Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data.

ABSTRACT: The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics' public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different lab

SUBMITTER: Teng M 

PROVIDER: S-EPMC5668949 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature