Features generated for computational splice-site prediction correspond to functional elements

Rezarta Islamaj Dogan¹^,2 , Lise Getoor¹ , W John Wilbur² and Stephen M Mount³^,4

¹Computer Science Department, University of Maryland, College Park, MD 20742, USA

²National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA

³Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA

⁴Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:410doi:10.1186/1471-2105-8-410


Published:	24 October 2007

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.

Conclusion

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.