Lones, Michael A. and Tyrrell, Andy M. (2007) Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics. pp. 403-414. ISSN 1545-5963Full text available as:
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.
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|Keywords:||evolutionary computation, population-based data clustering, motif discovery, transcription factor binding sites, muscle specific gene expression, FACTOR-BINDING SITES, TRANSCRIPTIONAL REGULATION, SEQUENCE|
|Academic Units:||The University of York > Electronics (York)|
|Depositing User:||Ms Diana Hilmer|
|Date Deposited:||09 Nov 2007 15:37|
|Last Modified:||17 Oct 2013 14:14|