NDLI: Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm

Content Provider	ACM Digital Library
Author	Lones, Michael Tyrrell, Andy
Abstract	This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences.
Starting Page	403
Ending Page	414
Page Count	12
File Format	PDF
ISSN	15455963
DOI	10.1109/tcbb.2007.1044
Volume Number	4
Issue Number	3
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-07-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolutionary computation, population-based data clustering, motif discovery, transcription factor binding sites, muscle-specific gene expression
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm

Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm

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