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In multi-cellular organisms, spatiotemporal activity of  cis -regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with  cis -regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS ( C o- O ccurrence  P attern  S earch), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.

COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets