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Bioinformatics Advance Access published online on November 5, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti132
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received September 7, 2004
Revised November 1, 2004
Accepted November 1, 2004

Article

Accurate identification of alternatively spliced exons using support vector machine

Gideon Dror 1*, Rotem Sorek 2, and Ron Shamir 3

1 The Academic College of Tel-Aviv-Yaffo, Tel-Aviv, 4044, Israel
2 Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; Compugen, Tel Aviv 69512, Israel
3 School of Computer Science, Tel Aviv University, Tel-Aviv 69073, Israel

* To whom correspondence should be addressed.
Gideon Dror, E-mail: gideon{at}mta.ac.il


  Abstract

Motivation: Alternative splicing is a major component of the regulation acting 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 use advanced machine learning methods to generate robust alternative exons classifier.

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.

Availability: Upon request from the authors.


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