title: FingerPRINTScan: intelligent searching of the PRINTS motif database.
abstract: BACKGROUND
  By identifying an unknown gene or protein as a member of a known family, we can infer a wealth of previously compiled information pertinent to that family and its members.
  
  
  RESULTS
  This paper introduces a method that classifies sequences using familial definitions from the PRINTS database, allowing progress to be made with the identification of distant evolutionary relationships. The approach makes use of the contextual information inherent in a multiple-motif method, and has the power to identify hitherto unidentified relationships in mass genome data. We exemplify our method by a comparison of database searches with uncharacterized sequences from the Caenorhabditis elegans and Saccharomyces cerevisiae genome projects. This analysis tool combines a simple, user-friendly interface with the capacity to provide an 'intelligent', biologically relevant result.
authors.affiliation: School of Biological Sciences, University of Manchester, UK. scordis@bioinf.man.ac.uk
authors.forename: P
authors.initials: P
authors.lastname: Scordis
authors.forename: D R
authors.initials: DR
authors.lastname: Flower
authors.forename: T K
authors.initials: TK
authors.lastname: Attwood
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics; D019295:Computational Biology; D016208:Databases, Factual; D016678:Genome; D016681:Genome, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software