Identifying cis-regulatory modules by combining comparative and compositional analysis of DNA

Motivation: Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction. Results: We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some 'false positive' predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs.