Package: annotation
Version: 1.20.0
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: effeff6a7f629861716991f1a13c6a7d
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
<maintainer@bioconductor.org>
URL:
http://bioconductor.org/help/workflows/annotation/Annotation_Resources/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/annotation
git_branch: RELEASE_3_15
git_last_commit: ee5d921
git_last_commit_date: 2022-04-26
Date/Publication: 2022-05-10
source.ver: src/contrib/annotation_1.20.0.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: 135
Package: arrays
Version: 1.22.0
Depends: R (>= 3.0.0)
Suggests: affy, limma, hgfocuscdf, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 37e01ef7d99e4aa8303ba2ebb4571080
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
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/arrays
git_branch: RELEASE_3_15
git_last_commit: fd62e32
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/arrays_1.22.0.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: BiocMetaWorkflow
Version: 1.18.0
Suggests: BiocStyle, knitr, rmarkdown, BiocWorkflowTools
License: Artistic-2.0
MD5sum: 530d8ef7fa1c06aa674476b1813bd5cf
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 OleÅ› [aut], Wolfgang Huber [ctb]
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocMetaWorkflow
git_branch: RELEASE_3_15
git_last_commit: a968965
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/BiocMetaWorkflow_1.18.0.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: BP4RNAseq
Version: 1.6.0
Depends: R (>= 4.0.0)
Imports: dplyr, fastqcr, stringr, tidyr, stats, utils, magrittr,
reticulate
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 31303c7e1d18ad8cdfc88008da6c73d2
NeedsCompilation: no
Title: A babysitter's package for reproducible RNA-seq analysis
Description: An automated pipe for reproducible RNA-seq analysis with
the minimal efforts from researchers. The package can process
bulk RNA-seq data and single-cell RNA-seq data. You can only
provide the taxa name and the accession id of RNA-seq data
deposited in the National Center for Biotechnology Information
(NCBI). After a cup of tea or longer, you will get formated
gene expression data as gene count and transcript count based
on both alignment-based and alignment-free workflows.
biocViews: GeneExpressionWorkflow
Author: Shanwen Sun [cre, aut], Lei Xu [aut], Quan Zou [aut]
Maintainer: Shanwen Sun <sunshanwen@gmail.com>
SystemRequirements: UNIX, SRA Toolkit=2.10.3, Entrez Direct=13.3,
FastQC=v0.11.9, Cutadapt=2.10, datasets, SAMtools=1.9,
HISAT2=2.2.0, StringTie=2.1.1, Salmon=1.2.1
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BP4RNAseq
git_branch: RELEASE_3_15
git_last_commit: 261599e
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/BP4RNAseq_1.6.0.tar.gz
vignettes: vignettes/BP4RNAseq/inst/doc/vignette.html
vignetteTitles: BP4RNAseq vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BP4RNAseq/inst/doc/vignette.R
dependencyCount: 91
Package: CAGEWorkflow
Version: 1.12.0
Depends: R (>= 3.6.0), CAGEfightR, nanotubes
Suggests: knitr, magick, rmarkdown, BiocStyle, BiocWorkflowTools,
pheatmap, ggseqlogo, viridis, magrittr, ggforce, ggthemes,
tidyverse, dplyr, 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: d8533bf0d21432aab9f37e07db9b4d7d
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 <maltethodberg@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CAGEWorkflow
git_branch: RELEASE_3_15
git_last_commit: 502360e
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/CAGEWorkflow_1.12.0.tar.gz
vignettes: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.html
vignetteTitles: CAGEWorkflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAGEWorkflow/inst/doc/CAGEWorkflow.R
dependencyCount: 155
Package: chipseqDB
Version: 1.20.0
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: a46c8b59e9e1431b07af6aa8df939c74
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 <infinite.monkeys.with.keyboards@gmail.com>
URL: https://www.bioconductor.org/help/workflows/chipseqDB/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chipseqDB
git_branch: RELEASE_3_15
git_last_commit: e1e51f9
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/chipseqDB_1.20.0.tar.gz
vignettes: vignettes/chipseqDB/inst/doc/cbp.html,
vignettes/chipseqDB/inst/doc/h3k27me3.html,
vignettes/chipseqDB/inst/doc/h3k9ac.html,
vignettes/chipseqDB/inst/doc/intro.html
vignetteTitles: 3. Differential binding of CBP in fibroblasts, 4.
Differential enrichment of H3K27me3 in lung epithelium, 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
dependencyCount: 0
Package: csawUsersGuide
Version: 1.12.0
Suggests: knitr, BiocStyle, BiocManager
License: GPL-3
MD5sum: 2ab5d0973a90d79611765fca787fbac2
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 <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/csawUsersGuide
git_branch: RELEASE_3_15
git_last_commit: 6d2ee7f
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/csawUsersGuide_1.12.0.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: 1.20.0
Depends: R (>= 3.6.0), BiocStyle, knitr, readxl, CATALYST, diffcyt,
HDCytoData, uwot, cowplot
Suggests: knitcitations, markdown, rmarkdown
License: Artistic-2.0
MD5sum: 53d2bd86f00c85f04bb97b5a2f8b4391
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 <mark.robinson@imls.uzh.ch>
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_15
git_last_commit: b999230
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/cytofWorkflow_1.20.0.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: 296
Package: EGSEA123
Version: 1.20.0
Depends: R (>= 3.4.0), EGSEA (>= 1.5.2), limma (>= 3.49.2), edgeR,
illuminaio
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 882cb7a06901c99c6a007cbdf4d152b8
NeedsCompilation: no
Title: Easy and efficient ensemble gene set testing with EGSEA
Description: R package that supports the workflow article `Easy and
efficient ensemble gene set testing with EGSEA', Alhamdoosh et
al. (2017), F1000Research, 6:2010.
biocViews: ImmunoOncologyWorkflow, Workflow, GeneExpressionWorkflow
Author: Monther Alhamdoosh, Charity Law, Luyi Tian, Julie Sheridan,
Milica Ng and Matthew Ritchie
Maintainer: Matthew Ritchie <mritchie@wehi.edu.au>
URL: https://f1000research.com/articles/6-2010
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EGSEA123
git_branch: RELEASE_3_15
git_last_commit: 9930bd3
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/EGSEA123_1.20.0.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: 179
Package: ExpHunterSuite
Version: 1.4.0
Depends: R (>= 4.1.0)
Imports: ReactomePA, limma, edgeR, NOISeq, biomaRt, topGO,
clusterProfiler, diffcoexp, DT, ggplot2, stringr, WGCNA, dplyr,
AnnotationDbi, BiocGenerics, enrichplot, rmarkdown, stats,
Biobase, DESeq2, ROCR, data.table, knitr, magrittr,
SummarizedExperiment, miRBaseConverter, pbapply, grDevices,
graphics, utils, BiocParallel, MKinfer, matrixStats, rlang,
plyr, tidyr
Suggests: optparse, PerformanceAnalytics, naivebayes, reshape2, TCC,
org.Hs.eg.db, org.Mm.eg.db
License: MIT + file LICENSE
MD5sum: 2a26ded30956d7c507880219949eca22
NeedsCompilation: no
Title: Package For The Comprehensive Analysis Of Transcriptomic Data
Description: The ExpHunterSuite implements a comprehensive protocol for
the analysis of transcriptional data using established *R*
packages and combining their results. It covers all key steps
in DEG detection, CEG detection and functional analysis for
RNA-seq data. It has been implemented as an R package
containing functions that can be run interactively. In
addition, it also contains scripts that wrap the functions and
can be run directly from the command line.
biocViews: GeneExpressionWorkflow
Author: James Perkins [aut, cre]
(<https://orcid.org/0000-0003-4108-096X>), Pedro Seoane Zonjic
[aut] (<https://orcid.org/0000-0002-3020-1415>), Fernando
Moreno Jabato [aut] (<https://orcid.org/0000-0001-7498-1962>),
José Córdoba Caballero [aut]
(<https://orcid.org/0000-0002-1821-5742>), Elena Rojano Rivera
[aut] (<https://orcid.org/0000-0002-2678-710X>), Rocio Bautista
Moreno [aut] (<https://orcid.org/0000-0003-1685-8119>), M.
Gonzalo Claros [aut] (<https://orcid.org/0000-0002-0112-3550>),
Isabel Gonzalez Gayte [aut], Juan Antonio GarcÃa Ranea [aut]
(<https://orcid.org/0000-0003-0327-1837>)
Maintainer: James Perkins <jimrperkins@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExpHunterSuite
git_branch: RELEASE_3_15
git_last_commit: 8075fe8
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/ExpHunterSuite_1.4.0.tar.gz
vignettes: vignettes/ExpHunterSuite/inst/doc/hunter.html
vignetteTitles: The Expression Hunter Suite
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExpHunterSuite/inst/doc/hunter.R
dependencyCount: 218
Package: ExpressionNormalizationWorkflow
Version: 1.22.0
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: 9ae890191bfb77b5a9600724c19601ca
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 <karthikeyanm60@yahoo.com>
VignetteBuilder: knitr
BugReports: https://github.com/
git_url:
https://git.bioconductor.org/packages/ExpressionNormalizationWorkflow
git_branch: RELEASE_3_15
git_last_commit: 464e305
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/ExpressionNormalizationWorkflow_1.22.0.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: 117
Package: fluentGenomics
Version: 1.8.0
Depends: R (>= 4.0)
Imports: plyranges (>= 1.7.7), dplyr, SummarizedExperiment, readr,
stats, utils
Suggests: knitr, rmarkdown, bookdown, rappdirs, BiocFileCache, DESeq2,
limma, ggplot2, tidyr, tximeta (>= 1.4.2), macrophage (>=
1.2.0),
License: MIT + file LICENSE
MD5sum: e4dc4caf10a0acf730e09f6ae6c8d080
NeedsCompilation: no
Title: A plyranges and tximeta workflow
Description: An extended workflow using the plyranges and tximeta
packages for fluent genomic data analysis. Use tximeta to
correctly import RNA-seq transcript quantifications and
summarize them to gene counts for downstream analysis. Use
plyranges for clearly expressing operations over genomic
coordinates and to combine results from differential expression
and differential accessibility analyses.
biocViews: Workflow, BasicWorkflow, GeneExpressionWorkflow
Author: Stuart Lee [aut, cre]
(<https://orcid.org/0000-0003-1179-8436>), Michael Love [aut,
ctb]
Maintainer: Stuart Lee <lee.s@wehi.edu.au>
URL: https://github.com/sa-lee/fluentGenomics
VignetteBuilder: knitr, rmarkdown
BugReports: https://github.com/sa-lee/fluentGenomics/issues
git_url: https://git.bioconductor.org/packages/fluentGenomics
git_branch: RELEASE_3_15
git_last_commit: 73b65cf
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/fluentGenomics_1.8.0.tar.gz
vignettes: vignettes/fluentGenomics/inst/doc/fluentGenomics.html
vignetteTitles: fluentGenomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/fluentGenomics/inst/doc/fluentGenomics.R
dependencyCount: 73
Package: generegulation
Version: 1.20.0
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: 329c03b1dd69a3d7ec9e4face0ec50d2
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
<maintainer@bioconductor.org>
URL: https://www.bioconductor.org/help/workflows/generegulation/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/generegulation
git_branch: RELEASE_3_15
git_last_commit: 68a6f5c
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/generegulation_1.20.0.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: 147
Package: GeoMxWorkflows
Version: 1.2.0
Depends: R (>= 4.0), NanoStringNCTools, GeomxTools
Imports: Biobase, S4Vectors, rjson, readxl, EnvStats, dplyr, reshape2,
methods, utils, stats, data.table, outliers, BiocGenerics,
ggplot2, ggrepel, ggforce, cowplot, scales, umap, Rtsne,
pheatmap, BiocStyle
Suggests: rmarkdown, knitr
License: MIT
MD5sum: 9655a523bd31ff368301d5a282a0c46d
NeedsCompilation: no
Title: GeoMx Digital Spatial Profiler (DSP) data analysis workflows
Description: Workflows for use with NanoString Technologies GeoMx
Technology. Package provides bioconductor focused workflows
for leveraging existing packages (e.g. GeomxTools) to process,
QC, and analyze the data.
biocViews: GeneExpressionWorkflow, ImmunoOncologyWorkflow,
SpatialWorkflow
Author: Jason Reeves [cre, aut], Prajan Divakar [aut], Nicole Ortogero
[aut], Maddy Griswold [aut], Zhi Yang [aut], Stephanie
Zimmerman [aut], Rona Vitancol [aut], Henderson David [aut]
Maintainer: Jason Reeves <jreeves@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeoMxWorkflows
git_branch: RELEASE_3_15
git_last_commit: 5abb724
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/GeoMxWorkflows_1.2.0.tar.gz
vignettes:
vignettes/GeoMxWorkflows/inst/doc/GeomxTools_RNA-NGS_Analysis.html
vignetteTitles: Analyzing GeoMx-NGS Data with GeomxTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeoMxWorkflows/inst/doc/GeomxTools_RNA-NGS_Analysis.R
dependencyCount: 153
Package: highthroughputassays
Version: 1.20.0
Depends: R (>= 3.3.0), flowCore, flowStats, flowWorkspace
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: f50a9594516ccdc26e235da6d9b7e74a
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 `flowWorkspace` 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
<maintainer@bioconductor.org>
URL: https://www.bioconductor.org/help/workflows/highthroughputassays/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/highthroughputassays
git_branch: RELEASE_3_15
git_last_commit: e88732f
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/highthroughputassays_1.20.0.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: 112
Package: liftOver
Version: 1.20.0
Depends: R (>= 3.3.0), gwascat, GenomicRanges, rtracklayer,
Homo.sapiens, BiocGenerics
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 4fa8701bb123f949e3afef4b310e3e38
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
<maintainer@bioconductor.org>
URL: https://www.bioconductor.org/help/workflows/liftOver/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/liftOver
git_branch: RELEASE_3_15
git_last_commit: 3937e13
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/liftOver_1.20.0.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: 137
Package: maEndToEnd
Version: 2.16.0
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,
enrichplot
Suggests: BiocStyle, knitr, devtools, rmarkdown
License: MIT + file LICENSE
MD5sum: 991563306dc94b0f488f1471c7aa9039
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], Stefanie Reisenauer [aut, cre]
Maintainer: Stefanie Reisenauer <steffi.reisenauer@tum.de>
URL: https://www.bioconductor.org/help/workflows/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/maEndToEnd
git_branch: RELEASE_3_15
git_last_commit: 2f770e7
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/maEndToEnd_2.16.0.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: 203
Package: methylationArrayAnalysis
Version: 1.20.0
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: 8e33a4570016f2369909487cb8fd552a
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 <jovana.maksimovic@mcri.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methylationArrayAnalysis
git_branch: RELEASE_3_15
git_last_commit: 44bf638
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/methylationArrayAnalysis_1.20.0.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: 230
Package: recountWorkflow
Version: 1.20.0
Depends: R (>= 3.6.0)
Imports: recount, GenomicRanges, limma, edgeR, DESeq2, regionReport,
clusterProfiler, org.Hs.eg.db, gplots, derfinder, GenomicState,
bumphunter, derfinderPlot
Suggests: BiocStyle, BiocWorkflowTools, knitr, sessioninfo, rmarkdown
License: Artistic-2.0
MD5sum: 141ac2ac0426169f0d8579ea582ac2c6
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 <lcolladotor@gmail.com>
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_15
git_last_commit: 4ebdec4
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/recountWorkflow_1.20.0.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: 249
Package: RNAseq123
Version: 1.20.0
Depends: R (>= 3.3.0), Glimma (>= 1.1.9), limma, edgeR, gplots,
RColorBrewer, Mus.musculus, R.utils, TeachingDemos, statmod,
BiocWorkflowTools
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: f99041089b9f52224fe53d5ffa4968a3
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 <mritchie@wehi.edu.au>
URL: https://f1000research.com/articles/5-1408/v3
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RNAseq123
git_branch: RELEASE_3_15
git_last_commit: b713d9d
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/RNAseq123_1.20.0.tar.gz
vignettes: vignettes/RNAseq123/inst/doc/designmatrices.html,
vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.html,
vignettes/RNAseq123/inst/doc/limmaWorkflow.html
vignetteTitles: A guide to creating design matrices for gene expression
experiments (English version), 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/designmatrices.R,
vignettes/RNAseq123/inst/doc/limmaWorkflow_CHN.R,
vignettes/RNAseq123/inst/doc/limmaWorkflow.R
dependencyCount: 168
Package: rnaseqDTU
Version: 1.16.0
Depends: R (>= 3.5.0), DRIMSeq, DEXSeq, stageR, DESeq2, edgeR, rafalib,
devtools
Suggests: knitr, rmarkdown
License: Artistic-2.0
MD5sum: 5aad8b714b812ae427818c53327745f4
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 <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/rnaseqDTU/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rnaseqDTU
git_branch: RELEASE_3_15
git_last_commit: 7033753
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/rnaseqDTU_1.16.0.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: 183
Package: rnaseqGene
Version: 1.20.0
Depends: R (>= 3.3.0), BiocStyle, airway (>= 1.5.3), tximeta, magrittr,
DESeq2, apeglm, vsn, dplyr, ggplot2, hexbin, pheatmap,
RColorBrewer, PoiClaClu, glmpca, ggbeeswarm, genefilter,
AnnotationDbi, org.Hs.eg.db, ReportingTools, Gviz, sva, RUVSeq,
fission
Suggests: knitr, rmarkdown
License: Artistic-2.0
MD5sum: d001fb1ab8690afd4406ec5f47a25225
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 <michaelisaiahlove@gmail.com>
URL: https://github.com/mikelove/rnaseqGene/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rnaseqGene
git_branch: RELEASE_3_15
git_last_commit: 5d6595d
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/rnaseqGene_1.20.0.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: 228
Package: RnaSeqGeneEdgeRQL
Version: 1.20.0
Depends: R (>= 3.3.0), edgeR, gplots, org.Mm.eg.db, GO.db, BiocStyle
Suggests: knitr, knitcitations, rmarkdown
License: Artistic-2.0
MD5sum: c571ff2c45948a82d510629b39ad9e69
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 <yuchen@wehi.edu.au>
URL: http://f1000research.com/articles/5-1438
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RnaSeqGeneEdgeRQL
git_branch: RELEASE_3_15
git_last_commit: c37af7c
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/RnaSeqGeneEdgeRQL_1.20.0.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: 77
Package: sequencing
Version: 1.20.0
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: cbe609c641d55034f0ef97d394bf11c1
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
<maintainer@bioconductor.org>
URL: https://www.bioconductor.org/help/workflows/sequencing/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sequencing
git_branch: RELEASE_3_15
git_last_commit: 2e1eab4
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/sequencing_1.20.0.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: 132
Package: simpleSingleCell
Version: 1.20.0
Imports: utils, methods, knitr, callr, rmarkdown, CodeDepends,
BiocStyle
Suggests: readxl, R.utils, SingleCellExperiment, scater, scran, limma,
BiocFileCache, org.Mm.eg.db
License: Artistic-2.0
MD5sum: fb3a1fb4197dfd0bfb190c6d9b5bb2ea
NeedsCompilation: no
Title: A step-by-step workflow for low-level analysis of single-cell
RNA-seq data with Bioconductor
Description: Once a proud workflow package, this is now a shell of its
former self. Almost all of its content has been cannibalized
for use in the "Orchestrating Single-Cell Analyses with
Bioconductor" book at https://osca.bioconductor.org. Most
vignettes here are retained as reminders of the glory that once
was, also providing redirection for existing external links to
the relevant OSCA book chapters.
biocViews: ImmunoOncologyWorkflow, Workflow, SingleCellWorkflow
Author: Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://www.bioconductor.org/help/workflows/simpleSingleCell/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/simpleSingleCell
git_branch: RELEASE_3_15
git_last_commit: 5235663
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/simpleSingleCell_1.20.0.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/multibatch.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, 12. Scalability for big
data, 10. Detecting differential expression, 08. Detecting
doublets, 01. Introduction, 13. Further analysis strategies,
11. Advanced batch correction, 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/misc.R
dependencyCount: 43
Package: SingscoreAMLMutations
Version: 1.11.0
Depends: R (>= 4.1.0)
Imports: dcanr, edgeR, ggplot2, gridExtra, GSEABase, mclust,
org.Hs.eg.db, plyr, reshape2, rtracklayer, singscore,
SummarizedExperiment, TCGAbiolinks, BiocFileCache
Suggests: knitr, rmarkdown, BiocStyle, BiocWorkflowTools, spelling
License: Artistic-2.0
MD5sum: 51795cc4b45b07a3653ac1adca4769be
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]
(<https://orcid.org/0000-0002-6398-9157>), Momeneh Foroutan
[aut] (<https://orcid.org/0000-0002-1440-0457>), Yi Xie [aut]
(<https://orcid.org/0000-0002-1938-4089>), Ruqian Lyu [aut],
Malvika Kharbanda [aut]
(<https://orcid.org/0000-0001-9726-3023>), Joseph Cursons [aut]
(<https://orcid.org/0000-0002-5053-4540>), Melissa J. Davis
[aut] (<https://orcid.org/0000-0003-4864-7033>)
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
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: master
git_last_commit: 1eefcf0
git_last_commit_date: 2021-10-26
Date/Publication: 2021-10-29
source.ver: src/contrib/SingscoreAMLMutations_1.11.0.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: 161
Package: TCGAWorkflow
Version: 1.19.1
Depends: R (>= 3.4.0)
Imports: AnnotationHub, knitr, ELMER, biomaRt,
BSgenome.Hsapiens.UCSC.hg19, circlize, c3net, ChIPseeker,
rmarkdown, ComplexHeatmap, ggpubr, clusterProfiler, downloader
(>= 0.4), GenomicRanges, GenomeInfoDb, ggplot2, ggthemes,
graphics, minet, motifStack, pathview, pbapply, parallel,
rGADEM, pander, maftools, RTCGAToolbox, SummarizedExperiment,
TCGAbiolinks, TCGAWorkflowData (>= 1.9.0), DT, gt
License: Artistic-2.0
MD5sum: c8341c9497d5d2d45584092d306d04b1
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 <tiagochst@gmail.com>, Antonio Colaprico
<antonio.colaprico@ulb.ac.be>, Catharina Olsen
<colsen@ulb.ac.be>, Fulvio D Angelo <fulvio.dan13@gmail.com>,
Gianluca Bontempi <gbonte@ulb.ac.be>, Michele Ceccarelli
<m.ceccarelli@gmail.com>, Houtan Noushmehr <houtan@usp.br>
Maintainer: Tiago Chedraoui Silva <tiagochst@gmail.com>
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: master
git_last_commit: c012560
git_last_commit_date: 2022-01-05
Date/Publication: 2022-01-07
source.ver: src/contrib/TCGAWorkflow_1.19.1.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: 316
Package: variants
Version: 1.20.0
Depends: R (>= 3.3.0), VariantAnnotation, 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: 322d293cd3d594878d8a500a2a494ff5
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: Valerie Obenchain [aut], Martin Morgan [ctb], Bioconductor
Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/help/workflows/variants/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/variants
git_branch: RELEASE_3_15
git_last_commit: 1164b80
git_last_commit_date: 2022-04-26
Date/Publication: 2022-04-29
source.ver: src/contrib/variants_1.20.0.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: 103
Package: proteomics
Version: 1.20.0
Depends: R (>= 3.3.0), mzR, mzID, MSnID, MSnbase, rpx, MLInterfaces,
pRoloc, pRolocdata, rols, hpar
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
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 <laurent.gatto@uclouvain.be>
URL: https://www.bioconductor.org/help/workflows/proteomics/
VignetteBuilder: knitr
PackageStatus: Deprecated