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Bioinformatics Advance Access published online on August 21, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn444
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Seeder: Discriminative Seeding DNA Motif Discovery

François Fauteux 1,2, Mathieu Blanchette 2,3 and Martina V. Strömvik 1,2,*

1Department of Plant Science, McGill University, 21111 Lakeshore Rd, Ste-Anne-de-Bellevue, Quebec, Canada, H9X 3V9;
2McGill Centre for Bioinformatics, McGill University, 3775 University Street, Montreal, Quebec, Canada, H3A 2B4;
3School of Computer Science, McGill University, 3480 University Street, Montreal, Quebec, Canada, H3A 2A7;

*To whom correspondence should be addressed. Dr. Martina V. Strömvik, E-mail: martina.stromvik{at}mcgill.ca


  Abstract

Motivation: The computational identification of transcription factor binding sites is a major challenge in bioinformatics and an important complement to experimental approaches.

Results: We describe a novel, exact discriminative seeding DNA motif discovery algorithm designed for fast and reliable prediction of cis-regulatory elements in eukaryotic promoters. The algorithm is tested on biological benchmark data and shown to perform equally or better than other motif discovery tools. The algorithm is applied to the analysis of plant tissue-specific promoter sequences and successfully identifies key regulatory elements.

Availability: The Seeder Perl distribution includes four modules. It is available for download on the Comprehensive Perl Archive Network (CPAN) at http://www.cpan.org.

Supplementary information: Supplementary information is available at Bioinformatics online.

Contact: martina.stromvik{at}mcgill.ca

Associate Editor: Prof. Ivo Hofacker

Received on May 14, 2008; revised on August 14, 2008; accepted on August 18, 2008

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