MDGA: motif discovery using a genetic algorithm

  • Authors:

  • Dongsheng Che;Yinglei Song;Khaled Rasheed

  • Affiliations:

  • University of Georgia, Athens, GA;University of Georgia, Athens, GA;University of Georgia, Athens, GA

  • Venue:
  • GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
  • Year:
  • 2005

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Abstract

Computationally identifying transcription factor binding sites in the promoter regions of genes is an important problem in computational biology and has been under intensive research for a decade. To predict the binding site locations efficiently, many algorithms that incorporate either approximate or heuristic techniques have been developed. However, the prediction accuracy is not satisfactory and binding site prediction thus remains a challenging problem. In this paper, we develop an approach that can be used to predict binding site motifs using a genetic algorithm. Based on the generic framework of a genetic algorithm, the approach explores the search space of all possible starting locations of the binding site motifs in different target sequences with a population that undergoes evolution. Individuals in the population compete to participate in the crossovers and mutations occur with a certain probability. Initial experiments demonstrated that our approach could achieve high prediction accuracy in a small amount of computation time. A promising advantage of our approach is the fact that the computation time does not explicitly depend on the length of target sequences and hence may not increase significantly when the target sequences become very long.