DocumentCode :
1092718
Title :
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
Author :
Lones, Michael A. ; Tyrrell, Andy M.
Author_Institution :
York Univ., York
Volume :
4
Issue :
3
fYear :
2007
Firstpage :
403
Lastpage :
414
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.
Keywords :
DNA; biology computing; evolutionary computation; molecular biophysics; stochastic processes; DNA promoter sequences; data clustering; population clustering evolutionary algorithm; position frequency matrix models; regulatory motif discovery; solution diversity; synthetic data sets; Amino acids; Biological system modeling; Biology computing; Clustering algorithms; DNA; Evolutionary computation; Frequency; Partitioning algorithms; Proteins; Sequences; Evolutionary computation; motif discovery; muscle-specific gene expression; population-based data clustering; transcription factor binding sites; Algorithms; Amino Acid Motifs; Binding Sites; Cluster Analysis; Evolution, Molecular; Promoter Regions (Genetics); Protein Binding; Regulatory Sequences, Nucleic Acid; Sequence Analysis, DNA; Transcription Factors;
fLanguage :
English
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher :
ieee
ISSN :
1545-5963
Type :
jour
DOI :
10.1109/tcbb.2007.1044
Filename :
4288066
Link To Document :
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