BRGenomics for analyzing high-resolution genomics data in R

Abstract

I present here the R/Bioconductor package BRGenomics, which provides fast and flexible methods for post-alignment processing and analysis of high-resolution genomics data within an interactive R environment. Utilizing GenomicRanges and other core Bioconductor packages, BRGenomics provides various methods for data importation and processing, read counting and aggregation, spike-in and batch normalization, re-sampling methods for robust 'metagene' analyses, and various other functions for cleaning and modifying sequencing and annotation data. Simple yet flexible, the included methods are optimized for handling multiple datasets simultaneously, make extensive use of parallel processing, and support multiple strategies for efficiently storing and quantifying different kinds of data, including whole reads, quantitative single-base data, and run-length encoded coverage information. BRGenomics has been used to analyze ATAC-seq, ChIP-seq/ChIP-exo, PRO-seq/PRO-cap, and RNA-seq data; is built to be unobtrusive and maximally compatible with the Bioconductor ecosystem; is extensively tested; and includes complete documentation, examples, and tutorials.