Package: annotation Version: 1.8.1 Depends: R (>= 3.3.0), VariantAnnotation, AnnotationHub, Organism.dplyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.ensGene, org.Hs.eg.db, org.Mm.eg.db, Homo.sapiens, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome, TxDb.Athaliana.BioMart.plantsmart22 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 8dffedf7a4ac2c50a8842dddcee8b08c NeedsCompilation: no Title: Genomic Annotation Resources Description: Annotation resources make up a significant proportion of the Bioconductor project. And there are also a diverse set of online resources available which are accessed using specific packages. This walkthrough will describe the most popular of these resources and give some high level examples on how to use them. biocViews: AnnotationWorkflow, Workflow Author: Marc RJ Carlson [aut], Herve Pages [aut], Sonali Arora [aut], Valerie Obenchain [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/help/workflows/annotation/Annotation_Resources/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotation git_branch: RELEASE_3_9 git_last_commit: aee0368 git_last_commit_date: 2019-08-06 Date/Publication: 2019-08-07 source.ver: src/contrib/annotation_1.8.1.tar.gz vignettes: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.html, vignettes/annotation/inst/doc/Annotation_Resources.html vignetteTitles: Annotating Genomic Ranges, Genomic Annotation Resources hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotation/inst/doc/Annotating_Genomic_Ranges.R, vignettes/annotation/inst/doc/Annotation_Resources.R dependencyCount: 113 Package: arrays Version: 1.10.0 Depends: R (>= 3.0.0) Suggests: affy, limma, hgfocuscdf, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 7bb7b1bd0a1387f0ced211e2c729c2d9 NeedsCompilation: no Title: Using Bioconductor for Microarray Analysis Description: Using Bioconductor for Microarray Analysis workflow biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/arrays git_branch: RELEASE_3_9 git_last_commit: 6e41b74 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/arrays_1.10.0.tar.gz vignettes: vignettes/arrays/inst/doc/arrays.html vignetteTitles: Using Bioconductor for Microarray Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrays/inst/doc/arrays.R dependencyCount: 0 Package: BgeeCall Version: 1.0.1 Depends: R (>= 3.6.0) Imports: GenomicFeatures, rhdf5, tximport, Biostrings, rtracklayer, BgeeDB, biomaRt, methods, grDevices, graphics, stats, utils Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 MD5sum: f75018b4fc5267d52d8f4b1a7df48bb8 NeedsCompilation: no Title: BgeeCall, a R package for automatic RNA-Seq present/absent gene expression calls generation Description: Reference intergenic regions are generated by the Bgee RNA-Seq pipeliene. These intergenic regions are used to generate all Bgee RNA-Seq present/absent expression calls. This package now allows to generate your own present/absent calls using both these intergenic regions and the expertise of Bgee, as long as your species is present in Bgee. The threshold of present/absent expression is no longer arbitrary defined but is calculated based on expression of all RNA-Seq libraries integrated in Bgee. biocViews: Workflow, GeneExpressionWorkflow Author: Julien Wollbrett [aut, cre], Julien Roux [aut], Sara Fonseca Costa [ctb], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: RELEASE_3_9 git_last_commit: 558ea29 git_last_commit_date: 2019-10-14 Date/Publication: 2019-10-14 source.ver: src/contrib/BgeeCall_1.0.1.tar.gz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 92 Package: BiocMetaWorkflow Version: 1.6.0 Suggests: BiocStyle, knitr, rmarkdown, BiocWorkflowTools License: Artistic-2.0 MD5sum: 7c9bcd19bd1ed2ede1cb9802db3fb719 NeedsCompilation: no Title: BioC Workflow about publishing a Bioc Workflow Description: Bioconductor Workflow describing how to use BiocWorkflowTools to work with a single R Markdown document to submit to both Bioconductor and F1000Research. biocViews: BasicWorkflow Author: Mike Smith [aut, cre], Andrzej Oleś [aut], Wolfgang Huber [ctb] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocMetaWorkflow git_branch: RELEASE_3_9 git_last_commit: ef2c77a git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/BiocMetaWorkflow_1.6.0.tar.gz vignettes: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.html vignetteTitles: Bioc Meta Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocMetaWorkflow/inst/doc/Authoring_BioC_Workflows.R dependencyCount: 0 Package: CAGEWorkflow Version: 1.0.0 Depends: R (>= 3.6.0), nanotubes Suggests: knitr, rmarkdown, BiocWorkflowTools, pheatmap, ggseqlogo, viridis, magrittr, ggforce, ggthemes, tidyverse, dplyr, CAGEfightR, GenomicRanges, SummarizedExperiment, GenomicFeatures, BiocParallel, InteractionSet, Gviz, DESeq2, limma, edgeR, statmod, BiasedUrn, sva, TFBSTools, motifmatchr, pathview, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, JASPAR2016, png License: GPL-3 MD5sum: 0a421bc72d2384a068e6913763bb7b2e NeedsCompilation: no Title: A step-by-step guide to analyzing CAGE data using R/Bioconductor Description: Workflow for analyzing Cap Analysis of Gene Expression (CAGE) data using R/Bioconductor. biocViews: GeneExpressionWorkflow, AnnotationWorkflow Author: Malte Thodberg [aut, cre] Maintainer: Malte Thodberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEWorkflow git_branch: RELEASE_3_9 git_last_commit: c1430f8 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/CAGEWorkflow_1.0.0.tar.gz vignettes: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.pdf vignetteTitles: CAGEWorkflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.R dependencyCount: 1 Package: chipseqDB Version: 1.8.0 Suggests: chipseqDBData, BiocStyle, BiocFileCache, ChIPpeakAnno, Gviz, Rsamtools, TxDb.Mmusculus.UCSC.mm10.knownGene, csaw, edgeR, knitr, org.Mm.eg.db, rtracklayer, rmarkdown License: Artistic-2.0 MD5sum: 6d1c6a94bff2b1ffac7a6f037d9ba586 NeedsCompilation: no Title: A Bioconductor Workflow to Detect Differential Binding in ChIP-seq Data Description: Describes a computational workflow for performing a DB analysis with sliding windows. The aim is to facilitate the practical implementation of window-based DB analyses by providing detailed code and expected output. The workflow described here applies to any ChIP-seq experiment with multiple experimental conditions and multiple biological samples in one or more of the conditions. It detects and summarizes DB regions between conditions in a de novo manner, i.e., without making any prior assumptions about the location or width of bound regions. Detected regions are then annotated according to their proximity to genes. biocViews: ImmunoOncologyWorkflow, Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/chipseqDB/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseqDB git_branch: RELEASE_3_9 git_last_commit: d3b9839 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/chipseqDB_1.8.0.tar.gz vignettes: vignettes/chipseqDB/inst/doc/cbp.html, vignettes/chipseqDB/inst/doc/h3k9ac.html, vignettes/chipseqDB/inst/doc/intro.html vignetteTitles: 3. Differential binding of CBP in fibroblasts, 2. Differential enrichment of H3K9ac in B cells, 1. Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseqDB/inst/doc/cbp.R, vignettes/chipseqDB/inst/doc/h3k9ac.R, vignettes/chipseqDB/inst/doc/intro.R dependencyCount: 0 Package: csawUsersGuide Version: 1.0.0 Suggests: csaw, chipseqDBData, edgeR, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, rtracklayer, Rsamtools, Gviz, knitr, BiocStyle License: GPL-3 MD5sum: 8ae9f27ea7dafbd01a2592629c0f16d0 NeedsCompilation: no Title: csaw User's Guide Description: A user's guide for the csaw package for detecting differentially bound regions in ChIP-seq data. Describes how to read in BAM files to obtain a per-window count matrix, filtering to obtain high-abundance windows of interest, normalization of sample-specific biases, testing for differential binding, consolidation of per-window results to obtain per-region statistics, and annotation and visualization of the DB results. biocViews: Workflow, EpigeneticsWorkflow Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csawUsersGuide git_branch: RELEASE_3_9 git_last_commit: 81d83a2 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/csawUsersGuide_1.0.0.tar.gz vignettes: vignettes/csawUsersGuide/inst/doc/csaw.pdf vignetteTitles: User's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csawUsersGuide/inst/doc/csaw.R dependencyCount: 0 Package: cytofWorkflow Version: 1.8.6 Depends: R (>= 3.6.0), BiocStyle, knitr, readxl, CATALYST, diffcyt, HDCytoData, uwot, cowplot Suggests: knitcitations License: Artistic-2.0 MD5sum: a6473d4e6b72c9264568aca190fa23a1 NeedsCompilation: no Title: CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets Description: High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals). biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow Author: Malgorzata Nowicka [aut], Helena L. Crowell [aut], Mark D. Robinson [aut, cre] Maintainer: Mark D. Robinson URL: https://github.com/markrobinsonuzh/cytofWorkflow VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/cytofWorkflow/issues git_url: https://git.bioconductor.org/packages/cytofWorkflow git_branch: RELEASE_3_9 git_last_commit: f163e1e git_last_commit_date: 2019-08-23 Date/Publication: 2019-08-23 source.ver: src/contrib/cytofWorkflow_1.8.6.tar.gz vignettes: vignettes/cytofWorkflow/inst/doc/cytofWorkflow.html vignetteTitles: A workflow for differential discovery in high-throughput high-dimensional cytometry datasets hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 222 Package: EGSEA123 Version: 1.8.0 Depends: R (>= 3.4.0), EGSEA (>= 1.5.2), limma, edgeR, illuminaio Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 2f75714cd1e932aa89f3e01f0f16c682 NeedsCompilation: no Title: Easy and efficient ensemble gene set testing with EGSEA Description: R package that supports the F1000Research workflow article `Easy and efficient ensemble gene set testing with EGSEA', Alhamdoosh et al. (2017). biocViews: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow Author: Monther Alhamdoosh, Charity Law, Luyi Tian, Julie Sheridan, Milica Ng and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://www.bioconductor.org/help/workflows/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA123 git_branch: RELEASE_3_9 git_last_commit: 8075c83 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/EGSEA123_1.8.0.tar.gz vignettes: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.html vignetteTitles: Easy and efficient ensemble gene set testing with EGSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA123/inst/doc/EGSEAWorkflow.R dependencyCount: 150 Package: eQTL Version: 1.8.0 Depends: R (>= 3.3.0), GGdata, GGtools, GenomeInfoDb, S4Vectors, SNPlocs.Hsapiens.dbSNP144.GRCh37, bibtex, biglm, data.table, doParallel, foreach, knitcitations, lumi, lumiHumanAll.db, parallel, rmeta, scatterplot3d, snpStats, grid, yri1kgv Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: cd59e8c330ca4b03422210f0f52a2ca4 NeedsCompilation: no Title: Cloud-enabled cis-eQTL searches with Bioconductor GGtools 5.x Description: This workflow focuses on searches for eQTL in cis, so that DNA variants local to the gene assayed for expression are tested for association. biocViews: Workflow, GenomicVariantsWorkflow Author: Vincent Carey [aut, cre] Maintainer: Vincent Carey URL: https://www.bioconductor.org/help/workflows/eQTL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eQTL git_branch: RELEASE_3_9 git_last_commit: ba69f96 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/eQTL_1.8.0.tar.gz vignettes: vignettes/eQTL/inst/doc/cloudeqtl.html vignetteTitles: Cloud-enabled cis-eQTL searches with Bioconductor GGtools 5.x hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eQTL/inst/doc/cloudeqtl.R dependencyCount: 202 Package: ExpressionNormalizationWorkflow Version: 1.10.0 Imports: Biobase (>= 2.24.0), limma (>= 3.20.9), lme4 (>= 1.1.7), matrixStats (>= 0.10.3), pvca (>= 1.4.0), snm (>= 1.12.0), sva (>= 3.10.0), vsn (>= 3.32.0) Suggests: knitr, BiocStyle License: GPL (>=3) MD5sum: a105a23c28c5a79b79b05b86545ad44b NeedsCompilation: no Title: Gene Expression Normalization Workflow Description: An extensive, customized expression normalization workflow incorporating Supervised Normalization of Microarryas(SNM), Surrogate Variable Analysis(SVA) and Principal Variance Component Analysis to identify batch effects and remove them from the expression data to enhance the ability to detect the underlying biological signals. biocViews: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow Author: Karthikeyan Murugesan [aut, cre], Greg Gibson [sad, ths] Maintainer: Karthikeyan Murugesan VignetteBuilder: knitr BugReports: https://github.com/ git_url: https://git.bioconductor.org/packages/ExpressionNormalizationWorkflow git_branch: RELEASE_3_9 git_last_commit: 2fbdd7b git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/ExpressionNormalizationWorkflow_1.10.0.tar.gz vignettes: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.html vignetteTitles: Gene Expression Normalization Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionNormalizationWorkflow/inst/doc/genExpNrm.R dependencyCount: 91 Package: generegulation Version: 1.8.0 Depends: R (>= 3.3.0), BSgenome.Scerevisiae.UCSC.sacCer3, Biostrings, GenomicFeatures, MotifDb, S4Vectors, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, motifStack, org.Sc.sgd.db, seqLogo Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 07b7c45aa8d314f58901f76a97ef0040 NeedsCompilation: no Title: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching Description: The binding of transcription factor proteins (TFs) to DNA promoter regions upstream of gene transcription start sites (TSSs) is one of the most important mechanisms by which gene expression, and thus many cellular processes, are controlled. Though in recent years many new kinds of data have become available for identifying transcription factor binding sites (TFBSs) -- ChIP-seq and DNase I hypersensitivity regions among them -- sequence matching continues to play an important role. In this workflow we demonstrate Bioconductor techniques for finding candidate TF binding sites in DNA sequence using the model organism Saccharomyces cerevisiae. The methods demonstrated here apply equally well to other organisms. biocViews: Workflow, EpigeneticsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/generegulation/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/generegulation git_branch: RELEASE_3_9 git_last_commit: a304bff git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/generegulation_1.8.0.tar.gz vignettes: vignettes/generegulation/inst/doc/generegulation.html vignetteTitles: Finding Candidate Binding Sites for Known Transcription Factors via Sequence Matching hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/generegulation/inst/doc/generegulation.R dependencyCount: 99 Package: highthroughputassays Version: 1.8.0 Depends: R (>= 3.3.0), flowCore, flowStats, flowViz Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b6d1a49af6d1ae04415f079d02b30c28 NeedsCompilation: no Title: Using Bioconductor with High Throughput Assays Description: The workflow illustrates use of the flow cytometry packages to load, transform and visualize the flow data and gate certain populations in the dataset. The workflow loads the `flowCore`, `flowStats` and `flowViz` packages and its dependencies. It loads the ITN data with 15 samples, each of which includes, in addition to FSC and SSC, 5 fluorescence channels: CD3, CD4, CD8, CD69 and HLADR. biocViews: ImmunoOncologyWorkflow, Workflow, ProteomicsWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/highthroughputassays/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/highthroughputassays git_branch: RELEASE_3_9 git_last_commit: 809d5bd git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/highthroughputassays_1.8.0.tar.gz vignettes: vignettes/highthroughputassays/inst/doc/high-throughput-assays.html vignetteTitles: Using Bioconductor with High Throughput Assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/highthroughputassays/inst/doc/high-throughput-assays.R dependencyCount: 83 Package: liftOver Version: 1.8.0 Depends: R (>= 3.3.0), gwascat, GenomicRanges, rtracklayer, Homo.sapiens, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 55c652dded0305dae4fc2ac4deea85f6 NeedsCompilation: no Title: Changing genomic coordinate systems with rtracklayer::liftOver Description: The liftOver facilities developed in conjunction with the UCSC browser track infrastructure are available for transforming data in GRanges formats. This is illustrated here with an image of the EBI/NHGRI GWAS catalog that is, as of May 10 2017, distributed with coordinates defined by NCBI build hg38. biocViews: Workflow, BasicWorkflow Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/liftOver/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/liftOver git_branch: RELEASE_3_9 git_last_commit: ee71f68 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/liftOver_1.8.0.tar.gz vignettes: vignettes/liftOver/inst/doc/liftov.html vignetteTitles: Changing genomic coordinate systems with rtracklayer::liftOver hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/liftOver/inst/doc/liftov.R dependencyCount: 82 Package: maEndToEnd Version: 2.4.0 Depends: R (>= 3.5.0), Biobase, oligoClasses, ArrayExpress, pd.hugene.1.0.st.v1, hugene10sttranscriptcluster.db, oligo, arrayQualityMetrics, limma, topGO, ReactomePA, clusterProfiler, gplots, ggplot2, geneplotter, pheatmap, RColorBrewer, dplyr, tidyr, stringr, matrixStats, genefilter, openxlsx, Rgraphviz Suggests: BiocStyle, knitr, devtools, rmarkdown License: MIT MD5sum: b0a805e7d8e69b28e8c4bbc63f4d1079 NeedsCompilation: no Title: An end to end workflow for differential gene expression using Affymetrix microarrays Description: In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene" type arrays, e.g. the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis. biocViews: GeneExpressionWorkflow Author: Bernd Klaus [aut, cre], Stefanie Reisenauer [aut] Maintainer: Bernd Klaus URL: https://www.bioconductor.org/help/workflows/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/maEndToEnd git_branch: RELEASE_3_9 git_last_commit: ff896ca git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/maEndToEnd_2.4.0.tar.gz vignettes: vignettes/maEndToEnd/inst/doc/MA-Workflow.html vignetteTitles: An end to end workflow for differential gene expression using Affymetrix microarrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maEndToEnd/inst/doc/MA-Workflow.R dependencyCount: 198 Package: methylationArrayAnalysis Version: 1.8.1 Depends: R (>= 3.3.0), knitr, rmarkdown, BiocStyle, limma, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, RColorBrewer, missMethyl, matrixStats, minfiData, Gviz, DMRcate, stringr, FlowSorted.Blood.450k License: Artistic-2.0 MD5sum: b2af4a1f66beec85eb59a2be2dade6dd NeedsCompilation: no Title: A cross-package Bioconductor workflow for analysing methylation array data. Description: Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This Bioconductor workflow uses multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data. biocViews: Workflow, EpigeneticsWorkflow Author: Jovana Maksimovic [aut, cre] Maintainer: Jovana Maksimovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylationArrayAnalysis git_branch: RELEASE_3_9 git_last_commit: 4f472a0 git_last_commit_date: 2019-06-06 Date/Publication: 2019-06-07 source.ver: src/contrib/methylationArrayAnalysis_1.8.1.tar.gz vignettes: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.html vignetteTitles: A cross-package Bioconductor workflow for analysing methylation array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylationArrayAnalysis/inst/doc/methylationArrayAnalysis.R dependencyCount: 195 Package: proteomics Version: 1.8.0 Depends: R (>= 3.3.0), mzR, mzID, MSnID, MSnbase, rpx, MLInterfaces, pRoloc, pRolocdata, MSGFplus, rols, hpar Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 74ee30ebc486c01107f367e6bc0ecbcc NeedsCompilation: no Title: Mass spectrometry and proteomics data analysis Description: This workflow illustrates R / Bioconductor infrastructure for proteomics. Topics covered focus on support for open community-driven formats for raw data and identification results, packages for peptide-spectrum matching, data processing and analysis. biocViews: ImmunoOncologyWorkflow, ProteomicsWorkflow, Workflow Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: https://www.bioconductor.org/help/workflows/proteomics/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proteomics git_branch: RELEASE_3_9 git_last_commit: 3ea1968 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-06 source.ver: src/contrib/proteomics_1.8.0.tar.gz vignettes: vignettes/proteomics/inst/doc/proteomics.html vignetteTitles: An R/Bioc proteomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteomics/inst/doc/proteomics.R dependencyCount: 193 Package: recountWorkflow Version: 1.8.1 Depends: R (>= 3.4.0) Imports: recount (>= 1.2.3), GenomicRanges, limma, edgeR, DESeq2, regionReport (>= 1.11.2), clusterProfiler (>= 3.5.5), org.Hs.eg.db (>= 3.4.1), gplots, derfinder, rtracklayer (>= 1.36.4), GenomicFeatures, bumphunter (>= 1.17.2), derfinderPlot Suggests: BiocStyle (>= 2.5.19), BiocWorkflowTools, knitr, sessioninfo, rmarkdown, knitcitations License: Artistic-2.0 MD5sum: 7eda31ab2e06e5bc58a42b86c80396e3 NeedsCompilation: no Title: recount workflow: accessing over 70,000 human RNA-seq samples with Bioconductor Description: The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recount Bioconductor package. This workflow explains in detail how to use the recount package and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data. biocViews: Workflow, ResourceQueryingWorkflow Author: Leonardo Collado-Torres [aut, cre], Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recountWorkflow VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recountWorkflow/ git_url: https://git.bioconductor.org/packages/recountWorkflow git_branch: RELEASE_3_9 git_last_commit: 216b263 git_last_commit_date: 2019-05-22 Date/Publication: 2019-05-29 source.ver: src/contrib/recountWorkflow_1.8.1.tar.gz vignettes: vignettes/recountWorkflow/inst/doc/recount-workflow.html vignetteTitles: recount workflow: accessing over 70,,000 human RNA-seq samples with Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountWorkflow/inst/doc/recount-workflow.R dependencyCount: 210 Package: RNAseq123 Version: 1.8.0 Depends: R (>= 3.3.0), Glimma (>= 1.1.9), limma, edgeR, gplots, RColorBrewer, Mus.musculus Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 46a31e4035b479ed6655defe4876bf1d NeedsCompilation: no Title: RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR Description: R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016). biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Charity Law, Monther Alhamdoosh, Shian Su, Xueyi Dong, Luyi Tian, Gordon Smyth and Matthew Ritchie Maintainer: Matthew Ritchie URL: https://f1000research.com/articles/5-1408/v3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAseq123 git_branch: RELEASE_3_9 git_last_commit: c02c562 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/RNAseq123_1.8.0.tar.gz vignettes: vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.html, vignettes/RNAseq123/inst/doc/limmaWorkflow.html vignetteTitles: RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (Chinese version), RNA-seq analysis is easy as 1-2-3 with limma,, Glimma and edgeR (English version) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.R, vignettes/RNAseq123/inst/doc/limmaWorkflow.R dependencyCount: 91 Package: rnaseqDTU Version: 1.4.0 Depends: R (>= 3.5.0), DRIMSeq, DEXSeq, stageR, DESeq2, edgeR, rafalib, devtools Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 5b342faf084de3d51ce0d7fb1ab2bbec NeedsCompilation: no Title: RNA-seq workflow for differential transcript usage following Salmon quantification Description: RNA-seq workflow for differential transcript usage (DTU) following Salmon quantification. This workflow uses Bioconductor packages tximport, DRIMSeq, and DEXSeq to perform a DTU analysis on simulated data. It also shows how to use stageR to perform two-stage testing of DTU, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Michael Love [aut, cre], Charlotte Soneson [aut], Rob Patro [aut] Maintainer: Michael Love URL: https://github.com/mikelove/rnaseqDTU/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqDTU git_branch: RELEASE_3_9 git_last_commit: 061e64e git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/rnaseqDTU_1.4.0.tar.gz vignettes: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.html vignetteTitles: RNA-seq workflow for differential transcript usage following Salmon quantification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqDTU/inst/doc/rnaseqDTU.R dependencyCount: 168 Package: rnaseqGene Version: 1.8.0 Depends: R (>= 3.3.0), BiocStyle, airway, Rsamtools, GenomicFeatures, GenomicAlignments, BiocParallel, magrittr, DESeq2, apeglm, vsn, dplyr, ggplot2, pheatmap, RColorBrewer, PoiClaClu, ggbeeswarm, genefilter, AnnotationDbi, org.Hs.eg.db, ReportingTools, Gviz, sva, RUVSeq, fission Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 5089953a4acc3fc8c7192edb90727559 NeedsCompilation: no Title: RNA-seq workflow: gene-level exploratory analysis and differential expression Description: Here we walk through an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Michael Love [aut, cre] Maintainer: Michael Love URL: https://github.com/mikelove/rnaseqGene/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqGene git_branch: RELEASE_3_9 git_last_commit: 1ca74bc git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/rnaseqGene_1.8.0.tar.gz vignettes: vignettes/rnaseqGene/inst/doc/rnaseqGene.html vignetteTitles: RNA-seq workflow at the gene level hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqGene/inst/doc/rnaseqGene.R dependencyCount: 186 Package: RnaSeqGeneEdgeRQL Version: 1.8.0 Depends: R (>= 3.3.0), edgeR, gplots, org.Mm.eg.db, GO.db Suggests: knitr, knitcitations License: Artistic-2.0 MD5sum: 4ee23409f7836e901faf7cbe253bd259 NeedsCompilation: no Title: Gene-level RNA-seq differential expression and pathway analysis using Rsubread and the edgeR quasi-likelihood pipeline Description: This workflow package provides, through its vignette, a complete case study analysis of an RNA-Seq experiment using the Rsubread and edgeR packages. The workflow starts from read alignment and continues on to data exploration, to differential expression and, finally, to pathway analysis. The analysis includes publication quality plots, GO and KEGG analyses, and the analysis of a expression signature as generated by a prior experiment. biocViews: Workflow, GeneExpressionWorkflow, ImmunoOncologyWorkflow Author: Yunshun Chen, Aaron Lun, Gordon Smyth Maintainer: Yunshun Chen URL: http://f1000research.com/articles/5-1438 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqGeneEdgeRQL git_branch: RELEASE_3_9 git_last_commit: c1a3f5b git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/RnaSeqGeneEdgeRQL_1.8.0.tar.gz vignettes: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html vignetteTitles: From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.R dependencyCount: 46 Package: sequencing Version: 1.8.0 Depends: R (>= 3.3.0), GenomicRanges, GenomicAlignments, Biostrings, Rsamtools, ShortRead, BiocParallel, rtracklayer, VariantAnnotation, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, RNAseqData.HNRNPC.bam.chr14 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: bcbcb0670933d4f0493229b8d1f703d2 NeedsCompilation: no Title: Introduction to Bioconductor for Sequence Data Description: Bioconductor enables the analysis and comprehension of high- throughput genomic data. We have a vast number of packages that allow rigorous statistical analysis of large data while keeping technological artifacts in mind. Bioconductor helps users place their analytic results into biological context, with rich opportunities for visualization. Reproducibility is an important goal in Bioconductor analyses. Different types of analysis can be carried out using Bioconductor, for example; Sequencing : RNASeq, ChIPSeq, variants, copy number etc.; Microarrays: expression, SNP, etc.; Domain specific analysis : Flow cytometry, Proteomics etc. For these analyses, one typically imports and works with diverse sequence-related file types, including fasta, fastq, BAM, gtf, bed, and wig files, among others. Bioconductor packages support import, common and advanced sequence manipulation operations such as trimming, transformation, and alignment including quality assessment. biocViews: ImmunoOncologyWorkflow, Workflow, BasicWorkflow Author: Sonali Arora [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/sequencing/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sequencing git_branch: RELEASE_3_9 git_last_commit: 6cd53a3 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/sequencing_1.8.0.tar.gz vignettes: vignettes/sequencing/inst/doc/sequencing.html vignetteTitles: Introduction to Bioconductor for Sequence Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sequencing/inst/doc/sequencing.R dependencyCount: 104 Package: simpleSingleCell Version: 1.8.0 Imports: BiocStyle, callr, rmarkdown Suggests: knitr, readxl, R.utils, Matrix, SingleCellExperiment, scater, scran, DropletUtils, org.Hs.eg.db, org.Mm.eg.db, EnsDb.Hsapiens.v86, TxDb.Mmusculus.UCSC.mm10.ensGene, dynamicTreeCut, cluster, igraph, Rtsne, pheatmap, limma, edgeR, BiocParallel, BiocFileCache, BiocNeighbors, BiocSingular, batchelor, scRNAseq, TENxBrainData License: Artistic-2.0 MD5sum: ba46f140144a3221074ef544b22df79b NeedsCompilation: no Title: A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor Description: This workflow implements a low-level analysis pipeline for scRNA-seq data using scran, scater and other Bioconductor packages. It describes how to perform quality control on the libraries, normalization of cell-specific biases, basic data exploration, cell cycle phase identification, doublet detection and batch correction. Procedures to detect highly variable genes, significantly correlated genes and subpopulation-specific marker genes are also shown. These analyses are demonstrated on publicly available scRNA-seq data sets from a variety of protocols including SMART-seq2 and 10X Genomics. biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow Author: Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut] Maintainer: Aaron Lun URL: https://www.bioconductor.org/help/workflows/simpleSingleCell/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simpleSingleCell git_branch: RELEASE_3_9 git_last_commit: 0a95c65 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/simpleSingleCell_1.8.0.tar.gz vignettes: vignettes/simpleSingleCell/inst/doc/batch.html, vignettes/simpleSingleCell/inst/doc/bigdata.html, vignettes/simpleSingleCell/inst/doc/de.html, vignettes/simpleSingleCell/inst/doc/doublets.html, vignettes/simpleSingleCell/inst/doc/intro.html, vignettes/simpleSingleCell/inst/doc/misc.html, vignettes/simpleSingleCell/inst/doc/qc.html, vignettes/simpleSingleCell/inst/doc/reads.html, vignettes/simpleSingleCell/inst/doc/spike.html, vignettes/simpleSingleCell/inst/doc/tenx.html, vignettes/simpleSingleCell/inst/doc/umis.html, vignettes/simpleSingleCell/inst/doc/var.html vignetteTitles: 05. Correcting batch effects, 11. Scalability for big data, 10. Detecting differential expression, 08. Detecting doublets, 01. Introduction, 12. Further analysis strategies, 06. Quality control details, 02. Read count data, 07. Spike-in normalization, 04. Droplet-based data, 03. UMI count data, 09. Advanced variance modelling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSingleCell/inst/doc/batch.R, vignettes/simpleSingleCell/inst/doc/bigdata.R, vignettes/simpleSingleCell/inst/doc/de.R, vignettes/simpleSingleCell/inst/doc/doublets.R, vignettes/simpleSingleCell/inst/doc/intro.R, vignettes/simpleSingleCell/inst/doc/misc.R, vignettes/simpleSingleCell/inst/doc/qc.R, vignettes/simpleSingleCell/inst/doc/reads.R, vignettes/simpleSingleCell/inst/doc/spike.R, vignettes/simpleSingleCell/inst/doc/tenx.R, vignettes/simpleSingleCell/inst/doc/umis.R, vignettes/simpleSingleCell/inst/doc/var.R dependencyCount: 30 Package: SingscoreAMLMutations Version: 1.0.1 Depends: R (>= 3.6.0) Imports: dcanr, edgeR, ggplot2, gridExtra, GSEABase, org.Hs.eg.db, plyr, reshape2, rtracklayer, singscore, SummarizedExperiment, TCGAbiolinks Suggests: knitr, rmarkdown, BiocStyle, BiocWorkflowTools, spelling License: Artistic-2.0 MD5sum: c3f0740d5d57586c086ca5e3b6be7fc3 NeedsCompilation: no Title: Using singscore to predict mutations in AML from transcriptomic signatures Description: This workflow package shows how transcriptomic signatures can be used to infer phenotypes. The workflow begins by showing how the TCGA AML transcriptomic data can be downloaded and processed using the TCGAbiolinks packages. It then shows how samples can be scored using the singscore package and signatures from the MSigDB. Finally, the predictive capacity of scores in the context of predicting a specific mutation in AML is shown.The workflow exhibits the interplay of Bioconductor packages to achieve a gene-set level analysis. biocViews: GeneExpressionWorkflow, GenomicVariantsWorkflow, ImmunoOncologyWorkflow, Workflow Author: Dharmesh D. Bhuva [aut, cre] (), Momeneh Foroutan [aut] (), Yi Xie [aut] (), Ruqian Lyu [aut], Joseph Cursons [aut] (), Melissa J. Davis [aut] () Maintainer: Dharmesh D. Bhuva URL: https://github.com/DavisLaboratory/SingscoreAMLMutations VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/SingscoreAMLMutations/issues git_url: https://git.bioconductor.org/packages/SingscoreAMLMutations git_branch: RELEASE_3_9 git_last_commit: 03cafd5 git_last_commit_date: 2019-06-26 Date/Publication: 2019-06-28 source.ver: src/contrib/SingscoreAMLMutations_1.0.1.tar.gz vignettes: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.html, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.html vignetteTitles: Using singscore to predict mutations in AML from transcriptomic signatures (Chinese version), Using singscore to predict mutations in AML from transcriptomic signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig_chinese.R, vignettes/SingscoreAMLMutations/inst/doc/workflow_transcriptional_mut_sig.R dependencyCount: 190 Package: TCGAWorkflow Version: 1.8.1 Depends: R (>= 3.4.0) Imports: AnnotationHub, knitr, ELMER, biomaRt, BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker, ComplexHeatmap, clusterProfiler, downloader (>= 0.4), gaia, GenomicRanges, GenomeInfoDb, ggplot2, ggthemes, graphics, minet, MotIV, motifStack, pathview, pbapply, parallel, rGADEM, pander, maftools, RTCGAToolbox, SummarizedExperiment, TCGAbiolinks, TCGAWorkflowData (>= 1.8.0), DT License: Artistic-2.0 MD5sum: 2a7c8c4303fe49b77ce01e99ee803f8e NeedsCompilation: no Title: TCGA Workflow Analyze cancer genomics and epigenomics data using Bioconductor packages Description: Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). biocViews: Workflow, ResourceQueryingWorkflow Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Fulvio D Angelo , Gianluca Bontempi , Michele Ceccarelli , Houtan Noushmehr Maintainer: Tiago Chedraoui Silva URL: https://f1000research.com/articles/5-1542/v2 VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAWorkflow/issues git_url: https://git.bioconductor.org/packages/TCGAWorkflow git_branch: RELEASE_3_9 git_last_commit: 1732071 git_last_commit_date: 2019-08-25 Date/Publication: 2019-08-26 source.ver: src/contrib/TCGAWorkflow_1.8.1.tar.gz vignettes: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.html vignetteTitles: 'TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages' hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAWorkflow/inst/doc/TCGAWorkflow.R dependencyCount: 280 Package: variants Version: 1.8.0 Depends: R (>= 3.3.0), VariantAnnotation, cgdv17, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131 Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: c91e761e983868f2a2bd278687b996a9 NeedsCompilation: no Title: Annotating Genomic Variants Description: Read and write VCF files. Identify structural location of variants and compute amino acid coding changes for non-synonymous variants. Use SIFT and PolyPhen database packages to predict consequence of amino acid coding changes biocViews: ImmunoOncologyWorkflow, AnnotationWorkflow, Workflow Author: Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://www.bioconductor.org/help/workflows/variants/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/variants git_branch: RELEASE_3_9 git_last_commit: 659b328 git_last_commit_date: 2019-05-02 Date/Publication: 2019-05-03 source.ver: src/contrib/variants_1.8.0.tar.gz vignettes: vignettes/variants/inst/doc/Annotating_Genomic_Variants.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variants/inst/doc/Annotating_Genomic_Variants.R dependencyCount: 80