GAME

GAME: Detecting cis-regulatory elements using a genetic algorithm. 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/


References in zbMATH (referenced in 4 articles )

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  1. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  2. Wang, Dianhui; Do, Hai Thanh: Computational localization of transcription factor binding sites using extreme learning machines (2012) ioport
  3. Huo, Hongwei; Zhao, Zhenhua; Stojkovic, Vojislav; Liu, Lifang: Optimizing genetic algorithm for motif discovery (2010) ioport
  4. Chan, Tak-Ming; Li, Gang; Leung, Kwong-Sak; Lee, Kin-Hong: Discovering multiple realistic TFBS motifs based on a generalized model (2009) ioport