Features generated for computational splice-site prediction correspond to functional elements.
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.
Dogan
Rezarta Islamaj
RI
Computer Science Department, University of Maryland, College Park, MD 20742, USA. rezarta@cs.umd.edu
Getoor
Lise
L
Wilbur
W John
WJ
Mount
Stephen M
SM
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
D022821:RNA Splice Sites; D012333:RNA, Messenger