Skip Navigation



Bioinformatics Advance Access published online on April 13, 2006

Bioinformatics, doi:10.1093/bioinformatics/btl125
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrowOA All Versions of this Article:
22/18/2310    most recent
btl125v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Chowdhary, R.
Right arrow Articles by Bajic, V. B.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Chowdhary, R.
Right arrow Articles by Bajic, V. B.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
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 *

1 Knowledge Extraction Lab, Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613
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

* To whom correspondence should be addressed.
Vladimir B. Bajic, E-mail: vlad{at}sanbi.ac.za


   Abstract

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.


Associate Editor: Keith A Crandall
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
K.-J. Won, A. Sandelin, T. T. Marstrand, and A. Krogh
Modeling promoter grammars with evolving hidden Markov models
Bioinformatics, August 1, 2008; 24(15): 1669 - 1675.
[Abstract] [Full Text] [PDF]


Home page
DNA ResHome page
A. Vandenbon, Y. Miyamoto, N. Takimoto, T. Kusakabe, and K. Nakai
Markov Chain-based Promoter Structure Modeling for Tissue-specific Expression Pattern Prediction
DNA Res, February 7, 2008; (2008) dsm034v1.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.