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Normalization and difference calling in ChIP-seq data

Bioconductor version: Release (3.19)

Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.

Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut]

Maintainer: Johannes Helmuth <johannes.helmuth at>

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


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Introduction to the normR package HTML R Script
Reference Manual PDF


biocViews Alignment, Bayesian, ChIPSeq, Classification, DataImport, DifferentialPeakCalling, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, RIPSeq, Software
Version 1.30.0
In Bioconductor since BioC 3.4 (R-3.3) (7.5 years)
License GPL-2
Depends R (>= 3.3.0)
Imports methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue(>= 2.2), bamsignals(>= 1.4), rtracklayer(>= 1.32)
System Requirements C++11
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Suggests BiocStyle, testthat (>= 1.0), knitr, rmarkdown
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Enhances BiocParallel
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Source Package normr_1.30.0.tar.gz
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