Unified Analysis of Multiple ChIP-Seq Datasets

High-throughput sequencing technologies are increasingly used in molecular cell biology to assess genome-wide chromatin dynamics of proteins bound to DNA, through techniques such as chromatin immunoprecipitation sequencing (ChIP-seq). These techniques often rely on an analysis strategy based on identifying genomic regions with increased sequencing signal to infer the binding location or chemical modifications of proteins bound to DNA. Peak calling within individual samples has been well described, however relatively little attention has been devoted to the merging of replicate samples, and the cross-comparison of many samples. Here, we present a generalized strategy to enable the unification of ChIP-seq datasets, enabling enhanced cross-comparison of binding patterns. The strategy works by merging peak data between different (even unrelated) samples, and then using a local background to recalculate enrichment. This strategy redefines the peaks within each experiment, allowing for more accurate cross-comparison of datasets.