Accurate recognition of cis-regulatory motifs with the correct lengths in prokaryotic genomes.

We present a new computational method for solving a classical problem, the identification problem of cis-regulatory motifs in a given set of promoter sequences, based on one key new idea. Instead of scoring candidate motifs individually like in all the existing motif-finding programs, our method scores groups of candidate motifs with similar sequences, called motif closures, using a P-value, which has substantially improved the prediction reliability over the existing methods. Our new P-value scoring scheme is sequence length independent, hence allowing direct comparisons among predicted motifs with different lengths on the same footing. We have implemented this method as a Motif Recognition Computer (MREC) program, and have extensively tested MREC on both simulated and biological data from prokaryotic genomes. Our test results indicate that MREC can accurately pick out the actual motif with the correct length as the best scoring candidate for the vast majority of the cases in our test set. We compared our prediction results with two motif-finding programs Cosmo and MEME, and found that MREC outperforms both programs across all the test cases by a large margin. The MREC program is available at http://csbl.bmb.uga.edu/~bingqiang/MREC1/.