A specialized learner for inferring structured cis-regulatory modules

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC.