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Bioinformatics Advance Access originally published online on April 21, 2006
Bioinformatics 2006 22(13):1577-1584; doi:10.1093/bioinformatics/btl147
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

GAME: detecting cis-regulatory elements using a genetic algorithm

Zhi Wei 1,* and Shane T. Jensen 2

1 Genomics and Computational Biology Graduate Group, University of Pennsylvania School of Medicine Philadelphia, PA 19104, USA
2 Department of Statistics, The Wharton School, University of Pennsylvania Philadelphia, PA 19104, USA

*To whom correspondence should be addressed.

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: http://mail.med.upenn.edu/~zhiwei/GAME/

Contact: zhiwei{at}mail.med.upenn.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


Received on January 10, 2006; revised on March 24, 2006; accepted on April 12, 2006

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