Title | Combining pattern discovery and discriminant analysis to predict gene co-regulation. |
Publication Type | Journal Article |
Year of Publication | 2004 |
Authors | Simonis, N., S. J. Wodak, G. N. Cohen, and J. van Helden |
Journal | Bioinformatics |
Volume | 20 |
Issue | 15 |
Pagination | 2370-9 |
Date Published | 2004 Oct 12 |
ISSN | 1367-4803 |
Keywords | Algorithms, Computer Simulation, Discriminant Analysis, Gene Expression Regulation, Genes, Regulator, Models, Genetic, Models, Statistical, Pattern Recognition, Automated, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Sequence Alignment, Sequence Analysis, DNA, Transcription Factors |
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. |
DOI | 10.1093/bioinformatics/bth252 |
Alternate Journal | Bioinformatics |
PubMed ID | 15073004 |
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