample. For instructions on manual installation, refer to the PDF file provided in the following documentation. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV Author: Habil Zare and Parisa Shooshtari Maintainer: Habil Zare source.ver: src/contrib/SamSPECTRAL_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SamSPECTRAL_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SamSPECTRAL_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SamSPECTRAL_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SamSPECTRAL_1.20.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R Package: sangerseqR Version: 1.3.4 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 0760b2c22c8eefe6f4ea9807f778f809 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr source.ver: src/contrib/sangerseqR_1.3.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/sangerseqR_1.3.4.zip win64.binary.ver: bin/windows64/contrib/3.1/sangerseqR_1.3.4.zip mac.binary.ver: bin/macosx/contrib/3.1/sangerseqR_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sangerseqR_1.3.4.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R Package: SANTA Version: 2.2.0 Depends: R (>= 2.14), igraph Imports: Matrix, snow, stringr Suggests: RUnit, BiocGenerics, knitr, knitcitations, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) Archs: i386, x64 MD5sum: f2b831374d4c78e450a082b6d611aa35 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex J. Cornish and Florian Markowetz Maintainer: Alex J. Cornish VignetteBuilder: knitr source.ver: src/contrib/SANTA_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SANTA_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SANTA_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SANTA_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SANTA_2.2.0.tgz vignettes: vignettes/SANTA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R, vignettes/SANTA/inst/doc/SANTA.R htmlDocs: vignettes/SANTA/inst/doc/SANTA-vignette.html htmlTitles: "Introduction to SANTA" Package: sapFinder Version: 1.4.0 Depends: R (>= 3.0.0),rTANDEM (>= 1.3.5) Imports: pheatmap,Rcpp (>= 0.10.6),graphics,grDevices,stats, utils LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: ccf86b2f3d2b67126d8bad35797cced4 NeedsCompilation: yes Title: A package for variant peptides detection and visualization in shotgun proteomics. Description: sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics. biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen source.ver: src/contrib/sapFinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sapFinder_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sapFinder_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sapFinder_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sapFinder_1.4.0.tgz vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sapFinder/inst/doc/sapFinder.R Package: savR Version: 1.4.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo License: AGPL-3 MD5sum: 3a2460722cc0c5411d2c1551ad6d42e7 NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR source.ver: src/contrib/savR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/savR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/savR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/savR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/savR_1.4.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R Package: SBMLR Version: 1.62.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 341297d610edf77fd9f4284d35f2c106 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html source.ver: src/contrib/SBMLR_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SBMLR_1.62.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SBMLR_1.62.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SBMLR_1.62.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SBMLR_1.62.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R Package: SCAN.UPC Version: 2.8.1 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 04578ab035b5cd7a012556636da8f014 NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc source.ver: src/contrib/SCAN.UPC_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/SCAN.UPC_2.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/SCAN.UPC_2.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/SCAN.UPC_2.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SCAN.UPC_2.8.1.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R Package: ScISI Version: 1.38.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL MD5sum: 0aca1e39c59646edc190a4f9554bf3e5 NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang source.ver: src/contrib/ScISI_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ScISI_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ScISI_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ScISI_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ScISI_1.38.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML Package: scsR Version: 1.3.2 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 MD5sum: a8bd50886e3abbe264a0f2df40883e8d NeedsCompilation: no Title: SiRNA correction for seed mediated off-target effect Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package. biocViews: Preprocessing Author: Andrea Franceschini Maintainer: Andrea Franceschini , Roger Meier , Christian von Mering source.ver: src/contrib/scsR_1.3.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/scsR_1.3.2.zip win64.binary.ver: bin/windows64/contrib/3.1/scsR_1.3.2.zip mac.binary.ver: bin/macosx/contrib/3.1/scsR_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/scsR_1.3.2.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scsR/inst/doc/scsR.R Package: segmentSeq Version: 2.0.1 Depends: R (>= 2.3.0), methods, baySeq (>= 1.99.0), ShortRead, GenomicRanges, IRanges, S4Vectors Imports: graphics, grDevices, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 9e3e24410a29c718ad54d6fab08e8ef2 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/segmentSeq_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/segmentSeq_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/segmentSeq_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/segmentSeq_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/segmentSeq_2.0.1.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R Package: SemDist Version: 1.0.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 00df72e8af46ecd3f37efc4c6fba1eb7 NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist source.ver: src/contrib/SemDist_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SemDist_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SemDist_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SemDist_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SemDist_1.0.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R Package: SeqArray Version: 1.6.1 Depends: gdsfmt (>= 1.1.0) Imports: methods, Biostrings, GenomicRanges, IRanges, S4Vectors, VariantAnnotation LinkingTo: gdsfmt Suggests: parallel, BiocStyle, BiocGenerics, RUnit, Rcpp License: GPL-3 Archs: i386, x64 MD5sum: a0e4150bfe3a7dd730a9fbc8e4f9e137 NeedsCompilation: yes Title: Big Data Management of Genome-wide Sequencing Variants Description: Big data management of genome-wide variants using the CoreArray C++ library: genotypic data and annotations are stored in an array-oriented manner, offering efficient access of genetic variants using the S programming language. biocViews: Infrastructure, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [aut], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/tutorials/SeqArray/, http://github.com/zhengxwen/SeqArray source.ver: src/contrib/SeqArray_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/SeqArray_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.1/SeqArray_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.1/SeqArray_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SeqArray_1.6.1.tgz vignettes: vignettes/SeqArray/inst/doc/SeqArray-JSM2013.pdf, vignettes/SeqArray/inst/doc/SeqArrayTutorial.pdf vignetteTitles: SeqArray-JSM2013.pdf, SeqArray hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SeqVarTools Package: seqbias Version: 1.14.0 Depends: R (>= 2.13.0), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods Imports: zlibbioc LinkingTo: Rsamtools Suggests: Rsamtools, ggplot2 License: LGPL-3 Archs: i386, x64 MD5sum: 33e91f2fff914ef105e2dfcfc18bc347 NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones source.ver: src/contrib/seqbias_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/seqbias_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/seqbias_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/seqbias_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/seqbias_1.14.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON Package: seqCNA Version: 1.10.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 Archs: i386, x64 MD5sum: 7e86832e3a66dafc898c1965e8463962 NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools source.ver: src/contrib/seqCNA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/seqCNA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/seqCNA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/seqCNA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/seqCNA_1.10.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf vignetteTitles: seqCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R Package: SeqGSEA Version: 1.6.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: fd82c681dc47e5dbbc3169545329be42 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression Author: Xi Wang Maintainer: Xi Wang source.ver: src/contrib/SeqGSEA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SeqGSEA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SeqGSEA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SeqGSEA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SeqGSEA_1.6.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R Package: seqLogo Version: 1.32.1 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: 556b4ab468f3bdd4585b3a44bf8a716f NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom Maintainer: Oliver Bembom source.ver: src/contrib/seqLogo_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/seqLogo_1.32.1.zip win64.binary.ver: bin/windows64/contrib/3.1/seqLogo_1.32.1.zip mac.binary.ver: bin/macosx/contrib/3.1/seqLogo_1.32.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/seqLogo_1.32.1.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: motifRG, rGADEM importsMe: PWMEnrich, rGADEM, TFBSTools suggestsMe: BCRANK, MotifDb Package: seqplots Version: 1.2.0 Depends: R (>= 3.1.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.11.0), DBI, RSQLite, RJSONIO, plotrix, fields, grid, kohonen, Cairo, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra Suggests: testthat, BiocStyle, knitr License: GPL-3 MD5sum: 4f6b2480256787cfd234f655f9cb2516 NeedsCompilation: no Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps. Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr source.ver: src/contrib/seqplots_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/seqplots_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/seqplots_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/seqplots_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/seqplots_1.2.0.tgz vignettes: vignettes/seqplots/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R htmlDocs: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html htmlTitles: "SeqPlots GUI QuickStart", "SeqPlots GUI manual" Package: seqTools Version: 1.0.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 1e09ee9a3a59ec16be7cbdd640aea3fd NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files. Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/seqTools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/seqTools_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/seqTools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/seqTools_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/seqTools_1.0.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R Package: SeqVarTools Version: 1.4.0 Depends: SeqArray (>= 1.1.1) Imports: methods, GenomicRanges, IRanges, GWASExactHW Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 1be8a61681e3a188061dd962fa8ba81e NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng Maintainer: Stephanie M. Gogarten , Xiuwen Zheng source.ver: src/contrib/SeqVarTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SeqVarTools_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SeqVarTools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SeqVarTools_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SeqVarTools_1.4.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Introduction to SeqVarTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/SeqVarTools.R Package: SGSeq Version: 1.0.6 Depends: BiocParallel, GenomicRanges, IRanges, methods Imports: AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, igraph, parallel, Rsamtools, rtracklayer, S4Vectors (>= 0.2.3) Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 8ed99eb5004b78f6d8973058778bbbeb NeedsCompilation: no Title: Prediction, quantification and visualization of alternative transcript events from RNA-seq data Description: RNA-seq data are informative for the analysis of known and novel transcript isoforms. While the short length of RNA-seq reads limits the ability to predict and quantify full-length transcripts, short read data are well suited for the analysis of individual alternative transcripts events (e.g. inclusion or skipping of a cassette exon). The SGSeq package enables the prediction, quantification and visualization of alternative transcript events from BAM files. biocViews: AlternativeSplicing, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr source.ver: src/contrib/SGSeq_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.1/SGSeq_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.1/SGSeq_1.0.6.zip mac.binary.ver: bin/macosx/contrib/3.1/SGSeq_1.0.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SGSeq_1.0.6.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.pdf vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R Package: shinyMethyl Version: 1.0.2 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.9.1), minfi (>= 1.6.0), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: d8627e6dc3c384630768285673c52496 NeedsCompilation: no Title: Interactive visualization for Illumina's 450k methylation arrays Description: Interactive tool for visualizing Illumina's 450k array data biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr source.ver: src/contrib/shinyMethyl_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/shinyMethyl_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/shinyMethyl_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/shinyMethyl_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/shinyMethyl_1.0.2.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R Package: shinyTANDEM Version: 1.4.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: b9bc032cacb7062295e958065568a776 NeedsCompilation: no Title: Provides a GUI for rTANDEM Description: This package provides a GUI interface for rTANDEM. The GUI is primarily designed to visualize rTANDEM result object or result xml files. But it will also provides an interface for creating parameter objects, launching searches or performing conversions between R objects and xml files. biocViews: MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier source.ver: src/contrib/shinyTANDEM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/shinyTANDEM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/shinyTANDEM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/shinyTANDEM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/shinyTANDEM_1.4.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.R Package: ShortRead Version: 1.24.0 Depends: BiocGenerics (>= 0.11.3), BiocParallel, Biostrings (>= 2.33.14), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.28) Imports: Biobase, S4Vectors (>= 0.2.2), IRanges (>= 1.99.27), GenomeInfoDb (>= 1.1.19), GenomicRanges (>= 1.17.39), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: e2f132765e1a2cf029d9a438bd8f64fe NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/ShortRead_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ShortRead_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ShortRead_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ShortRead_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ShortRead_1.24.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: Basic4Cseq, chipseq, ChIPseqR, EDASeq, girafe, HTSeqGenie, nucleR, OTUbase, Rolexa, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: ArrayExpressHTS, BEAT, chipseq, ChIPseqR, ChIPsim, easyRNASeq, GOTHiC, nucleR, QuasR, R453Plus1Toolbox, Rolexa, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, Genominator, PICS, PING, Repitools, Rsamtools Package: sigaR Version: 1.10.0 Depends: Biobase, CGHbase, methods, mvtnorm, penalized Imports: corpcor (>= 1.6.2), graphics, igraph, marray, MASS, mvtnorm, quadprog, penalized (>= 0.9-39), snowfall, stats License: GPL (>= 2) MD5sum: b3e973cba72696986ead55f883df03f2 NeedsCompilation: no Title: statistics for integrative genomics analyses in R Description: Facilites the joint analysis of high-throughput data from multiple molecular levels. Contains functions for manipulation of objects, various analysis types, and some visualization. biocViews: Microarray, DifferentialExpression, aCGH, GeneExpression, Pathways Author: Wessel N. van Wieringen Maintainer: Wessel N. van Wieringen URL: http://www.few.vu.nl/~wvanwie source.ver: src/contrib/sigaR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sigaR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sigaR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sigaR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sigaR_1.10.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigaR/inst/doc/sigaR.R dependsOnMe: HCsnip Package: SigCheck Version: 1.0.2 Depends: R (>= 3.1.0), MLInterfaces, Biobase, e1071, BiocParallel Imports: graphics, stats, utils Suggests: BiocStyle, breastCancerNKI License: Artistic-2.0 MD5sum: df2ec287bbfdf250d0f1bf40e4526870 NeedsCompilation: no Title: Check a gene signature's classification performance against random signatures, permuted data, and known signatures. Description: While gene signatures are frequently used to classify data (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based partly on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its classification performance (using the MLInterfaces package) against a) random gene signatures of the same length; b) known, (related and unrelated) gene signatures; and c) permuted data. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Justin Norden and Rory Stark Maintainer: Rory Stark source.ver: src/contrib/SigCheck_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/SigCheck_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.1/SigCheck_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.1/SigCheck_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SigCheck_1.0.2.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R Package: SigFuge Version: 1.4.0 Depends: R (>= 3.1.1), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: d629dadeb6f9c7dbda105a5af21b4fe6 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes source.ver: src/contrib/SigFuge_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SigFuge_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SigFuge_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SigFuge_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SigFuge_1.4.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R Package: siggenes Version: 1.40.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: fff0253db09b4f351f7fcc851f2dbda1 NeedsCompilation: no Title: Multiple testing using SAM and Efron's empirical Bayes approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender source.ver: src/contrib/siggenes_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/siggenes_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/siggenes_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/siggenes_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/siggenes_1.40.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf vignetteTitles: siggenes Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart, oneChannelGUI importsMe: charm, GeneSelector, minfi suggestsMe: GeneSelector, logicFS, trio, XDE Package: sigPathway Version: 1.34.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 Archs: i386, x64 MD5sum: 488aea0a98b52ae34d7f15b8a42df4ac NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html source.ver: src/contrib/sigPathway_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sigPathway_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sigPathway_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sigPathway_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sigPathway_1.34.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome Package: SIM Version: 1.36.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: 4941e413a1b619a18b39fef379c46cc6 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes source.ver: src/contrib/SIM_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SIM_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SIM_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SIM_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SIM_1.36.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R Package: SimBindProfiles Version: 1.4.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: e942cc0167805aebd61f0ec580421669 NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer source.ver: src/contrib/SimBindProfiles_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SimBindProfiles_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SimBindProfiles_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SimBindProfiles_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SimBindProfiles_1.4.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R Package: simpleaffy Version: 2.42.0 Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma License: GPL (>= 2) Archs: i386, x64 MD5sum: f28c4225a6e38785526c97627a92abfb NeedsCompilation: yes Title: Very simple high level analysis of Affymetrix data Description: Provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. Makes heavy use of the affy library. Also has some basic scatter plot functions and mechanisms for generating high resolution journal figures... biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Transcription, DataImport, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Crispin J Miller Maintainer: Crispin Miller URL: http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/ source.ver: src/contrib/simpleaffy_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/simpleaffy_2.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/simpleaffy_2.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/simpleaffy_2.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/simpleaffy_2.42.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleaffy/inst/doc/simpleAffy.R dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW Package: simulatorZ Version: 1.0.0 Depends: R (>= 3.1), Biobase, survival, CoxBoost Imports: GenomicRanges, gbm, Hmisc, stats, graphics Suggests: BiocGenerics, RUnit, BiocStyle, curatedOvarianData, parathyroidSE, superpc License: Artistic-2.0 Archs: i386, x64 MD5sum: 9a6bb286eeb4bf49d9b50176907c64c9 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSets and SummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ source.ver: src/contrib/simulatorZ_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/simulatorZ_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/simulatorZ_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/simulatorZ_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/simulatorZ_1.0.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.R Package: sizepower Version: 1.36.0 Depends: stats License: LGPL MD5sum: 33dc0a7516da24ba529de1fde89d9b9b NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu source.ver: src/contrib/sizepower_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sizepower_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sizepower_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sizepower_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sizepower_1.36.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R suggestsMe: oneChannelGUI Package: SJava Version: 0.92.1 Depends: R (>= 2.10.0), methods License: GPL (>= 2) MD5sum: cf7f8acd84eef15c630738a14190d132 NeedsCompilation: yes Title: The Omegahat interface for R and Java. Description: An interface from R to Java to create and call Java objects and methods. biocViews: Infrastructure Author: Duncan Temple Lang, John Chambers Maintainer: Martin Morgan source.ver: src/contrib/SJava_0.92.1.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RWebServices importsMe: RWebServices Package: SLGI Version: 1.26.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 1fe78de02895883bd62ef7235061b6d4 NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur source.ver: src/contrib/SLGI_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SLGI_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SLGI_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SLGI_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SLGI_1.26.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependsOnMe: PCpheno Package: SLqPCR Version: 1.32.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 906583b8a8cb4a38f56a51558e3fc403 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl source.ver: src/contrib/SLqPCR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SLqPCR_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SLqPCR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SLqPCR_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SLqPCR_1.32.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R suggestsMe: EasyqpcR Package: SMAP Version: 1.30.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 516f8630af33d9cf13f6befaa44ea9cc NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson source.ver: src/contrib/SMAP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SMAP_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SMAP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SMAP_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SMAP_1.30.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R Package: SNAGEE Version: 1.6.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 44709a41d97b3325d7e13cd66aced654 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ source.ver: src/contrib/SNAGEE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SNAGEE_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SNAGEE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SNAGEE_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SNAGEE_1.6.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R Package: snapCGH Version: 1.36.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 37c7653b6276046e798754d45d7591e3 NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data. Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni source.ver: src/contrib/snapCGH_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/snapCGH_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.1/snapCGH_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.1/snapCGH_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/snapCGH_1.36.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP Package: snm Version: 1.14.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 63e38825dd8756b33ae72d32f1c96502 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/snm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/snm_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/snm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/snm_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/snm_1.14.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R Package: SNPchip Version: 2.12.0 Depends: R (>= 2.14.0) Imports: graphics, lattice, grid, foreach, utils, methods, oligoClasses (>= 1.21.12), Biobase, GenomicRanges, IRanges, GenomeInfoDb Suggests: crlmm (>= 1.17.14), RUnit Enhances: doSNOW, VanillaICE (>= 1.21.24), RColorBrewer License: LGPL (>= 2) MD5sum: c041366c40b0d113c3a29c9ebe295ff0 NeedsCompilation: no Title: Visualizations for copy number alterations Description: This package defines methods for visualizing high-throughput genomic data biocViews: CopyNumberVariation, SNP, GeneticVariability, Visualization Author: Robert Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf URL: http://www.biostat.jhsph.edu/~iruczins/software/snpchip.html source.ver: src/contrib/SNPchip_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SNPchip_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SNPchip_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SNPchip_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SNPchip_2.12.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPchip/inst/doc/PlottingIdiograms.R dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: Category, MinimumDistance, VanillaICE Package: SNPRelate Version: 1.0.1 Depends: R (>= 2.14), gdsfmt (>= 1.2.2) LinkingTo: gdsfmt Suggests: parallel, RUnit, lattice, BiocStyle, BiocGenerics, knitr License: GPL-3 Archs: i386, x64 MD5sum: 3e4af7813cb2ea591a934d6defa6a75a NeedsCompilation: yes Title: Parallel computing toolset for relatedness and principal component analysis of SNP data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP format in this package is also used by the GWASTools package with the support of S4 classes and generic functions. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/tutorials/SNPRelate/ VignetteBuilder: knitr source.ver: src/contrib/SNPRelate_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/SNPRelate_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/SNPRelate_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/SNPRelate_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SNPRelate_1.0.1.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.pdf vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.R suggestsMe: GWASTools Package: snpStats Version: 1.16.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 0f6f65b622aea4469503258acb67e911 NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies, extending the snpMatrix package biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton source.ver: src/contrib/snpStats_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/snpStats_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/snpStats_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/snpStats_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/snpStats_1.16.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/differences.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: GGBase importsMe: FunciSNP, GGtools, gwascat suggestsMe: crlmm, GWASTools, VariantAnnotation Package: SomatiCA Version: 1.10.0 Depends: R (>= 2.14.0), lars, DNAcopy, foreach, methods, rebmix, GenomicRanges, IRanges, doParallel Imports: foreach, lars, sn, DNAcopy, methods, rebmix, GenomicRanges, IRanges Enhances: sn, SomatiCAData License: GPL (>=2) MD5sum: 9e4e19e74bb72a6f29c965b244ce64db NeedsCompilation: no Title: SomatiCA: identifying, characterizing, and quantifying somatic copy number aberrations from cancer genome sequencing Description: SomatiCA is a software suite that is capable of identifying, characterizing, and quantifying somatic CNAs from cancer genome sequencing. First, it uses read depths and lesser allele frequencies (LAF) from mapped short sequence reads to segment the genome and identify candidate CNAs. Second, SomatiCA estimates the admixture rate from the relative copy-number profile of tumor-normal pair by a Bayesian finite mixture model. Third, SomatiCA quantifies absolute somatic copy-number and subclonality for each genomic segment to guide its characterization. Results from SomatiCA can be further integrated with single nucleotide variations (SNVs) to get a better understanding of the tumor evolution. biocViews: Sequencing, CopyNumberVariation Author: Mengjie Chen , Hongyu Zhao Maintainer: Mengjie Chen source.ver: src/contrib/SomatiCA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SomatiCA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SomatiCA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SomatiCA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SomatiCA_1.10.0.tgz vignettes: vignettes/SomatiCA/inst/doc/SomatiCA.pdf, vignettes/SomatiCA/inst/doc/SomatiCAUserGuide.pdf vignetteTitles: SomatiCA Vignette, SomatiCAUserGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SomatiCA/inst/doc/SomatiCA.R Package: SomaticSignatures Version: 2.2.3 Depends: R (>= 3.0.2), VariantAnnotation, GenomicRanges Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.UCSC.hg19, SomaticCancerAlterations, COSMIC.67, ggdendro, fastICA License: GPL-3 MD5sum: 158471f97374497c0a52822ee6be16eb NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring (EMBL Heidelberg) Maintainer: Julian Gehring URL: http://bioconductor.org/packages/release/bioc/html/SomaticSignatures.html, https://github.com/julian-gehring/SomaticSignatures VignetteBuilder: knitr source.ver: src/contrib/SomaticSignatures_2.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.1/SomaticSignatures_2.2.3.zip win64.binary.ver: bin/windows64/contrib/3.1/SomaticSignatures_2.2.3.zip mac.binary.ver: bin/macosx/contrib/3.1/SomaticSignatures_2.2.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SomaticSignatures_2.2.3.tgz vignettes: vignettes/SomaticSignatures/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R htmlDocs: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html htmlTitles: "SomaticSignatures" importsMe: Rariant Package: SpacePAC Version: 1.4.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 4493d413205955cbc524b030b80fc64f NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik source.ver: src/contrib/SpacePAC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SpacePAC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SpacePAC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SpacePAC_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SpacePAC_1.4.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R Package: spade Version: 1.14.0 Depends: R (>= 2.11), igraph, Rclusterpp Imports: Biobase, flowCore Suggests: flowViz License: GPL-2 Archs: i386, x64 MD5sum: 9bb015d11b41d39f88a1bb2fcd568ed3 NeedsCompilation: yes Title: SPADE -- An analysis and visualization tool for Flow Cytometry Description: SPADE, or Spanning tree Progression of Density normalized Events, is an analysis and visualization tool for high dimensional flow cytometry data that organizes cells into hierarchies of related phenotypes. biocViews: FlowCytometry, GraphAndNetwork, GUI, Visualization, Clustering Author: M. Linderman, P. Qiu, E. Simonds, Z. Bjornson Maintainer: Zach Bjornson URL: http://cytospade.org source.ver: src/contrib/spade_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spade_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spade_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spade_1.14.0.tgz vignettes: vignettes/spade/inst/doc/SPADE.pdf vignetteTitles: spade package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spade/inst/doc/SPADE.R Package: specL Version: 1.0.0 Depends: R (>= 3.0.2), methods, DBI, RSQLite, seqinr, protViz (>= 0.2.5) Suggests: RUnit, BiocGenerics, BiocStyle, BiocParallel License: GPL-3 MD5sum: f884e7574c600c2a7baa67d8e070dd12 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: specL provides a function for generating spectra libraries which can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. biocViews: MassSpectrometry, Proteomics Author: Christian Trachsel , Christian Panse , Jonas Grossmann Maintainer: Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/specL.html source.ver: src/contrib/specL_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/specL_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/specL_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/specL_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/specL_1.0.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf vignetteTitles: specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/specL.R Package: SpeCond Version: 1.20.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 536e2f359d255a9e2013a59613422d65 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli source.ver: src/contrib/SpeCond_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SpeCond_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SpeCond_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SpeCond_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SpeCond_1.20.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R Package: SPEM Version: 1.6.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 641afd1fb6df3e6caae919d465b770f0 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG source.ver: src/contrib/SPEM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SPEM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SPEM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SPEM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SPEM_1.6.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R Package: SPIA Version: 2.18.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: GPL (>= 2) MD5sum: 0b2d581738955d9afebaca914736b277 NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 source.ver: src/contrib/SPIA_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SPIA_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SPIA_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SPIA_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SPIA_2.18.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser, ToPASeq suggestsMe: graphite, KEGGgraph Package: spikeLI Version: 2.26.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: f8ef893f8a2cc76a42883463a7158780 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon source.ver: src/contrib/spikeLI_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spikeLI_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spikeLI_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spikeLI_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/spikeLI_2.26.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spikeLI/inst/doc/spikeLI.R Package: spkTools Version: 1.22.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: c11aec487459f15bbc926856c54be5d4 NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org source.ver: src/contrib/spkTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spkTools_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spkTools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spkTools_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/spkTools_1.22.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R Package: splicegear Version: 1.38.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: dcf99cbb4c486101f7791cb7e7033385 NeedsCompilation: no Title: splicegear Description: A set of tools to work with alternative splicing biocViews: Infrastructure, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/splicegear_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/splicegear_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/splicegear_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/splicegear_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/splicegear_1.38.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splicegear/inst/doc/splicegear.R Package: spliceR Version: 1.8.0 Depends: R (>= 2.15.0), methods, cummeRbund, rtracklayer, VennDiagram, RColorBrewer, plyr Imports: GenomicRanges, IRanges Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome License: GPL (>=2) Archs: i386, x64 MD5sum: 3a5fd2c11d697b4e7446cf49f96706c6 NeedsCompilation: yes Title: Classification of alternative splicing and prediction of coding potential from RNA-seq data. Description: An R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, Visualization Author: Johannes Waage , Kristoffer Vitting-Seerup Maintainer: Johannes Waage , Kristoffer Vitting-Seerup source.ver: src/contrib/spliceR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spliceR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spliceR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spliceR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/spliceR_1.8.0.tgz vignettes: vignettes/spliceR/inst/doc/spliceR.pdf vignetteTitles: spliceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceR/inst/doc/spliceR.R Package: spliceSites Version: 1.4.0 Depends: methods,rbamtools,refGenome (>= 1.1.2),doBy,Biobase,Biostrings (>= 2.28.0),seqLogo Imports: BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: b017aae20ef964d49779ed61868e8bc6 NeedsCompilation: yes Title: Manages align gap positions from RNA-seq data Description: Align gap positions from RNA-seq data biocViews: RNASeq, GeneExpression, DifferentialExpression, Proteomics Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/spliceSites_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spliceSites_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spliceSites_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spliceSites_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/spliceSites_1.4.0.tgz vignettes: vignettes/spliceSites/inst/doc/spliceSites.pdf vignetteTitles: RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceSites/inst/doc/spliceSites.R Package: SplicingGraphs Version: 1.6.0 Depends: BiocGenerics, S4Vectors (>= 0.1.0), IRanges (>= 1.99.21), GenomicRanges (>= 1.17.22), GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, igraph, BiocGenerics, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: c592528aa9117a13119812a0f07ef87f NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression Author: D. Bindreither, M. Carlson, M. Morgan, H. Pages Maintainer: H. Pages source.ver: src/contrib/SplicingGraphs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SplicingGraphs_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SplicingGraphs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SplicingGraphs_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SplicingGraphs_1.6.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R Package: splots Version: 1.32.0 Imports: grid, RColorBrewer License: LGPL MD5sum: f0d9b30cc6e459453d82c028a165e0bc NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: The splots package provides the plotScreen function for visualising data in microtitre plate or slide format. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber source.ver: src/contrib/splots_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/splots_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/splots_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/splots_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/splots_1.32.0.tgz vignettes: vignettes/splots/inst/doc/splotsHOWTO.pdf vignetteTitles: Visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splotsHOWTO.R dependsOnMe: cellHTS2 importsMe: RNAinteract, RNAither Package: spotSegmentation Version: 1.40.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 90a0b3333ac50873596a4681509dc79d NeedsCompilation: no Title: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots Description: Spot segmentation via model-based clustering and gridding for blocks within microarray slides, as described in Li et al, Robust Model-Based Segmentation of Microarray Images, Technical Report no. 473, Department of Statistics, University of Washington. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Maintainer: Chris Fraley URL: http://www.stat.washington.edu/fraley source.ver: src/contrib/spotSegmentation_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/spotSegmentation_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/spotSegmentation_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/spotSegmentation_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/spotSegmentation_1.40.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.16.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 4163aa6f51367af48ec963fc1015f527 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html source.ver: src/contrib/SQUADD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SQUADD_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SQUADD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SQUADD_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SQUADD_1.16.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD package, SQUADD package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R Package: SRAdb Version: 1.20.13 Depends: RSQLite (>= 0.8-4) , graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 1961ecc04eb73ed6c78d55f616b6d010 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/sra/ source.ver: src/contrib/SRAdb_1.20.13.tar.gz win.binary.ver: bin/windows/contrib/3.1/SRAdb_1.20.13.zip win64.binary.ver: bin/windows64/contrib/3.1/SRAdb_1.20.13.zip mac.binary.ver: bin/macosx/contrib/3.1/SRAdb_1.20.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SRAdb_1.20.13.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R Package: sRAP Version: 1.6.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 391f205e913895ed9c52fc810f18b5c3 NeedsCompilation: no Title: Simplified RNA-Seq Analysis Pipeline Description: This package provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data. biocViews: GeneExpression, RNAseq, Microarray, Preprocessing, QualityControl, Statistics, DifferentialExpression, Visualization, GeneSetEnrichment, GO Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/sRAP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sRAP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sRAP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sRAP_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sRAP_1.6.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sRAP/inst/doc/sRAP.R Package: sscore Version: 1.38.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: ebf347f9a4a4cc211c9f709b23b33a7b NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html source.ver: src/contrib/sscore_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sscore_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sscore_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sscore_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sscore_1.38.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R Package: sSeq Version: 1.4.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: d55c28355dbb17eda96c6841daf79732 NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu source.ver: src/contrib/sSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sSeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sSeq_1.4.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R Package: ssize Version: 1.40.0 Depends: gdata, xtable License: LGPL MD5sum: 3895d5551c4754fe74c0a75c745b9c52 NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes source.ver: src/contrib/ssize_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ssize_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ssize_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ssize_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ssize_1.40.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: oneChannelGUI Package: SSPA Version: 2.6.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: 3cdeceea34aa1b645c9960ca6d80fb17 NeedsCompilation: yes Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/SSPA_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SSPA_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SSPA_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SSPA_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SSPA_2.6.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R Package: ssviz Version: 1.0.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer Suggests: knitr License: GPL-2 MD5sum: b37a5000094d8a57db510c75afe579e8 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/ssviz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ssviz_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ssviz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ssviz_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ssviz_1.0.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R Package: STAN Version: 1.0.0 Depends: Rsolnp, methods Suggests: BiocStyle, Gviz, GenomicRanges, IRanges, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 5a4a4a6fa1d0ddeb7f6e8493f1660a8a NeedsCompilation: yes Title: STrand-specific ANnotation of genomic data Description: STAN (STrand-specic ANnotation of genomic data) implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription, DNA replication, recombination or DNA repair by integrating genomic data. bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing Author: Benedikt Zacher, Julien Gagneur, Achim Tresch Maintainer: Benedikt Zacher VignetteBuilder: knitr source.ver: src/contrib/STAN_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/STAN_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/STAN_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/STAN_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/STAN_1.0.0.tgz vignettes: vignettes/STAN/inst/doc/STAN.pdf vignetteTitles: STrand-specific ANnotation of genomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN.R Package: staRank Version: 1.8.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: d9e03dd3a371f70b065a4267f5a1a925 NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg source.ver: src/contrib/staRank_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/staRank_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/staRank_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/staRank_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/staRank_1.8.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R Package: Starr Version: 1.22.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: a70e5414f301cbc161436c1de53104b5 NeedsCompilation: yes Title: Simple tiling array analysis of Affymetrix ChIP-chip data Description: Starr facilitates the analysis of ChIP-chip data, in particular that of Affymetrix tiling arrays. The package provides functions for data import, quality assessment, data visualization and exploration. Furthermore, it includes high-level analysis features like association of ChIP signals with annotated features, correlation analysis of ChIP signals and other genomic data (e.g. gene expression), peak-finding with the CMARRT algorithm and comparative display of multiple clusters of ChIP-profiles. It uses the basic Bioconductor classes ExpressionSet and probeAnno for maximum compatibility with other software on Bioconductor. All functions from Starr can be used to investigate preprocessed data from the Ringo package, and vice versa. An important novel tool is the the automated generation of correct, up-to-date microarray probe annotation (bpmap) files, which relies on an efficient mapping of short sequences (e.g. the probe sequences on a microarray) to an arbitrary genome. biocViews: Microarray,OneChannel,DataImport,QualityControl,Preprocessing,ChIPchip Author: Benedikt Zacher, Johannes Soeding, Pei Fen Kuan, Matthias Siebert, Achim Tresch Maintainer: Benedikt Zacher source.ver: src/contrib/Starr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Starr_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Starr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Starr_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Starr_1.22.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Starr/inst/doc/Starr.R Package: STATegRa Version: 1.0.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, grid, MASS, calibrate, gplots Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3) License: GPL-2 MD5sum: be4d5376311dd4add0718efbb53d0685 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Patricia Sebastián-León , Gordon Ball VignetteBuilder: knitr source.ver: src/contrib/STATegRa_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/STATegRa_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/STATegRa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/STATegRa_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/STATegRa_1.0.0.tgz vignettes: vignettes/STATegRa/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R htmlDocs: vignettes/STATegRa/inst/doc/STATegRa.html htmlTitles: "STATegRa User’s Guide" Package: stepNorm Version: 1.38.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: b39e7dcd3b060b225920b9c922199d18 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ source.ver: src/contrib/stepNorm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/stepNorm_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/stepNorm_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/stepNorm_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/stepNorm_1.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: stepwiseCM Version: 1.12.0 Depends: R (>= 2.14), randomForest, MAclinical, tspair, pamr, snowfall, glmpath, penalized, e1071, Biobase License: GPL (>2) MD5sum: afd4452daabcbb4c220af7d80c1e4416 NeedsCompilation: no Title: Stepwise Classification of Cancer Samples using High-dimensional Data Sets Description: Stepwise classification of cancer samples using multiple data sets. This package implements the classification strategy using two heterogeneous data sets without actually combining them. Package uses the data type for which full measurements are available at the first stage, and the data type for which only partial measurements are available at the second stage. For incoming new samples package quantifies how much improvement will be obtained if covariates of new samples for the data types at the second stage are measured. This packages suits for the application where study goal is not only obtain high classification accuracy, but also requires economically cheap classifier. biocViews: Classification, Microarray Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/stepwiseCM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/stepwiseCM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/stepwiseCM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/stepwiseCM_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/stepwiseCM_1.12.0.tgz vignettes: vignettes/stepwiseCM/inst/doc/stepwiseCM.pdf vignetteTitles: stepwiseCM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stepwiseCM/inst/doc/stepwiseCM.R Package: Streamer Version: 1.12.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 76ca670ffd85b58497fa0421d245d42c NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan source.ver: src/contrib/Streamer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Streamer_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Streamer_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Streamer_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Streamer_1.12.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy Package: STRINGdb Version: 1.5.5 Depends: R (>= 2.14.0), png, sqldf, plyr, igraph, RCurl, plotrix, methods, RColorBrewer, gplots, hash Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: a9ab507590214e9e33505aaf19faf9ea NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a user-friendly interface to the STRING protein-protein interactions database ( http://www.string-db.org ). biocViews: Network Author: Andrea Franceschini Maintainer: Andrea Franceschini , Alexander Roth , Christian Von Mering , Michael Kuhn , Lars J Jensen source.ver: src/contrib/STRINGdb_1.5.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/STRINGdb_1.5.5.zip win64.binary.ver: bin/windows64/contrib/3.1/STRINGdb_1.5.5.zip mac.binary.ver: bin/macosx/contrib/3.1/STRINGdb_1.5.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/STRINGdb_1.5.5.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: scsR Package: supraHex Version: 1.4.0 Depends: R (>= 3.0.2), hexbin Imports: ape, MASS License: GPL-2 MD5sum: 556d13743899e58346def5b8307d8027 NeedsCompilation: no Title: A supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes (see http://www.ncbi.nlm.nih.gov/pubmed/24309102). biocViews: Bioinformatics, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://supfam.org/supraHex source.ver: src/contrib/supraHex_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/supraHex_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/supraHex_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/supraHex_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/supraHex_1.4.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R Package: survcomp Version: 1.16.0 Depends: survival, prodlim, R (>= 2.10) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta Suggests: Hmisc, CPE, clinfun, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: d524056f2819c14ef560b9da4f87da4b NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: R package providing functions to assess and to compare the performance of risk prediction (survival) models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/survcomp_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/survcomp_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/survcomp_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/survcomp_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/survcomp_1.16.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu suggestsMe: metaseqR Package: Sushi Version: 1.2.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: 7a4aa35f6699384cc696a3f9742984c5 NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel source.ver: src/contrib/Sushi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Sushi_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Sushi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Sushi_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Sushi_1.2.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R Package: sva Version: 3.12.0 Depends: R (>= 2.8), mgcv, genefilter Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq License: Artistic-2.0 Archs: i386, x64 MD5sum: f427ad0acdb68b208918660178316480 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , John D. Storey Maintainer: Jeffrey T. Leek , John D. Storey source.ver: src/contrib/sva_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/sva_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/sva_3.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/sva_3.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/sva_3.12.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: SCAN.UPC importsMe: ballgown, ChAMP, charm, PAA, trigger Package: SwimR Version: 1.4.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 MD5sum: 0d6492bd3178064b290700fac223dfb0 NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely source.ver: src/contrib/SwimR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/SwimR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/SwimR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/SwimR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/SwimR_1.4.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R Package: switchBox Version: 1.0.0 Depends: R (>= 2.13.1) License: GPL-2 Archs: i386, x64 MD5sum: 2aa0cd2f219a753bb99fb34d812daa27 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm. Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni Maintainer: Bahman Afsari , Luigi Marchionni source.ver: src/contrib/switchBox_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/switchBox_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/switchBox_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/switchBox_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/switchBox_1.0.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R Package: synapter Version: 1.8.4 Depends: R (>= 2.15), methods, MSnbase Imports: hwriter, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel Suggests: synapterdata, xtable, tcltk, tcltk2 License: GPL-2 MD5sum: c459c1ab5e07dcd78b4eb7550dec3765 NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: MassSpectrometry, Proteomics, GUI Author: Laurent Gatto, Nick J. Bond and Pavel V. Shliaha with contributions from Sebastian Gibb. Maintainer: Laurent Gatto URL: http://lgatto.github.com/synapter/ VignetteBuilder: knitr source.ver: src/contrib/synapter_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.1/synapter_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.1/synapter_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.1/synapter_1.8.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/synapter_1.8.4.tgz vignettes: vignettes/synapter/inst/doc/synapter.pdf vignetteTitles: Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/synapter.R suggestsMe: pRoloc Package: systemPipeR Version: 1.0.12 Depends: Rsamtools, Biostrings, ShortRead, methods Imports: BiocGenerics, rjson, grid, ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs Suggests: ape, RUnit, BiocStyle, biomaRt, GenomicFeatures, BiocParallel License: Artistic-2.0 MD5sum: 23f0a707866b849eda370b09ea9ed57e NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building end-to-end analysis pipelines with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. An important feature is support for running command-line software, such as NGS aligners, on both single machines or compute clusters. Instructions for using systemPipeR are given in the Overview Vignette (PDF). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl Author: Thomas Girke Maintainer: Thomas Girke URL: https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. source.ver: src/contrib/systemPipeR_1.0.12.tar.gz win.binary.ver: bin/windows/contrib/3.1/systemPipeR_1.0.12.zip win64.binary.ver: bin/windows64/contrib/3.1/systemPipeR_1.0.12.zip mac.binary.ver: bin/macosx/contrib/3.1/systemPipeR_1.0.12.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/systemPipeR_1.0.12.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeR.pdf, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeVARseq.pdf vignetteTitles: ChIP-Seq Report Template, Overview Vignette, RNA-Seq Report Template, VAR-Seq Report Template hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.R, vignettes/systemPipeR/inst/doc/systemPipeR.R, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.R, vignettes/systemPipeR/inst/doc/systemPipeVARseq.R Package: TargetScore Version: 1.4.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: daba8919988da67c091d6dd2fff5d3da NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/TargetScore_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TargetScore_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TargetScore_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TargetScore_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TargetScore_1.4.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R Package: TargetSearch Version: 1.22.0 Depends: R (>= 2.7.0), mzR Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 7f0ceab4e6b3bbc5f86cc9534f7b1191 NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data. Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree Author: Alvaro Cuadros-Inostroza , Jan Lisec , Henning Redestig , Matt Hannah Maintainer: Alvaro Cuadros-Inostroza source.ver: src/contrib/TargetSearch_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TargetSearch_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TargetSearch_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TargetSearch_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TargetSearch_1.22.0.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R Package: TCC Version: 1.6.5 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports: samr Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 9fb3ea5128f9d159932e1831cecfa9ec NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama source.ver: src/contrib/TCC_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/TCC_1.6.5.zip win64.binary.ver: bin/windows64/contrib/3.1/TCC_1.6.5.zip mac.binary.ver: bin/macosx/contrib/3.1/TCC_1.6.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TCC_1.6.5.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR Package: TDARACNE Version: 1.16.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: bdba76b44a26550aa082436c92ccf802 NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro source.ver: src/contrib/TDARACNE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TDARACNE_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TDARACNE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TDARACNE_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TDARACNE_1.16.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R Package: TEQC Version: 3.6.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 694559025b2bf1c49f08b7a43549bf69 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Manuela Hummel source.ver: src/contrib/TEQC_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TEQC_3.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TEQC_3.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TEQC_3.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TEQC_3.6.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R Package: ternarynet Version: 1.10.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 98eadd2e8ae893714a12558a1c21bb86 NeedsCompilation: yes Title: Ternary Network Estimation Description: A computational Bayesian approach to ternary gene regulatory network estimation from gene perturbation experiments. biocViews: Software, CellBiology, GraphAndNetwork Author: Matthew N. McCall , Anthony Almudevar Maintainer: Matthew N. McCall source.ver: src/contrib/ternarynet_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/ternarynet_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/ternarynet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/ternarynet_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ternarynet_1.10.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R Package: TFBSTools Version: 1.4.0 Depends: R (>= 3.0.1) Imports: Biostrings(>= 2.33.3), RSQLite(>= 0.11.4), seqLogo, GenomicRanges(>= 1.17.7), caTools(>= 1.14), XVector(>= 0.5.8), rtracklayer(>= 1.25.3), BSgenome(>= 1.30.0), S4Vectors(>= 0.2.3), IRanges(>= 1.99.28), methods, gtools(>= 3.0.0), CNEr(>= 0.99.8), BiocParallel(>= 0.5.6), DirichletMultinomial(>= 1.7.1), TFMPvalue(>= 0.0.5) Suggests: JASPAR2014(>= 0.99.3), RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: baaa37108c04a138156428cbd4c68295 NeedsCompilation: yes Title: Software package for transcription factor binding site (TFBS) analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites and transcription factor profile matrices. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery Author: Ge Tan Maintainer: Ge Tan URL: http://jaspar.genereg.net/ SystemRequirements: meme source.ver: src/contrib/TFBSTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TFBSTools_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TFBSTools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TFBSTools_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TFBSTools_1.4.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.pdf vignetteTitles: TFBSTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R Package: tigre Version: 1.20.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle License: AGPL-3 Archs: i386, x64 MD5sum: 40a142f60bc9c98ad909366f3ff352c4 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: http://www.bioinf.manchester.ac.uk/resources/tiger/ source.ver: src/contrib/tigre_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tigre_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tigre_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tigre_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tigre_1.20.0.tgz vignettes: vignettes/tigre/inst/doc/tigre_quick.pdf, vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre Quick Guide, tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre_quick.R, vignettes/tigre/inst/doc/tigre.R Package: tilingArray Version: 1.44.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 6be3eba3b29735ba0781fb21c498db74 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu source.ver: src/contrib/tilingArray_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tilingArray_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tilingArray_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tilingArray_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tilingArray_1.44.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/assessNorm.R, vignettes/tilingArray/inst/doc/costMatrix.R, vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R, vignettes/tilingArray/inst/doc/segmentation.R importsMe: ADaCGH2, snapCGH Package: timecourse Version: 1.38.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: b121e18353e1f9a5700f907d7d32a2dd NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org source.ver: src/contrib/timecourse_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/timecourse_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/timecourse_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/timecourse_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/timecourse_1.38.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R Package: TitanCNA Version: 1.4.0 Depends: R (>= 3.1.0), foreach (>= 1.4.0), IRanges (>= 1.99.1), Rsamtools (>= 1.17.11), GenomeInfoDb (>= 1.1.3) License: file LICENSE Archs: i386, x64 MD5sum: 5fb187d95c6fec810df6d7b4bfc3a646 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalenece of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation Author: Gavin Ha, Sohrab P Shah Maintainer: Gavin Ha , Sohrab P Shah source.ver: src/contrib/TitanCNA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TitanCNA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TitanCNA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TitanCNA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TitanCNA_1.4.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf vignetteTitles: TitanCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R Package: tkWidgets Version: 1.44.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: c178d5beb746c2e66956d4b647b7fce9 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang source.ver: src/contrib/tkWidgets_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tkWidgets_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tkWidgets_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tkWidgets_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tkWidgets_1.44.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R dependsOnMe: oneChannelGUI importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: ToPASeq Version: 1.0.1 Depends: graphite, gRbase, graph, locfit Imports: R.utils, edgeR, DESeq2, GenomicRanges, igraph, DESeq, fields, limma, TeachingDemos, SPIA, clipper, topologyGSA Suggests: RUnit, BiocGenerics, gageData, Rgraphviz, DEGraph License: AGPL-3 MD5sum: 25f6db2427135d66d5d30d62a1ca7208 NeedsCompilation: no Title: Package for Topology-based Pathway Analysis of RNASeq data Description: Implementation of seven methods for topology-based pathway analysis of both RNASeq and microarray data: SPIA, DEGraph, TopologyGSA, TAPPA, TBS, PWEA and a visualization tool for a single pathway. biocViews: Software, GeneExpression, NetworkEnrichment, GraphAndNetwork, RNASeq, Visualization, Microarray, Pathways, DifferentialExpression, Author: Ivana Ihnatova, Eva Budinska Maintainer: Ivana Ihnatova source.ver: src/contrib/ToPASeq_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/ToPASeq_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.1/ToPASeq_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.1/ToPASeq_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/ToPASeq_1.0.1.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq.pdf vignetteTitles: An R Package for topology-based pathway analysis of microaray and RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq.R Package: topGO Version: 2.18.0 Depends: R (>= 2.10.0), methods, graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: methods, graph, Biobase, SparseM, AnnotationDbi, lattice Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: f293b82e72d583747c1aac2f20518c7e NeedsCompilation: no Title: topGO: Enrichment analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa source.ver: src/contrib/topGO_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/topGO_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.1/topGO_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.1/topGO_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/topGO_2.18.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: compEpiTools, RNAither, tRanslatome importsMe: GOSim, mvGST suggestsMe: FGNet, miRNAtap, Ringo Package: tracktables Version: 1.0.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 3e2aedc9c69f6ff9fb08c33e1fcb1feb NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/tracktables_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tracktables_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tracktables_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tracktables_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tracktables_1.0.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R Package: trackViewer Version: 1.2.0 Depends: R (>= 3.1.0), methods, GenomicRanges, grid, gWidgetstcltk Imports: GenomicAlignments, GenomicFeatures, Gviz, pbapply, Rsamtools, rtracklayer, scales Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 8fdad3b7a3934a5c6753e526c31c9afe NeedsCompilation: no Title: A bioconductor package with minimalist design for plotting elegant track layers Description: visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq. biocViews: Visualization Author: Jianhong Ou, Yong-Xu Wang, Lihua Julie Zhu Maintainer: Jianhong Ou source.ver: src/contrib/trackViewer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/trackViewer_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/trackViewer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/trackViewer_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/trackViewer_1.2.0.tgz vignettes: vignettes/trackViewer/inst/doc/trackViewer.pdf vignetteTitles: trackViewer Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R Package: tRanslatome Version: 1.4.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix License: LGPL MD5sum: da1ae0a9eaafc11b578d0f249906c545 NeedsCompilation: no Title: Comparison between multiple levels of gene expression. Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, SAM, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, GeneExpression, DifferentialExpression, Microarray, Sequencing, QualityControl, GO, MultipleComparison Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi source.ver: src/contrib/tRanslatome_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tRanslatome_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tRanslatome_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tRanslatome_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tRanslatome_1.4.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R Package: TransView Version: 1.10.0 Depends: methods,GenomicRanges Imports: Rsamtools,zlibbioc,gplots,IRanges LinkingTo: Rsamtools Suggests: RUnit,pasillaBamSubset License: GPL-3 Archs: i386, x64 MD5sum: ccac9e2563c72d494e2609b97862af63 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets. Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html source.ver: src/contrib/TransView_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TransView_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TransView_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TransView_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TransView_1.10.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R Package: triform Version: 1.8.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: IRanges, yaml, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: 3abc29f23a2d86b38cdd8ec7b6384a4f NeedsCompilation: no Title: Triform finds enriched regions (peaks) in transcription factor ChIP-sequencing data Description: The Triform algorithm uses model-free statistics to identify peak-like distributions of TF ChIP sequencing reads, taking advantage of an improved peak definition in combination with known profile characteristics. biocViews: Sequencing, ChIPSeq Author: Karl Kornacker Developer [aut], Tony Handstad Developer [aut, cre] Maintainer: Tony Handstad Developer source.ver: src/contrib/triform_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/triform_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/triform_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/triform_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/triform_1.8.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/triform/inst/doc/triform.R Package: trigger Version: 1.12.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: f7fc37fa890e79d1d5980ad42a03f056 NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey source.ver: src/contrib/trigger_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/trigger_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/trigger_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/trigger_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/trigger_1.12.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R Package: trio Version: 3.4.0 Depends: R (>= 3.0.1) Suggests: survival, haplo.stats, mcbiopi, siggenes, splines, LogicReg (>= 1.5.3), logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: dc15224db7db5298f2b423ab896754f1 NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender source.ver: src/contrib/trio_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/trio_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/trio_3.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/trio_3.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/trio_3.4.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R Package: triplex Version: 1.6.0 Depends: R (>= 2.15.0), S4Vectors, IRanges (>= 1.99.1), XVector (>= 0.5.3), Biostrings (>= 2.33.3) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer, GenomeGraphs License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: cef7919ddc8b94be1d840d04a80f0a6b NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ source.ver: src/contrib/triplex_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/triplex_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/triplex_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/triplex_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/triplex_1.6.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R Package: TSCAN Version: 1.2.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, TSP, combinat, mgcv, gplots Suggests: knitr License: GPL(>=2) MD5sum: 7569fcd285ba41431878514ececb1556 NeedsCompilation: no Title: TSCAN: Tools for Single-Cell ANalysis Description: TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/TSCAN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TSCAN_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TSCAN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TSCAN_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TSCAN_1.2.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R Package: tspair Version: 1.24.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: b8912c55eb0d710d203e133d5bf42312 NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek source.ver: src/contrib/tspair_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tspair_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tspair_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tspair_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tspair_1.24.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependsOnMe: stepwiseCM Package: TSSi Version: 1.12.0 Depends: R (>= 2.13.2), BiocGenerics (>= 0.3.2), IRanges Imports: methods, BiocGenerics, S4Vectors, Hmisc, minqa, stats, Biobase (>= 0.3.2), plyr, IRanges Suggests: rtracklayer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: cdfcb14c18da092e2a96ecb49785c0be NeedsCompilation: yes Title: Transcription Start Site Identification Description: Identify and normalize transcription start sites in high-throughput sequencing data. biocViews: Sequencing, RNASeq, Genetics, Preprocessing Author: Clemens Kreutz, Julian Gehring Maintainer: Julian Gehring URL: http://julian-gehring.github.com/TSSi/ source.ver: src/contrib/TSSi_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TSSi_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TSSi_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TSSi_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TSSi_1.12.0.tgz vignettes: vignettes/TSSi/inst/doc/TSSi.pdf vignetteTitles: Introduction to the TSSi package: Identification of Transcription Start Sites hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSSi/inst/doc/TSSi.R Package: TurboNorm Version: 1.14.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL Archs: i386, x64 MD5sum: 810e8f7978865b5dd76d577793df439d NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/TurboNorm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TurboNorm_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TurboNorm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TurboNorm_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TurboNorm_1.14.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R Package: tweeDEseq Version: 1.12.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 2df65f629f55adda33d866e62ebaf827 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm source.ver: src/contrib/tweeDEseq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/tweeDEseq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/tweeDEseq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/tweeDEseq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/tweeDEseq_1.12.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R Package: twilight Version: 1.42.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: i386, x64 MD5sum: 8b95cd1bb06dbbb0cc4eb9ea88ae199d NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml source.ver: src/contrib/twilight_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/twilight_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/twilight_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/twilight_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/twilight_1.42.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList importsMe: OrderedList Package: TypeInfo Version: 1.32.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 754f10671bc199b8ead4ef0cb09be779 NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang source.ver: src/contrib/TypeInfo_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/TypeInfo_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/TypeInfo_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/TypeInfo_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/TypeInfo_1.32.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependsOnMe: RWebServices Package: UNDO Version: 1.8.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 5105aa92d931c6c984756cf2016b9a73 NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang source.ver: src/contrib/UNDO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/UNDO_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.1/UNDO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.1/UNDO_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/UNDO_1.8.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R Package: unifiedWMWqPCR Version: 1.2.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 11f39e5180307c9b83534a02ea6d23cc NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys source.ver: src/contrib/unifiedWMWqPCR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/unifiedWMWqPCR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/unifiedWMWqPCR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/unifiedWMWqPCR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/unifiedWMWqPCR_1.2.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R Package: UniProt.ws Version: 2.6.2 Depends: RSQLite, RCurl, methods, utils Imports: BiocGenerics, AnnotationDbi Suggests: RUnit License: Artistic License 2.0 MD5sum: 1a0f547f55576f326eb15f3b9b01af32 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the Uniprot web services. biocViews: Annotation, Infrastructure Author: Marc Carlson Maintainer: Marc Carlson source.ver: src/contrib/UniProt.ws_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.1/UniProt.ws_2.6.2.zip win64.binary.ver: bin/windows64/contrib/3.1/UniProt.ws_2.6.2.zip mac.binary.ver: bin/macosx/contrib/3.1/UniProt.ws_2.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/UniProt.ws_2.6.2.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R suggestsMe: cleaver, dagLogo Package: VanillaICE Version: 1.28.5 Depends: R (>= 3.0.0), GenomicRanges Imports: Biobase, oligoClasses (>= 1.24.0), IRanges (>= 1.14.0), S4Vectors, foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb, crlmm Suggests: RUnit, SNPchip, human610quadv1bCrlmm, BSgenome.Hsapiens.UCSC.hg18, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: 19a6dfc0061c9ed02c2e3b2953ea86c7 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays biocViews: CopyNumberVariation Author: Robert Scharpf , Kevin Scharpf, and Ingo Ruczinski Maintainer: Robert Scharpf source.ver: src/contrib/VanillaICE_1.28.5.tar.gz win.binary.ver: bin/windows/contrib/3.1/VanillaICE_1.28.5.zip win64.binary.ver: bin/windows64/contrib/3.1/VanillaICE_1.28.5.zip mac.binary.ver: bin/macosx/contrib/3.1/VanillaICE_1.28.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/VanillaICE_1.28.5.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R importsMe: MinimumDistance Package: VariantAnnotation Version: 1.12.9 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.7.7), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.40), Rsamtools (>= 1.17.26) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.2.3), IRanges (>= 2.0.1), XVector (>= 0.5.6), Biostrings (>= 2.33.5), AnnotationDbi (>= 1.27.9), BSgenome, rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.17.13) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rsamtools Suggests: RUnit, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20110815, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 4fe58a5dac8b91d487ff6d59f3671368 NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes biocViews: DataImport, Sequencing, SNP, Annotation, Genetics Author: Valerie Obenchain [aut, cre], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Valerie Obenchain Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/VariantAnnotation_1.12.9.tar.gz win.binary.ver: bin/windows/contrib/3.1/VariantAnnotation_1.12.9.zip win64.binary.ver: bin/windows64/contrib/3.1/VariantAnnotation_1.12.9.zip mac.binary.ver: bin/macosx/contrib/3.1/VariantAnnotation_1.12.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/VariantAnnotation_1.12.9.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: filterVcf Overview, Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, HTSeqGenie, Rariant, SomaticSignatures, VariantTools importsMe: biovizBase, customProDB, FunciSNP, ggbio, GGtools, gmapR, R453Plus1Toolbox, SeqArray, VariantFiltering suggestsMe: GenomicRanges, GWASTools, trio, vtpnet Package: VariantFiltering Version: 1.2.14 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.11.3) Imports: DBI, RSQLite (>= 1.0.0), Biobase, S4Vectors, IRanges (>= 1.99.17), AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.2.2), GenomicRanges (>= 1.17.19), GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, BSgenome.Hsapiens.UCSC.hg19, VariantAnnotation (>= 1.12.2), shiny LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608, MafDb.ALL.wgs.phase1.release.v3.20101123, MafDb.ESP6500SI.V2.SSA137.dbSNP138, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 08f528bc7bff3e886341995d6add232d NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minimum allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Dei M. Elurbe, Robert Castelo Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering/issues source.ver: src/contrib/VariantFiltering_1.2.14.tar.gz win.binary.ver: bin/windows/contrib/3.1/VariantFiltering_1.2.14.zip win64.binary.ver: bin/windows64/contrib/3.1/VariantFiltering_1.2.14.zip mac.binary.ver: bin/macosx/contrib/3.1/VariantFiltering_1.2.14.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/VariantFiltering_1.2.14.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: VariantTools Version: 1.8.1 Depends: S4Vectors (>= 0.0.2), IRanges (>= 1.99.2), GenomicRanges (>= 1.17.7), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.17.6), BiocGenerics, Biostrings, parallel, gmapR (>= 1.7.2), GenomicFeatures (>= 1.17.13), Matrix, rtracklayer (>= 1.25.3), BiocParallel, GenomeInfoDb, BSgenome Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph License: Artistic-2.0 MD5sum: 5fb49cf1a1a841bebf67309e7885caf3 NeedsCompilation: no Title: Tools for Working with Genetic Variants Description: Tools for detecting, filtering, calling, comparing and plotting variants. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/VariantTools_1.8.1.tar.gz vignettes: vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie Package: vbmp Version: 1.34.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: e53c5f286fd7b275a2856c8d741e396b NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 source.ver: src/contrib/vbmp_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/vbmp_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/vbmp_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/vbmp_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/vbmp_1.34.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R Package: Vega Version: 1.14.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: e4c7cb857f53b5d0a92b6db6aec3c17a NeedsCompilation: yes Title: An R package for copy number data segmentation Description: Vega (Variational Estimator for Genomic Aberrations) is an algorithm that adapts a very popular variational model (Mumford and Shah) used in image segmentation so that chromosomal aberrant regions can be efficiently detected. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella Maintainer: Sandro Morganella source.ver: src/contrib/Vega_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/Vega_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.1/Vega_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.1/Vega_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/Vega_1.14.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Vega/inst/doc/Vega.R Package: VegaMC Version: 3.4.0 Depends: R (>= 2.10.0), biomaRt, Biobase, genoset Imports: methods License: GPL-2 Archs: i386, x64 MD5sum: 3497e266683de73bb1a398f018c0a5e8 NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella source.ver: src/contrib/VegaMC_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/VegaMC_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.1/VegaMC_3.4.0.zip mac.binary.ver: bin/macosx/contrib/3.1/VegaMC_3.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/VegaMC_3.4.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R Package: viper Version: 1.2.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats Suggests: bcellViper License: GPL (>=2) MD5sum: 9442899fa968534fa3d0fc613b4ff010 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez source.ver: src/contrib/viper_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/viper_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.1/viper_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.1/viper_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/viper_1.2.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/viper/inst/doc/viper.R Package: vsn Version: 3.34.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: methods, affy (>= 1.23.4), limma, lattice Suggests: affydata, hgu95av2cdf License: Artistic-2.0 Archs: i386, x64 MD5sum: 4abfa73534b7122daebfadfbd6481e19 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, both between colours within array, and between arrays. The method uses a robust variant of the maximum-likelihood estimator for the stochastic model of microarray data described in the references (see vignette). The model incorporates data calibration (a.k.a. normalization), a model for the dependence of the variance on the mean intensity, and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth. Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber source.ver: src/contrib/vsn_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/vsn_3.34.0.zip win64.binary.ver: bin/windows64/contrib/3.1/vsn_3.34.0.zip mac.binary.ver: bin/macosx/contrib/3.1/vsn_3.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/vsn_3.34.0.tgz vignettes: vignettes/vsn/inst/doc/convergence2.pdf, vignettes/vsn/inst/doc/likelihoodcomputations.pdf, vignettes/vsn/inst/doc/vsn.pdf vignetteTitles: Verifying and assessing the performance with simulated data, Likelihood Calculations for vsn, Introduction to vsn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/convergence2.R, vignettes/vsn/inst/doc/likelihoodcomputations.R, vignettes/vsn/inst/doc/vsn.R dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, imageHTS, LVSmiRNA, metaseqR, MSnbase, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, cellHTS, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight Package: vtpnet Version: 0.6.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 8c2fc7a3be78f567fa68af285a8ea771 NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/vtpnet_0.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/vtpnet_0.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/vtpnet_0.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/vtpnet_0.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/vtpnet_0.6.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R Package: wateRmelon Version: 1.6.0 Depends: R (>= 2.10), limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: RPMM Enhances: minfi, methylumi, IMA License: GPL-3 MD5sum: c8e72611f068df7ad7cfe16ee2edb5e0 NeedsCompilation: no Title: Illumina 450 methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi, minfi and IMA packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic Maintainer: Leo source.ver: src/contrib/wateRmelon_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/wateRmelon_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/wateRmelon_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/wateRmelon_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/wateRmelon_1.6.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R Package: wavClusteR Version: 2.0.0 Depends: R (>= 3.0.0), GenomicRanges, Rsamtools Imports: Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, IRanges, mclust, rtracklayer, seqinr, stringr, wmtsa Suggests: BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: ea9d6fb84efa1445f15c6cf06f3474a4 NeedsCompilation: no Title: wavClusteR Description: Infer PAR-CLIP induced transitions and discriminate them from sequencing error, SNPs, contaminants and additional non-experimental causes, using a non-parametric mixture model. wavClusteR resolves cluster boundaries at high resolution and provides robust estimation of cluster statistics. In addition, the package allows to integrate RNA-Seq data to estimate FDR over the entire range of relative substitution frequencies. Furthermore, the package provides post-processing of results and functions to export results for UCSC genome browser visualization and motif search analysis. Key functions support parallel multicore computing. While wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other Next-Generation Sequencing data obtained from substitution inducing experimental procedures (e.g. BisSeq) biocViews: Sequencing, Software Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio source.ver: src/contrib/wavClusteR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/wavClusteR_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.1/wavClusteR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.1/wavClusteR_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/wavClusteR_2.0.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.pdf vignetteTitles: wavClusteR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R Package: waveTiling Version: 1.8.1 Depends: oligo, oligoClasses, Biobase, Biostrings, GenomeGraphs Imports: methods, affy, preprocessCore, GenomicRanges, waveslim, IRanges Suggests: BSgenome, BSgenome.Athaliana.TAIR.TAIR9, waveTilingData, pd.atdschip.tiling, TxDb.Athaliana.BioMart.plantsmart22 License: GPL (>=2) Archs: i386, x64 MD5sum: 30336ad59d4a465bab58ebc1a0f17d86 NeedsCompilation: yes Title: Wavelet-Based Models for Tiling Array Transcriptome Analysis Description: This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models. biocViews: Microarray, DifferentialExpression, TimeCourse, GeneExpression Author: Kristof De Beuf , Peter Pipelers and Lieven Clement Maintainer: Kristof De Beuf URL: https://r-forge.r-project.org/projects/wavetiling/ source.ver: src/contrib/waveTiling_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/waveTiling_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.1/waveTiling_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.1/waveTiling_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/waveTiling_1.8.1.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/waveTiling/inst/doc/waveTiling-vignette.R Package: weaver Version: 1.32.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 05de40752db4da5826aab27debde3043 NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon source.ver: src/contrib/weaver_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/weaver_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.1/weaver_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.1/weaver_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/weaver_1.32.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R suggestsMe: BiocCaseStudies Package: webbioc Version: 1.38.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: a82fdd8379a9b32c4538a0fbc7867b72 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm source.ver: src/contrib/webbioc_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/webbioc_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.1/webbioc_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.1/webbioc_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/webbioc_1.38.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/webbioc/inst/doc/demoscript.R, vignettes/webbioc/inst/doc/webbioc.R Package: widgetTools Version: 1.44.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: fd45cffc01817c973ed7c14fbbbb0f00 NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang source.ver: src/contrib/widgetTools_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/widgetTools_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.1/widgetTools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.1/widgetTools_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/widgetTools_1.44.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui suggestsMe: affy Package: xcms Version: 1.42.0 Depends: R (>= 2.14.0), methods, mzR (>= 1.1.6), BiocGenerics, Biobase Suggests: faahKO, msdata, ncdf, multtest, rgl, MassSpecWavelet (>= 1.5.2), RANN, RUnit Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: d96ea0f031ba0c0a0fedb0ce75ca2d89 NeedsCompilation: yes Title: LC/MS and GC/MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley Maintainer: Ralf Tautenhahn URL: http://metlin.scripps.edu/download/ and https://github.com/sneumann/xcms source.ver: src/contrib/xcms_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/xcms_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.1/xcms_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.1/xcms_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/xcms_1.42.0.tgz vignettes: vignettes/xcms/inst/doc/xcmsDirect.pdf, vignettes/xcms/inst/doc/xcmsInstall.pdf, vignettes/xcms/inst/doc/xcmsMSn.pdf, vignettes/xcms/inst/doc/xcmsPreprocess.pdf vignetteTitles: Grouping FTICR-MS data with xcms, Installation Instructions for xcms, Processing Tandem-MS and MS$^n$ data with xcms, LC/MS Preprocessing and Analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/xcmsDirect.R, vignettes/xcms/inst/doc/xcmsInstall.R, vignettes/xcms/inst/doc/xcmsMSn.R, vignettes/xcms/inst/doc/xcmsPreprocess.R dependsOnMe: CAMERA, flagme, Metab, metaMS importsMe: CAMERA, cosmiq, Risa suggestsMe: MassSpecWavelet, RMassBank Package: XDE Version: 2.12.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), methods, graphics Imports: Biobase, BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm Suggests: siggenes, genefilter, MASS, RColorBrewer, GeneMeta, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: f33d1c7f2015fe1a6a86b711dfaf3d98 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf source.ver: src/contrib/XDE_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/XDE_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/XDE_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/XDE_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/XDE_2.12.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R Package: xmapbridge Version: 1.24.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 3a74a5391cc93a5e7d5d845744e9589c NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org source.ver: src/contrib/xmapbridge_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/xmapbridge_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.1/xmapbridge_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.1/xmapbridge_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/xmapbridge_1.24.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R Package: xps Version: 1.26.1 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) Archs: i386 MD5sum: 5fa8639ba208c012713806daa218de96 NeedsCompilation: yes Title: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays Description: The package handles pre-processing, normalization, filtering and analysis of Affymetrix GeneChip expression arrays, including exon arrays (Exon 1.0 ST: core, extended, full probesets), gene arrays (Gene 1.0 ST) and plate arrays on computers with 1 GB RAM only. It imports Affymetrix .CDF, .CLF, .PGF and .CEL as well as annotation files, and computes e.g. RMA, MAS5, FARMS, DFW, FIRMA, tRMA, MAS5-calls, DABG-calls, I/NI-calls. It is an R wrapper to XPS (eXpression Profiling System), which is based on ROOT, an object-oriented framework developed at CERN. Thus, the prior installation of ROOT is a prerequisite for the usage of this package, however, no knowledge of ROOT is required. ROOT is licensed under LGPL and can be downloaded from http://root.cern.ch. biocViews: ExonArray, GeneExpression, Microarray, OneChannel, DataImport, Preprocessing, Transcription, DifferentialExpression Author: Christian Stratowa, Vienna, Austria Maintainer: Christian Stratowa SystemRequirements: GNU make source.ver: src/contrib/xps_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.1/xps_1.26.1.zip win64.binary.ver: bin/windows64/contrib/3.1/xps_1.26.1.zip mac.binary.ver: bin/macosx/contrib/3.1/xps_1.26.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/xps_1.26.1.tgz vignettes: vignettes/xps/inst/doc/APTvsXPS.pdf, vignettes/xps/inst/doc/xps.pdf, vignettes/xps/inst/doc/xpsClasses.pdf, vignettes/xps/inst/doc/xpsPreprocess.pdf vignetteTitles: 3. XPS Vignette: Comparison APT vs XPS, 1. XPS Vignette: Overview, 2. XPS Vignette: Classes, 4. XPS Vignette: Function express() hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xps/inst/doc/APTvsXPS.R, vignettes/xps/inst/doc/xps.R, vignettes/xps/inst/doc/xpsClasses.R, vignettes/xps/inst/doc/xpsPreprocess.R Package: XVector Version: 0.6.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.2.0), IRanges (>= 1.99.26) Imports: methods, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 6e14862f76bf9407930963038e62724c NeedsCompilation: yes Title: Representation and manpulation of external sequences Description: Memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: H. Pages and P. Aboyoun Maintainer: H. Pages source.ver: src/contrib/XVector_0.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/XVector_0.6.0.zip win64.binary.ver: bin/windows64/contrib/3.1/XVector_0.6.0.zip mac.binary.ver: bin/macosx/contrib/3.1/XVector_0.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/XVector_0.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, Rsamtools, triplex importsMe: Biostrings, BSgenome, ChIPsim, CNEr, compEpiTools, DECIPHER, gcrma, GenomicRanges, Gviz, kebabs, R453Plus1Toolbox, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges Package: yaqcaffy Version: 1.26.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: c6884ec94b09920e1025e4150ae2b5c8 NeedsCompilation: no Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto source.ver: src/contrib/yaqcaffy_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/yaqcaffy_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.1/yaqcaffy_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.1/yaqcaffy_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/yaqcaffy_1.26.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yaqcaffy/inst/doc/yaqcaffy.R suggestsMe: qcmetrics Package: zlibbioc Version: 1.12.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 4c3ad7878ea26e25cc9511c0e6048326 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Zlibbioc.html source.ver: src/contrib/zlibbioc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.1/zlibbioc_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.1/zlibbioc_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.1/zlibbioc_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.1/zlibbioc_1.12.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zlibbioc/inst/doc/UsingZlibbioc.R dependsOnMe: BitSeq importsMe: affy, affyio, affyPLM, Biostrings, ChemmineOB, DiffBind, makecdfenv, oligo, QuasR, rhdf5, Rsamtools, rtracklayer, seqbias, ShortRead, snpStats, Starr, VariantAnnotation