Abstract
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. We have designed a Genetic Algorithm for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the hemoglobin family. We also present the achieved results so as the comparisons performed with results provided by T-COFFEE.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pal, S.K., Bandyopadhyay, S., Ray, S.S.: Evolutionary computation in bioinformatics: A review. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 36, 601–615 (2006)
Horng, J., Wu, L., Lin, C., Yang, B.: A genetic algorithm for multiple sequence alignment. Soft Computing 9, 407–420 (2005)
Notredame, C., Higgins, D.G., Heringa, J.: T-Coffee: A novel method for fast and accurate multiple sequence alignment. Journal of Molecular Biology 302, 205–217 (2000)
Lecompte, O., Thompson, J.D., Plewniak, F., Thierry, J.-C., Poch, O.: Multiple alignment of complete sequences (MACS) in the post-genomic era, Gene, pp. 17–30 (2001)
Anbarasu, L.A., Narayanasamy, P., Sundararajan, V.: Multiple molecular sequence alignment by island parallel genetic algorithm. Current Science 78, 858–863 (2000)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs - Third, Revised and Extended Edition. Springer, Heidelberg (1996)
de Vega, F.F.: Parallel and Distributed Genetic Programming Models with applications to Logic Síntesis on FPGAs. Computer Science Department, vol. PhD., Universidad de Extremadura, Cáceres, p. 156 (2001)
De Jong, K.: Learning with genetic algorithms: An overview. Machine Learning 3, 121–138 (1988)
Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research 24, 1515–1524 (1996)
Notredame, C., O’Brien, E.A., Higgins, D.G.: RAGA: RNA sequence alignment by genetic algorithm. Nucleic Acids Research 25, 4570–4580 (1997)
Nicholas Jr., H.B., Ropelewski, A.J., Deerfield II, D.W.: Strategies for Multiple Sequence Alignment. BioTechniques 32, 572–591 (2002)
Wang, C., Lefkowitz, E.J.: Genomic multiple sequence alignments: refinement using a genetic algorithm. BMC Bioinformatics 6 (2005)
Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22, 4673–4680 (1994)
L, J.: Calculate PAM Matrix. Wageningen Bioinformatics Webportal (2004), http://www.bioinformatics.nl/tools/pam.html
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)
Zhang, C., Wong, A.K.C.: A genetic algorithm for multiple molecular sequence alignment. Comput. Appl. Biosci. 13, 565–581 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
da Silva, F.J.M., Pérez, J.M.S., Pulido, J.A.G., Rodríguez, M.A.V. (2008). AlineaGA: A Genetic Algorithm for Multiple Sequence Alignment. In: Nguyen, N.T., Katarzyniak, R. (eds) New Challenges in Applied Intelligence Technologies. Studies in Computational Intelligence, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79355-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-79355-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79354-0
Online ISBN: 978-3-540-79355-7
eBook Packages: EngineeringEngineering (R0)