MSCAN: identification of functional clusters of transcription factor binding sites
Center for Genomics and Bioinformatics, Karolinska Institutet, SE-17177 Stockholm, Sweden, 1 Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0114, USA, 2 Stockholm Bioinformatics Center and Department of Numerical Analysis and Computer Science, KTH, SE-10044 Stockholm, Sweden and 3 Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, B.C. Children's Hospital, University of British Columbia, Vancouver, V5Z 4H4 Canada
* To whom correspondence should be addressed. Tel: +1 604 875 3812; Fax: +1 604 875 3819; Email: wyeth{at}cmmt.ubc.ca
Received February 12, 2004; Revised and Accepted March 24, 2004
Identification of functional transcription factor binding sites in genomic sequences is notoriously difficult. The critical problem is the low specificity of predictions, which directly reflects the low target specificity of DNA binding proteins. To overcome the noise produced in predictions of individual binding sites, a new generation of algorithms achieves better predictive specificity by focusing on locally dense clusters of binding sites. MSCAN is a leading method for binding site cluster detection that determines the significance of observed sites while correcting for local compositional bias of sequences. The algorithm is highly flexible, applying any set of input binding models to the analysis of a user-specified sequence. From the user's perspective, a key feature of the system is that no reference data sets of regulatory sequences from co-regulated genes are required to train the algorithm. The output from MSCAN consists of an ordered list of sequence segments that contain potential regulatory modules. We have chosen the features in MSCAN such that sequence and matrix retrieval is highly facilitated, resulting in a web server that is intuitive to use. MSCAN is available at http://mscan.cgb.ki.se/cgi-bin/MSCAN.
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