Table of Contents
Journal of Artificial Evolution and Applications
Volume 2009, Article ID 963150, 10 pages
Research Article

Multiple Sequence Alignment Using a Genetic Algorithm and GLOCSA

1Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de Mexico, Circuito exterior s/n, Ciudad Universitaria, 04510 Mexico, DF, Mexico
2Instituto de Biología, Universidad Nacional Autónoma de Mexico, Apdo. Postal 70-367, 04510 Mexico, DF, Mexico

Received 14 November 2008; Revised 4 April 2009; Accepted 13 June 2009

Academic Editor: Jason Moore

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.


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.