title: Improving MEME via a two-tiered significance analysis.
abstract: BACKGROUND
  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.
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, AustraliaSchool of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia.
authors.forename: Emi
authors.initials: E
authors.lastname: Tanaka
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Bailey
authors.affiliation: School of Mathematics and Statistics, University of Sydney, Sydney 2006, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong 2522, New South Wales and Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia.
authors.forename: Uri
authors.initials: U
authors.lastname: Keich
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D059372:Nucleotide Motifs; D017422:Sequence Analysis, DNA; D012984:Software