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Bioinformatics Advance Access originally published online on November 5, 2004
Bioinformatics 2005 21(7):897-901; doi:10.1093/bioinformatics/bti132
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Accurate identification of alternatively spliced exons using support vector machine

Gideon Dror 1,*, Rotem Sorek 2,3 and Ron Shamir 4

1The Academic College of Tel-Aviv-Yaffo Tel Aviv 4044, Israel
2Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University Tel Aviv 69978, Israel
3Compugen Tel Aviv 69512, Israel
4School of Computer Science, Tel Aviv University Tel Aviv 69073, Israel

*To whom correspondence should be addressed.

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.

Availability: Upon request from the authors.

Contact: gideon{at}mta.ac.il


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