Abstract
ChIP-Seq is widely used to characterize genome-wide binding patterns of transcription factors and other chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene regulation, few quantitative approaches have been developed. Here, we present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets describing transcription factor binding sites and epigenetic modifications. The quantitative binding differences inferred by MAnorm showed strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators.
Highlights
Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-Seq) has become the preferred method to determine genome-wide binding patterns of transcription factors and other chromatinassociated proteins [1]
To circumvent the issue of differences in signalto-background noise (S/N) ratio between samples, we focused on ChIP-enriched regions, and introduced a novel idea, that ChIPSeq common peaks could serve as a reference to build the rescaling model for normalization
We borrow the idea of the MA plot and propose a novel method for quantitative comparison of ChIP-Seq data sets based on two empirical assumptions
Summary
Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-Seq) has become the preferred method to determine genome-wide binding patterns of transcription factors and other chromatinassociated proteins [1]. With the rapid accumulation of ChIP-Seq data, comparison of multiple ChIP-Seq data sets is increasingly becoming critical for addressing important biological questions. Comparison of biological replicates is commonly used to find robust binding sites, and the identification of sites that are differentially bound by chromatin-associated proteins in different cellular contexts is important for elucidating underlying mechanisms of cell type-specific regulation. Several methods have been proposed for finding ChIPenriched regions in a ChIP-Seq sample compared to a suitable negative control (for example, mock or nonspecific immunoprecipitation). These involve fitting a model derived from negative control and/or sample low
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