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Bioinformatics. 2002 Sep;18(9):1167-75.

Identification of regulatory elements using a feature selection method.

Author information

  • 1Division of Biostatistics, U. of California, Berkeley, CA 94720, USA. keles@stat.berkeley.edu

Abstract

MOTIVATION:

Many methods have been described to identify regulatory motifs in the transcription control regions of genes that exhibit similar patterns of gene expression across a variety of experimental conditions. Here we focus on a single experimental condition, and utilize gene expression data to identify sequence motifs associated with genes that are activated under this experimental condition. We use a linear model with two-way interactions to model gene expression as a function of sequence features (words) present in presumptive transcription control regions. The most relevant features are selected by a feature selection method called stepwise selection with monte carlo cross validation. We apply this method to a publicly available dataset of the yeast Saccharomyces cerevisiae, focussing on the 800 basepairs immediately upstream of each gene's translation start site (the upstream control region (UCR)).

RESULTS:

We successfully identify regulatory motifs that are known to be active under the experimental conditions analyzed, and find additional significant sequences that may represent novel regulatory motifs. We also discuss a complementary method that utilizes gene expression data from a single microarray experiment and allows averaging over variety of experimental conditions as an alternative to motif finding methods that act on clusters of co-expressed genes.

AVAILABILITY:

The software is available upon request from the first author or may be downloaded from http://www.stat.berkeley.edu/~sunduz.

CONTACT:

keles@stat.berkeley.edu

PMID:
12217908
[PubMed - indexed for MEDLINE]
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