Statistical identification of co-regulatory gene modules using multiple ChIP-Seq experiments

ChIP-Seq experiments provide accurate measurements of the regulatory roles of transcription factors (TFs) under specific condition. Downstream target genes can be detected by analyzing the enriched TF binding sites (TFBSs) in genes' promoter regions. The location and statistical information of TFBSs make it possible to evaluate the relative importance of each binding. Based on the assumption that the TFBSs of one ChIP-Seq experiment follow the same specific location distribution, a statistical model is first proposed using both location and significance information of peaks to weigh target genes. With genes' binding scores from different TFs, we merge them into a weighted binding matrix. A Markov Chain Monte Carlo (MCMC) based approach is then applied to the binding matrix for co-regulatory module identification. We demonstrate the efficiency of our statistical model on an ER-α ChIP-Seq dataset and further identify coregulatory modules by using eleven breast cancer related TFs from ENCODE ChIP-Seq datasets. The results show that the TFs in individual module regulate common high score target genes; the association of TFs is biologically meaningful, and the functional roles of TFs and target genes are consistent.