BRGenomics

This package is for version 3.19 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see BRGenomics.

Tools for the Efficient Analysis of High-Resolution Genomics Data


Bioconductor version: Release (3.19)

This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features.

Author: Mike DeBerardine [aut, cre]

Maintainer: Mike DeBerardine <mike.deberardine at gmail.com>

Citation (from within R, enter citation("BRGenomics")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("BRGenomics")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

Reference Manual PDF

Details

biocViews ATACSeq, ChIPSeq, Coverage, DataImport, GeneExpression, GeneRegulation, Normalization, RNASeq, Sequencing, Software, Transcription
Version 1.16.1
In Bioconductor since BioC 3.11 (R-4.0) (4.5 years)
License Artistic-2.0
Depends R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors
Imports GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods
System Requirements
URL https://mdeber.github.io
Bug Reports https://github.com/mdeber/BRGenomics/issues
See More
Suggests BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary (x86_64)
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/BRGenomics
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/BRGenomics
Package Short Url https://bioconductor.org/packages/BRGenomics/
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