ChIP-Seq now is widely used to characterize the genome-wide patterns of epigenetic modifications and transcription factors’ bindings. 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, it’s very challenging to find a proper quantitative approach for this task.

Here first I will present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets. By applying MAnorm to ChIP-Seq data between different cell lines, we find the quantitative differences of H3K27ac, a histone mark of active promoters and enhancers, show strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators. Then, through three case studies, I’ll show this new model is really a power tool for

studying the stage-specific epigenetic regulations in human erythroid cells;

quantifying the association between genotypes and variations of epigenetic marks across different human individuals;

understanding the role of two critical histone demethylases in mouse embryonic stem cells.

Besides, I’ll also present a sequence-based model for predicting PRC2-associated lincRNAs. Finally, as extension of current works, I’ll introduce some statistical tools and a web-based analysis platform, which we are developing right now, for quantitative comparison and integration of ChIP-Seq data with other types of data.