title: PhyME: a probabilistic algorithm for finding motifs in sets of orthologous sequences.
abstract: BACKGROUND
  This paper addresses the problem of discovering transcription factor binding sites in heterogeneous sequence data, which includes regulatory sequences of one or more genes, as well as their orthologs in other species.
  
  
  RESULTS
  We propose an algorithm that integrates two important aspects of a motif's significance - overrepresentation and cross-species conservation - into one probabilistic score. The algorithm allows the input orthologous sequences to be related by any user-specified phylogenetic tree. It is based on the Expectation-Maximization technique, and scales well with the number of species and the length of input sequences. We evaluate the algorithm on synthetic data, and also present results for data sets from yeast, fly, and human.
  
  
  CONCLUSIONS
  The results demonstrate that the new approach improves motif discovery by exploiting multiple species information.
authors.affiliation: Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10021, USA. saurabh@lonnrot.rockefeller.edu
authors.forename: Saurabh
authors.initials: S
authors.lastname: Sinha
authors.forename: Mathieu
authors.initials: M
authors.lastname: Blanchette
authors.forename: Martin
authors.initials: M
authors.lastname: Tompa
mesh_terms: D000465:Algorithms; D000818:Animals; D001482:Base Composition / Q000235:genetics; D004247:DNA / Q000235:genetics*; D004271:DNA, Fungal / Q000235:genetics*; D004330:Drosophila / Q000235:genetics; D004331:Drosophila melanogaster / Q000235:genetics; D004742:Enhancer Elements, Genetic / Q000235:genetics; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D010802:Phylogeny; D012440:Saccharomyces / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software; D013045:Species Specificity
chemical_list: D004271:DNA, Fungal; D004247:DNA