chipseqDB
A Bioconductor Workflow to Detect Differential Binding in ChIP-seq Data
Bioconductor version: 3.23 · Package version: 1.36.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("chipseqDB")Details
| Maintainer | Aaron Lun <infinite.monkeys.with.keyboards@gmail.com> |
| Author | Aaron Lun [aut, cre], Gordon Smyth [aut] |
| License | Artistic-2.0 |
| URL | https://www.bioconductor.org/help/workflows/chipseqDB/ |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | EpigeneticsWorkflow, ImmunoOncologyWorkflow, Workflow |
| Package Short Url | https://bioconductor.org/packages/chipseqDB/ |
Citation
From within R, enter citation("chipseqDB"):
Aaron Lun, Gordon Smyth. chipseqDB: A Bioconductor Workflow to Detect Differential Binding in ChIP-seq Data. doi:10.18129/B9.bioc.chipseqDB, R package version 1.36.0, https://bioconductor.org/packages/chipseqDB.
Generated from the package metadata; it may differ from the package's own citation.
Download
Follow the installation instructions to use this package in your R session.
| Source package | chipseqDB_1.36.0.tar.gz |
Dependencies
Suggests: chipseqDBData, BiocStyle, BiocFileCache, ChIPpeakAnno, Gviz, Rsamtools, TxDb.Mmusculus.UCSC.mm10.knownGene, csaw, edgeR, knitr, org.Mm.eg.db, rtracklayer, rmarkdown