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

TitleCombining pattern discovery and discriminant analysis to predict gene co-regulation.
Publication TypeJournal Article
Year of Publication2004
AuthorsSimonis, N., Wodak S. J., Cohen G. N., and van Helden J.
JournalBioinformatics
Volume20
Issue15
Pagination2370-9
Date Published2004 Oct 12
ISSN1367-4803
KeywordsAlgorithms, 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.

DOI10.1093/bioinformatics/bth252
Alternate JournalBioinformatics
PubMed ID15073004