title: Assessing phylogenetic motif models for predicting transcription factor binding sites.
abstract: BACKGROUND
  A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning.
  
  
  RESULTS
  We evaluate three PMM-based prediction algorithms, each of which uses a different treatment of gapped alignments, and we compare their prediction accuracy with that of a non-phylogenetic motif scanning approach. Surprisingly, all of these algorithms appear to be inferior to simple motif scanning, when accuracy is measured using a gold standard of validated yeast TFBSs. However, the PMM scanners perform much better than simple motif scanning when we abandon the gold standard and consider the number of statistically significant sites predicted, using column-shuffled 'random' motifs to measure significance. These results suggest that the common practice of measuring the accuracy of binding site predictors using collections of known sites may be dangerously misleading since such collections may be missing 'weak' sites, which are exactly the type of sites needed to discriminate among predictors. We then extend our previous theoretical model of the statistical power of PMM-based prediction algorithms to allow for loss of binding sites during evolution, and show that it gives a more accurate upper bound on scanner accuracy. Finally, utilizing our theoretical model, we introduce a new method for predicting the number of real binding sites in a genome. The results suggest that the number of true sites for a yeast TF is in general several times greater than the number of known sites listed in the Saccharomyces cerevisiae Database (SCPD). Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs.
authors.affiliation: Institute for Molecular Bioscience, University of Queensland, Qld, Australia. j.hawkins@imb.uq.edu.au
authors.forename: John
authors.initials: J
authors.lastname: Hawkins
authors.forename: Charles
authors.initials: C
authors.lastname: Grant
authors.forename: William Stafford
authors.initials: WS
authors.lastname: Noble
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Bailey
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D016678:Genome; D010802:Phylogeny; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors