GAME: detecting cis-regulatory elements using a genetic algorithm

• Published in: Bioinformatics Vol. 22; no. 13; pp. 1577 - 1584
• Main Authors: Wei, Zhi, Jensen, Shane T.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.07.2006; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino Acid Motifs; Base Sequence; Bayes Theorem; Binding Sites; Bioinformatics; Biological and medical sciences; Computational Biology - methods; Escherichia coli - genetics; Fundamental and applied biological sciences. Psychology; Gene Expression Regulation; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Molecular Sequence Data; Pattern Recognition, Automated; Regulatory Elements, Transcriptional; Software; Bayes estimation; Scoring; Discoveries; Original document; Computer program; Genetic algorithm; Evolution; Regulatory sequence; Detection; Transcription factor; Web site; Bioinformatics; Comparative study
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btl147

Motivation: Identification of a transcription factor binding sites is an important aspect of the analysis of genetic regulation. Many programs have been developed for the de novo discovery of a binding motif (collection of binding sites). Recently, a scoring function formulation was derived that allows for the comparison of discovered motifs from different programs [S.T. Jensen, X.S. Liu, Q. Zhou and J.S. Liu (2004) Stat. Sci., 19, 188–204.] A simple program, BioOptimizer, was proposed in [S.T. Jensen and J.S. Liu (2004) Bioinformatics, 20, 1557–1564.] that improved discovered motifs by optimizing a scoring function. However, BioOptimizer is a very simple algorithm that can only make local improvements upon an already discovered motif and so BioOptimizer can only be used in conjunction with other motif-finding software. Results: We introduce software, GAME, which utilizes a genetic algorithm to find optimal motifs in DNA sequences. GAME evolves motifs with high fitness from a population of randomly generated starting motifs, which eliminate the reliance on additional motif-finding programs. In addition to using standard genetic operations, GAME also incorporates two additional operators that are specific to the motif discovery problem. We demonstrate the superior performance of GAME compared with MEME, BioProspector and BioOptimizer in simulation studies as well as several real data applications where we use an extended version of the GAME algorithm that allows the motif width to be unknown. Availability: Contact:zhiwei@mail.med.upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.