Improving MEME via a two-tiered significance analysis

Emi Tanaka, Timothy L. Bailey, Uri Keich

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1965-1973
Number of pages9
JournalBioinformatics
Volume30
Issue number14
DOIs
Publication statusPublished - 15 Jul 2014
Externally publishedYes

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