Multiple Sequence Alignment with Evolutionary Computation

  • Authors:

  • Conrad Shyu;Luke Sheneman;James A. Foster

  • Affiliations:

  • Initiatives for Bioinformatics and Evolutionary Studies (IBEST), Department of Bioinformatics and Computational Biology, University of Idaho, Moscow, USA 83844-1010;Initiatives for Bioinformatics and Evolutionary Studies (IBEST), Department of Bioinformatics and Computational Biology, University of Idaho, Moscow, USA 83844-1010;Initiatives for Bioinformatics and Evolutionary Studies (IBEST), Department of Bioinformatics and Computational Biology, University of Idaho, Moscow, USA 83844-1010

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2004

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Abstract

In this paper we provide a brief review of current work in the area of multiple sequence alignment (MSA) for DNA and protein sequences using evolutionary computation (EC). We detail the strengths and weaknesses of EC techniques for MSA. In addition, we present two novel approaches for inferring MSA using genetic algorithms. Our first novel approach utilizes a GA to evolve an optimal guide tree in a progressive alignment algorithm and serves as an alternative to the more traditional heuristic techniques such as neighbor-joining. The second novel approach facilitates the optimization of a consensus sequence with a GA using a vertically scalable encoding scheme in which the number of iterations needed to find the optimal solution is approximately the same regardless the number of sequences being aligned. We compare both of our novel approaches to the popular progressive alignment program Clustal W. Experiments have confirmed that EC constitutes an attractive and promising alternative to traditional heuristic algorithms for MSA.