GADEM: a genetic algorithm guided formation of spaced dyads coupled with an EM algorithm for motif discovery.

ABSTRACT: Genome-wide analyses of protein binding sites generate large amounts of data; a ChIP dataset might contain 10,000 sites. Unbiased motif discovery in such datasets is not generally feasible using current methods that employ probabilistic models. We propose an efficient method, GADEM, which combines spaced dyads and an expectation-maximization (EM) algorithm. Candidate words (four to six nucleotides) for constructing spaced dyads are prioritized by their degree of overrepresentation in the input sequence data. Spaced dyads are converted into starting position weight matrices (PWMs). GADEM then employs a genetic algorithm (GA), with an embedded EM algorithm to improve starting PWMs, to guide the evolution of a population of spaced dyads toward one whose entropy scores are more statistically s

SUBMITTER: Li L 

PROVIDER: S-EPMC2756050 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature