Accurate identification of alternatively spliced exons using support vector machine

Gideon Dror, Rotem Sorek, Ron Shamir

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: Alternative splicing is a major component of the regulatory action on mammalian transcriptomes. It is estimated that over half of all human genes have more than one splice variant. Previous studies have shown that alternatively spliced exons possess several features that distinguish them from constitutively spliced ones. Recently, we have demonstrated that such features can be used to distinguish alternative from constitutive exons. In the current study, we used advanced machine learning methods to generate robust classifier of alternative exons. Results: We extracted several hundred local sequence features of constitutive as well as alternative exons. Using feature selection methods we find seven attributes that are dominant for the task of classification. Several less informative features help to slightly increase the performance of the classifier. The classifier achieves a true positive rate of 50% for a false positive rate of 0.5%. This result enables one to reliably identify alternatively spliced exons in exon databases that are believed to be dominated by constitutive exons.

Original languageEnglish
Pages (from-to)897-901
Number of pages5
JournalBioinformatics
Volume21
Issue number7
DOIs
StatePublished - 1 Apr 2005

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