Bioinformatics Advance Access published online on April 13, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl125
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1 Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
* To whom correspondence should be addressed.
Summary: DPM is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence, and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to: a/ detect regions in a genomic sequence which are similar to the target promoters, or b/ to classify other promoters as similar or not to the target promoter group. DPM can also be used for modeling of enhancers and silencers. Availability: http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/. Supplementary Information: Manual for using DPM web server is provided at http://defiant.i2r.a-star.edu.sg/projects/BayesPromoter/html/manual/manual.htm.
Received December 29, 2005
Revised March 26, 2006
Accepted March 28, 2006
Applications note
Dragon Promoter Mapper (DPM): a Bayesian framework for modeling promoter structures
Rajesh Chowdhary 1,
Sin Lam Tan 1,
R. Ayesha Ali 2,
Brent Boerlage 3,
Limsoon Wong 4,
and
Vladimir B. Bajic 5 *
2 Department of Mathematics & Statistics, University of Guelph, Guelph ON, Canada N1G 2W1
3 Norsys Software Corp., 3512 West 23rd Avenue, Vancouver, BC, Canada V6S 1K5
4 School of Computing, National University of Singapore, Singapore 117543
5 Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613; University of the Western Cape, South African National Bioinformatics Institute (SANBI), Private Bag X17, Bellville 7535, South Africa
Vladimir B. Bajic, E-mail: vlad{at}sanbi.ac.za
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Associate Editor: Keith A Crandall
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