BRGenomics is designed to help users avoid code repetition by providing efficient and tested functions to accomplish common, discrete tasks in the analysis of high-throughput sequencing data. The included functions are geared toward analyzing basepair-resolution sequencing data, the properties of which are exploited to increase performance and user-friendliness. We leverage standard Bioconductor methods and classes to maximize compatibility with its rich ecoystem of bioinformatics tools, and we aim to make BRGenomics sufficient for most post-alignment data processing. Common data processing and analytical steps are turned into fast-running one-liners that can be simultaneously applied across numerous datasets. BRGenomics is fully-documented, and we aim it to be beginner-friendly.
BRGenomics 1.10.0
This package is designed to:
bedtools
and deeptools
hitslib
or the kent source utilities from the UCSC genome browserbigWig
R packageDESeq2
to calculate differential
expression in a manner that is robust to global changes1
DESeq2
analysis, e.g. to exclude of specific
sites/peaks from the analysis (not usually supported by DESeq2)GRanges
” object), which is already supported by a
rich, user-friendly suite of tools that greatly simplify working with datasets
and annotationsData processing:
Signal counting and analysis:
Avoid the default behavior of calculating genewise dispersion across all samples present, which is invalid if any experimental condition causes broad changes↩︎