An Ant-Based Model for Multiple Sequence Alignment
Conference paper
- 2 Citations
- 1.1k Downloads
Abstract
Multiple sequence alignment is a key process in today’s biology, and finding a relevant alignment of several sequences is much more challenging than just optimizing some improbable evaluation functions. Our approach for addressing multiple sequence alignment focuses on the building of structures in a new graph model: the factor graph model. This model relies on block-based formulation of the original problem, formulation that seems to be one of the most suitable ways for capturing evolutionary aspects of alignment. The structures are implicitly built by a colony of ants laying down pheromones in the factor graphs, according to relations between blocks belonging to the different sequences.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Altschul, S.F.: Gap costs for multiple sequence alignment. Journal of Theoretical Biology 138, 297–309 (1989)MathSciNetCrossRefGoogle Scholar
- 2.Chen, Y., et al.: Partitioned optimization algorithms for multiple sequence alignment. In: Proceedings of the 20th International Conference on Advanced Information Networking and Applications (AINA 2006), vol. 2, pp. 618–622. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
- 3.Dorigo, M., Di Caro, G.: New Ideas in Optimization. In: Corne, D., Dorigo, M., Glover, F. (eds.) The Ant Colony Optimization Meta-Heuristic, pp. 11–32. McGraw-Hill, New York (1997)Google Scholar
- 4.Grassé, P.-P.: La reconstruction du nid et les coordinations inter-individuelles chez belicositermes natalensis et cubitermes s.p. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes sociaux 6, 41–80 (1959)CrossRefGoogle Scholar
- 5.Kurtz, S., et al.: Versatile and open software for comparing large genomes. Genome Biology 5(2), 12 (2004)CrossRefGoogle Scholar
- 6.Moss, J.D., Johnson, C.G.: An ant colony algorithm for multiple sequence alignment in bioinformatics. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds.) Artificial Neural Networks and Genetic Algorithms, pp. 182–186. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 7.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)CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2008