Improving MEME via a two-tiered significance analysis

Motivation : With over 9000 unique users recorded in the first half of 2013, MEME is one of the most popular motif-finding tools available. Reliable estimates of the statistical significance of motifs can greatly increase the usefulness of any motif finder. By analogy, it is difficult to imagine evaluating a BLAST result without its accompanying E -value. Currently MEME evaluates its EM-generated candidate motifs using an extension of BLAST’s E -value to the motif-finding context. Although we previously indicated the drawbacks of MEME’s current significance evaluation, we did not offer a practical substitute suited for its needs, especially because MEME also relies on the E -value internally to rank competing candidate motifs. Results : Here we offer a two-tiered significance analysis that can replace the E -value in selecting the best candidate motif and in evaluating its overall statistical significance. We show that our new approach could substantially improve MEME’s motif-finding performance and would also provide the user with a reliable significance analysis. In addition, for large input sets, our new approach is in fact faster than the currently implemented E -value analysis. Contact : uri.keich@sydney.edu.au or emi.tanaka@sydney.edu.au Supplementary information : Supplementary data are available at Bioinformatics online.