GAMI-CRM: Using de novo motif inference to detect cis-regulatory modules

In this work, we extend GAMI (Genetic Algorithms for Motif Inference), a de novo motif inference system, to find sets of motifs that may function as part of a cis-regulatory module (CRM) using a comparative genomics approach. Evidence suggests that most transcription factors binding sites are part of a CRM, so our new approach is expected to yield stronger candidates for de novo inference of candidate regulatory elements and their combinatorial regulation of genes. Thanks to our genetic algorithms based approach, we are able to search relatively large input sequences (100,000nt or longer). Most current computational approaches to identifying candidate CRMs depend on foreknowledge of the processes that the genes they regulate are involved in. In comparison with one leading method, Cluster-Buster, our prototype de novo approach, which we call GAMI-CRM, performed well, suggesting that GAMI-CRM will be particularly useful in predicting CRMs for genes whose interactions are poorly understood.