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Bioinformatics 2005 21(Suppl 1):i107-i115; doi:10.1093/bioinformatics/bti1010
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

ANOSVA: a statistical method for detecting splice variation from expression data

Melissa S. Cline 1,2,*, John Blume 1, Simon Cawley 1, Tyson A. Clark 1, Jing-Shan Hu 3, Gang Lu 4, Nathan Salomonis 5, Hui Wang 1 and Alan Williams 1

1Affymetrix, Inc. 3380 Central Expressway, Santa Clara, CA 95051, USA
2Pasteur Institute 25–28 Rue du Docteur Roux, 75724 Paris, France
3Roche, Inc. 3431 Hillview Avenue, Palo Alto, CA 94304, USA
4University of Rochester Medical Center 601 Elmwood Avenue, Rochester, NY 14612, USA
5Gladstone Institute of Cardiovascular Disease, University of California 1650 Owens Street, San Francisco, CA 94158, USA

*To whom correspondence should be addressed.

Motivation: Many or most mammalian genes undergo alternative splicing, generating a variety of transcripts from a single gene. New information on splice variation is becoming available through technology for measuring expression levels of several exons or splice junctions per gene. We have developed a statistical method, ANalysis Of Splice VAriation (ANOSVA) to detect alternative splicing from expression data. Since ANOSVA requires no transcript information, it can be applied when the level of annotation is poor. When validated against spiked clone data, it generated no false positives and few false negatives. We demonstrated ANOSVA with data from a prototype mouse alternative splicing array, run against normal adult tissues, yielding a set of genes with evidence of tissue-specific splice variation.

Availability: The results are available at the supplementary information site.

Contact: cline{at}pasteur.fr

Supplementary information: The results are available at the supplementary information site https://bioinfo.affymetrix.com/Papers/ANOSVA/

Received on January 15, 2005; accepted on March 27, 2005

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