Sequence information for the splicing of human pre-mRNA identified by support vector machine classification

Abstract

Vertebrate pre-mRNA transcripts contain many sequences that resemble splice sites on the basis of agreement to the consensus, yet these more numerous false splice sites are usually completely ignored by the cellular splicing machinery. Even at the level of exon definition, pseudo exons defined by such false splices sites outnumber real exons by an order of magnitude. We used a support vector machine to discover sequence information that could be used to distinguish real exons from pseudo exons. This machine learning tool led to the definition of potential branch points, an extended polypyrimidine tract, and C-rich and TG-rich motifs in a region limited to 50 nt upstream of constitutively spliced exons. C-rich sequences were also found in a region extending to 80 nt downstream of exons, along with G-triplet motifs. In addition, it was shown that combinations of three bases within the splice donor consensus sequence were more effective than consensus values in distinguishing real from pseudo splice sites; two-way base combinations were optimal for distinguishing 3′ splice sites. These data also suggest that interactions between two or more of these elements may contribute to exon recognition, and provide candidate sequences for assessment as intronic splicing enhancers.