Improved benchmarks for computational motif discovery

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 benchmark suite for motif discovery, none of the common motif models can accurately discriminate the binding sites from remaining sequence. This may obscure the distinction between the potential performance of the moti...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Geir Kjetil Sandve, Osman Abul, Vegard Walseng, Finn Drabløs
• Keywords: BMCBI 2007, Motif Discovery, Motif Models, Transcription Factors