- author = "Conrad Shyu and Luke Sheneman and James A. Foster",
- title = "Multiple Sequence Alignment with Evolutionary Computation",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2004",
- volume = "5",
- number = "2",
- pages = "121--144",
- month = jun,
- keywords = "genetic algorithms, genetic programming, multiple sequence alignment, genetic algorithm, progressive alignments, DNA sequences",
- ISSN = "1389-2576",
-
DOI = "
doi:10.1023/B:GENP.0000023684.05565.78",
- abstract = "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 approach uses 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.",
-
notes = "Initiatives for Bioinformatics and Evolutionary
Studies (IBEST), Department of Bioinformatics and
Computational Biology, University of Idaho, Moscow,
Idaho 83844-1010, USA
Part of \cite{banzhaf:2004:biogec} Special Issue on Biological Applications of Genetic and Evolutionary Computation Guest Editor(s): Wolfgang Banzhaf , James Foster",
