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Bioinformatics Advance Access originally published online on April 8, 2004
Bioinformatics 2004 20(15):2370-2379; doi:10.1093/bioinformatics/bth252
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Bioinformatics 20(15) © Oxford University Press 2004; all rights reserved.

Combining pattern discovery and discriminant analysis to predict gene co-regulation

N. Simonis 1,*, S. J. Wodak 1, G. N. Cohen 2 and J. van Helden 1

1 Service de Conformation des Macromolécules Biologiques et Bioinformatique, Centre de Biologie Structurale et Bioinformatique, CP 263, Université Libre de Bruxelles, Bld. du Triomphe B-1050 Bruxelles, Belgium and 2 Institut Pasteur, Unité d’Expression des Gènes Eucaryotes, Institut Pasteur, 28, rue du Docteur Roux, 75724 Paris Cedex 15, France

Received on November 14, 2003; accepted on March 26, 2004
Advance Access Publication April 8, 2004

Motivation: Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences.

Methods: String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs.

Results: The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes.

Supplementary information: http://rsat.ulb.ac.be/rsat/

Contact: mailto:nicolas{at}scmbb.ulb.ac.be

* To whom correspondence should be addressed.


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