Shape matters: differential peak detection for Chip-seq data sets

Gabriele Schweikert, Guido Sanguinetti


Statistical analysis of ChIP-Seq data remains challenging, due to the highly structured nature of the data and the paucity of replicates. Current approaches to detect differentially bound or modified regions are mainly borrowedfrom RNA-Seq data analysis, thus focusing on total counts of reads mapped to a region, ignoring any information encoded in the peak shape. We suggest that higher order features play an important role in detecting differentialhistone modifications and present MMDiff, a Kernel-based testing methodology. Our analysis shows that the method detects functional changes in histone modifications, which are complementary to changes detected bycount-based methods.


Chip-Seq; differential peak detection; Kernel methods; machine learning; histone modifications; H3K4me3

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