title: Features generated for computational splice-site prediction correspond to functional elements.
abstract: BACKGROUND
  Accurate selection of splice sites during the splicing of precursors to messenger RNA requires both relatively well-characterized signals at the splice sites and auxiliary signals in the adjacent exons and introns. We previously described a feature generation algorithm (FGA) that is capable of achieving high classification accuracy on human 3' splice sites. In this paper, we extend the splice-site prediction to 5' splice sites and explore the generated features for biologically meaningful splicing signals.
  
  
  RESULTS
  We present examples from the observed features that correspond to known signals, both core signals (including the branch site and pyrimidine tract) and auxiliary signals (including GGG triplets and exon splicing enhancers). We present evidence that features identified by FGA include splicing signals not found by other methods.
  
  
  CONCLUSIONS
  Our generated features capture known biological signals in the expected sequence interval flanking splice sites. The method can be easily applied to other species and to similar classification problems, such as tissue-specific regulatory elements, polyadenylation sites, promoters, etc.
authors.affiliation: Computer Science Department, University of Maryland, College Park, MD 20742, USA. rezarta@cs.umd.edu
authors.forename: Rezarta Islamaj
authors.initials: RI
authors.lastname: Dogan
authors.forename: Lise
authors.initials: L
authors.lastname: Getoor
authors.forename: W John
authors.initials: WJ
authors.lastname: Wilbur
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Mount
mesh_terms: D019295:Computational Biology / Q000379:methods* / Q000639:trends; D006801:Humans; D011237:Predictive Value of Tests; D022821:RNA Splice Sites / Q000502:physiology*; D012333:RNA, Messenger / Q000502:physiology
chemical_list: D022821:RNA Splice Sites; D012333:RNA, Messenger