Dragon Promoter Mapper (DPM): A Bayesian framework for modelling promoter structures

Rajesh Chowdhary, Sin Lam Tan, R. Ayesha Ali, Brent Boerlage, Limsoon Wong, Vladimir Bajic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Summary: Dragon Promoter Mapper (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 (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers.

Original languageEnglish (US)
Pages (from-to)2310-2312
Number of pages3
JournalBioinformatics
Volume22
Issue number18
DOIs
StatePublished - Sep 15 2006

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Dragon Promoter Mapper (DPM): A Bayesian framework for modelling promoter structures'. Together they form a unique fingerprint.

Cite this