normR: Regime enrichment calling for ChIP-seq data

Johannes Helmuth; Na Li; Laura Arrigoni; Kathrin Gianmoena; Cristina Cadenas; Gilles Gasparoni; Anupam N. Sinha; Philip C Rosenstiel; Jörn Walter; Jan Georg Hengstler; Thomas Manke; Ho‐Ryun Chung

doi:10.1101/082263

BioinformaticsbioRxivOct 25, 2016

Preprint

normR: Regime enrichment calling for ChIP-seq data

Johannes Helmuth iD, Na Li iD, Laura Arrigoni iD, Kathrin Gianmoena iD, Cristina Cadenas iD, Gilles Gasparoni iD, Anupam N. Sinha iD, Philip C Rosenstiel iD, Jörn Walter iD, Jan Georg Hengstler iD, Thomas Manke iD, Ho‐Ryun Chung iD

Abstract

ChIP-seq probes genome-wide localization of DNA-associated proteins. To mitigate technical biases ChIP-seq read densities are normalized to read densities obtained by a control. Our statistical framework \"normR\" achieves a sensitive normalization by accounting for the effect of putative protein-bound regions on the overall read statistics. Here, we demonstrate normRs suitability in three studies: (i) calling enrichment for high (H3K4me3) and low (H3K36me3) signal-to-ratio data; (ii) identifying two previously undescribed H3K27me3 and H3K9me3 heterochromatic regimes of broad and peak enrichment; and (iii) calling differential H3K4me3 or H3K27me3-enrichment between HepG2 hepatocarcinoma cells and primary human Hepatocytes. normR is readily available on http://bioconductor.org/packages/normr

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