Improved benchmarks for computational motif discovery.

ABSTRACT:

Background

An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research.

Results

We use a machine learning perspective to analyze collections of transcription factors with known binding sites. Algorithms are presented for finding position weight matrices (PWMs), IUPAC-type motifs and mismatch motifs with optimal discrimination of binding sites from remaining sequence. We show that for many data sets in a recently proposed benchm

SUBMITTER: Sandve GK 

PROVIDER: S-EPMC1903367 | biostudies-literature | 2007 Jun

REPOSITORIES: biostudies-literature