title: Pre-mRNA secondary structure prediction aids splice site prediction.
abstract: Accurate splice site prediction is a critical component of any computational approach to gene prediction in higher organisms. Existing approaches generally use sequence-based models that capture local dependencies among nucleotides in a small window around the splice site. We present evidence that computationally predicted secondary structure of moderate length pre-mRNA subsequencies contains information that can be exploited to improve acceptor splice site prediction beyond that possible with conventional sequence-based approaches. Both decision tree and support vector machine classifiers, using folding energy and structure metrics characterizing helix formation near the splice site, achieve a 5-10% reduction in error rate with a human data set. Based on our data, we hypothesize that acceptors preferentially exhibit short helices at the splice site.
authors.affiliation: University of Washington, Box 352350, Seattle, WA 98195, USA.
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Patterson
authors.forename: Ken
authors.initials: K
authors.lastname: Yasuhara
authors.forename: Walter L
authors.initials: WL
authors.lastname: Ruzzo
mesh_terms: D003198:Computer Simulation; D005091:Exons; D016013:Likelihood Functions; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D012322:RNA Precursors / Q000737:chemistry*; D012326:RNA Splicing; D012333:RNA, Messenger / Q000737:chemistry* / Q000235:genetics; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: D012322:RNA Precursors; D012333:RNA, Messenger