Combining pattern discovery and discriminant analysis to predict gene co-regulation |
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Authors: | Simonis N Wodak S J Cohen G N van Helden J |
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Institution: | 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. nicolas@scmb.ulb.ac.be |
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Abstract: | 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. |
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