Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA

Arenas-Díaz, Edgar D.; Ochoterena, Helga; Rodríguez-Vázquez, Katya

doi:https://doi.org/10.1155/2009/963150

Journal of Artificial Evolution and Applications

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Artificial Evolution Methods in the Biological and Biomedical Sciences

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Research Article | Open Access

Volume 2009 | Article ID 963150 | https://doi.org/10.1155/2009/963150

Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA

Edgar D. Arenas-Díaz,¹Helga Ochoterena,²and Katya Rodríguez-Vázquez¹

Academic Editor: Jason Moore

Received14 Nov 2008

Revised04 Apr 2009

Accepted13 Jun 2009

Published27 Aug 2009

Abstract

Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the number of putative synapomorphies. We also present a Genetic Algorithm that uses GLOCSA as the objective function to produce sequence alignments refining alignments previously generated by additional existing alignment tools (we recommend MUSCLE). We show that in the example cases our GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA values, resulting in alignments that imply less putative synapomorphies.

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Copyright

Copyright © 2009 Edgar D. Arenas-Díaz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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