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AlineaGA - a genetic algorithm with local search optimization for multiple sequence alignment
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| The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE. | 848.55 KB | Adobe PDF |
Orientador(es)
Resumo(s)
The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE.
Descrição
Palavras-chave
Multiple sequence alignments Genetic algorithms Local search Optimization Hybridization Bioinformatics
Contexto Educativo
Citação
da Silva, F.J.M., Sánchez Pérez, J.M., Gómez Pulido, J.A. et al. AlineaGA—a genetic algorithm with local search optimization for multiple sequence alignment. Appl Intell 32, 164–172 (2010). https://doi.org/10.1007/s10489-009-0189-4.
Editora
Springer Nature