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.