Journal Article

Seeder: discriminative seeding DNA motif discovery

François Fauteux, Mathieu Blanchette and Martina V. Strömvik

in Bioinformatics

Volume 24, issue 20, pages 2303-2307
Published in print October 2008 | ISSN: 1367-4803
Published online August 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn444

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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.

Contact: martina.stromvik@mcgill.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  3445 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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