title: Enhanced protein domain discovery using taxonomy.
abstract: BACKGROUND
  It is well known that different species have different protein domain repertoires, and indeed that some protein domains are kingdom specific. This information has not yet been incorporated into statistical methods for finding domains in sequences of amino acids.
  
  
  RESULTS
  We show that by incorporating our understanding of the taxonomic distribution of specific protein domains, we can enhance domain recognition in protein sequences. We identify 4447 new instances of Pfam domains in the SP-TREMBL database using this technique, equivalent to the coverage increase given by the last 8.3% of Pfam families and to a 0.7% increase in the number of domain predictions. We use PSI-BLAST to cross-validate our new predictions. We also benchmark our approach using a SCOP test set of proteins of known structure, and demonstrate improvements relative to standard Hidden Markov model techniques.
  
  
  CONCLUSIONS
  Explicitly including knowledge about the taxonomic distribution of protein domains can enhance protein domain recognition. Our method can also incorporate other context-specific domain distributions - such as domain co-occurrence and protein localisation.
authors.affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. lc1@sanger.ac.uk
authors.forename: Lachlan
authors.initials: L
authors.lastname: Coin
authors.forename: Alex
authors.initials: A
authors.lastname: Bateman
authors.forename: Richard
authors.initials: R
authors.lastname: Durbin
mesh_terms: D019295:Computational Biology / Q000706:statistics & numerical data; D030562:Databases, Protein; D015233:Models, Statistical; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D013045:Species Specificity
chemical_list: D011506:Proteins