Brundle: Normalisation Tools for Inter-Condition Variability of ChIP-Seq Data

Inter-sample condition variability is a key challenge of normalising ChIP-seq data. This implementation uses either spike-in or a second factor as a control for normalisation. Input can either be from 'DiffBind' or a matrix formatted for 'DESeq2'. The output is either a 'DiffBind' object or the default 'DESeq2' output. Either can then be processed as normal. Supporting manuscript Guertin, Markowetz and Holding (2017) <doi:10.1101/182261>.

Version: 1.0.9
Depends: R (≥ 2.10), DiffBind, Rsamtools, DESeq2, lattice, stats, utils, graphics
Published: 2019-04-23
Author: Andrew N Holding
Maintainer: Andrew N Holding <andrew.holding at>
License: CC BY 4.0
NeedsCompilation: no
Materials: README
CRAN checks: Brundle results


Reference manual: Brundle.pdf


Package source: Brundle_1.0.9.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): Brundle_1.0.9.tgz, r-oldrel (arm64): not available, r-release (x86_64): Brundle_1.0.9.tgz, r-oldrel (x86_64): Brundle_1.0.9.tgz
Old sources: Brundle archive


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