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

Rezarta Islamaj Dogan,Lise Getoor,W John Wilbur,Stephen M Mount

doi:10.1186/1471-2105-8-410

Abstract

BackgroundAccurate 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.ResultsWe 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.ConclusionOur 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.

Highlights

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
Sequences and splice-site neighborhood For these experiments we considered canonical splice sites
Description of generated feature sets Here we summarize the specific steps used to generate the composite feature sets used in our analysis

Summary

Introduction

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. A complete and accurate description of the gene structure on the basis of sequence alone remains a difficult problem [1]. Sequences known as introns are removed from precursors to mRNA, in the complex process of splicing. The boundaries between introns and exons are called splice sites and the identification of these positions poses a particular challenge. The adjacent nucleotides on intron boundaries comprise two different consensus sequences for the 5' (donor) site and 3' (acceptor) site. Similar sequences within introns and (page number not for citation purposes)

Methods

Results

Conclusion