Department of Statistics, University of California, Los Angeles, CA 90095, USA
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Motivation:With the accumulation of genome-wide binding data for many transcription factors (TFs) in the same cell type or cellular condition, it is of great current interest to systematically infer the complex regulatory logic among multiple TFs. In particular, ChIP-Seq data have been generated for 14 core TFs critical to the maintenance and reprogramming of mouse embryonic stem cells (ESCs). This provides a great opportunity to study the regulatory collaboration and interaction among these TFs and with other unknown co-regulators.Results:In combination with liquid association among gene expression profiles, we develop a computational method to predict context-dependent (CD) co-egulators of these core TFs in ESCs from pairwise binding datasets. That is, co-occupancy between a core TF and a predicted co-regulator depends on the presence or absence of binding sites of another core TF, which is regarded as a binding context. Unbiased external validation confirms that the predicted CD binding of a co-regulator is reliable. Our results reveal a detailed CD co-regulation network among the 14 core TFs and provide many other potential co-regulators showing strong agreement with the literature.Availability:See www.stat.ucla.edu/˜zhou/CMF for software and source code.Contact:firstname.lastname@example.orgSupplementary information:Supplementary data are available at Bioinformatics online.