MMDiff: Statistical Testing for ChIP-Seq data sets

ChIP-Seq has rapidly become the dominant experimental technique to determine the location of transcription factor binding sites and histone modifications. Typically, computational peak finders, such as Macs [Zhang et al., 2008], are used to identify potential candidate regions, i.e. regions with significantly enriched read coverage compared to some background. In the following, we will simply call these regions peaks and will assume that their genomic coordinates are provided. Going beyond this basic analysis, it is often of interest to detect a subset of peaks where significant changes of read coverage occur in a treatment experiment relative to a control (see Figure 1). Statistical analysis of ChIP-Seq data however remains challenging, due to the highly structured nature of the data and the paucity of replicates. Current approaches to detect differentially bound regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peak profile. Higher order features of ChIP-Seq peak enrichment profiles carry important and often complementary information to total counts, and hence are potentially important in assessing differential binding. We therefore incorporate higher order information into testing for differential binding by adapting recently proposed kernel-based statistical tests to ChIP-Seq data.