Abstract
A new evolutionary-progressive method for Multiple Sequence Alignment problem is proposed. The method efficiently combines flexibility of evolutionary approach with speed and accuracy of progressive technique. The results show that the hybrid method is an interesting alternative for purely genetic or purely progressive approaches.
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References
Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. Journal of Computational Biology 1(4), 337–348 (1994)
Carrillo, H., Lipman, D.J.: The multiple sequence alignment problem in biology. SIAM Journal on Applied Mathematics 48(5), 1073–1082 (1988)
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 Res. 22, 4673–4680 (1994)
Zhu, M.-J., Hu, G.-W., Zheng, Q.-L., Peng, H.: Multiple sequence alignment using minimum spanning tree. In: Proc. 4th International Conference on Machine Learning, Guangzhou, pp. 3352–3356 (2005)
Chaichoompu, K., Kittitornkun, S., Tongsima, S.: MT-ClustalW: Multithreading Multiple Sequence Alignment. In: Proc. International Parallel and Distributed Processing Symposium, IPDPS (2006)
Notredame, C., Higgins, D., Heringa, J.: T-coffee: A novel method for multiple sequence alignment. Journal of Molecular Biology 302, 205–217 (2000)
Zhang, C., Wong, A.K.C.: A genetic algorithm for multiple molecular sequence alignment. Computer Application in the Biosciences 13(6), 565–581 (1997)
Thomsen, R., Fogel, G.B., Krink, T.: Improvement of Clustal-Derived Sequence Alignments with Evolutionary Algorithms. In: Proc. The 2003 Congress on Evolutionary Algorithms, vol. 1, pp. 312–319 (2003)
Liu, L., Huo, H., Wang, B.: Aligning multiple sequences by genetic algorithm. In: Proc. International Conference on Communications, Circuits and Systems (ICCCAS’04), vol. 2, pp. 994–998 (2004)
Zhang, G.-Z., Huang, D.-S.: Aligning Multiple Protein Sequence by An Improved Genetic Algorithm. In: Proc. 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1179–1183 (2004)
Abdesslem, L., Soham, M., Mohamed, B.: Multiple Sequence Alignment by Quantum Genetic Algorithm. In: Proc. International Parallel and Distributed Processing Symposium, IPDPS (2006)
Myers, E.W., Miller, W.: Optimal alignments in linear space. Bioinformatics 4(1), 11–17 (1988)
Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res. 24(8), 1515–1524 (1996)
Chenna, R., et al.: Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Research 31(13), 3497–3500 (2003)
Thompson, J.D., Plewniak, F., Poch, O.: A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res. 27(13), 2682–2690 (1999)
Thompson, J.D., Koehl, P., Ripp, R., Poch, O.: BAliBASE 3.0: Latest Developments of the Multiple Sequence Alignment Benchmark. PROTEINS: Structure, Function, and Bioinformatics 61, 127–136 (2005)
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Kupis, P., Mańdziuk, J. (2007). Multiple Sequence Alignment with Evolutionary-Progressive Method. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_3
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DOI: https://doi.org/10.1007/978-3-540-71618-1_3
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