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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>

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


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

if (!require("BiocManager", quietly = TRUE))


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


Reference Manual PDF


biocViews ATACSeq, ChIPSeq, Coverage, DataImport, GeneExpression, GeneRegulation, Normalization, RNASeq, Sequencing, Software, Transcription
Version 1.16.0
In Bioconductor since BioC 3.11 (R-4.0) (4 years)
License Artistic-2.0
Depends R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors
Imports GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods
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Suggests BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings
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