Abstract
This paper describes a novel approach to deal with multiple sequence alignment (MSA). MSA is an essential task in bioinformatics which is at the heart of denser and more complex tasks in biological sequence analysis. MSA problem still attracts researcher’s attention despite the significant research effort spent to solve it. We propose in this paper a quantum evolutionary algorithm to improve solutions given by CLUSTALX package. The contribution consists in defining an appropriate representation scheme that allows applying successfully on MSA problem some quantum computing principles like qubit representation and superposition of states. This representation scheme is embedded within an evolutionary algorithm leading to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to improve by many orders of magnitude the CLUSTALX’s solutions.
This is a preview of subscription content, log in via an institution to check access.
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
Attwood, T.K., Smith, D.J.: Introduction to Bioinformatics. Addison-Wesley Longman, Amsterdam (1999)
Notredame, C.: Recent progresses in MSA: a survey. Pharmacogenomic 3, 1–14 (2002)
Thomsen, R., Fogel, G.B., Krink, T.A.: Clustal Alignment Improver using Evolutionary Algorithms. In: Proceedings of the Fourth Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 121–126 (2002)
Notredame, C., Holm, L., Higgins, D.G.: COFFEE: An objective function for multiple sequence alignments. Bioinformatics 14(5), 407–422 (1998)
Needlman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino-acid sequence of two proteins. Journal of Molecular Biology 48, 443–453 (1970)
Kim, J., Pramanik, S., Chung, M.J.: Multiple sequence alignment using simulated annealing. Computer applications in bioscience 10, 419–426 (1994)
Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research 24(8), 1515–1524 (1996)
Riaz, T., Wang, Y., Li, K.B.: Multiple sequence alignment using Tabu Search. In: Proc. 2nd Asia-Pacific Bioinformatics Conference (APBC), vol. 2004, pp. 223–232 (2004)
Chellapilla, K., Fogel, G.B.: Multiple sequence alignment using evolutionary programming. In: Proc. 1999 Congr. Evolutionary Computation, pp. 445–452 (1999)
Anbarasu, L.A., Narayanasamy, P., Sundararajan, V.: Multiple molecular sequence alignment by island parallel genetic algorithm. Current Science 78, 858–863 (2000)
Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proc. IEEE Int. Conf. Evolutionary Computation, vol. 1996, pp. 61–66 (1996)
Han, K.H., Kim, J.H.: Quantum-Inspired Evolutionary Algorithms with a New Termination Criterion, H ε Gate, and Two-Phase Scheme. IEEE Transactions on Evolutionary Computation 8, 156–169 (2004)
Shor, P.W.: Algorithms for quantum computation: Discrete logarithms and factoring. In: Proc. 35th Annu. Symp. Foundations Computer Science, Sante Fe, NM, pp. 124–134 (1994)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proc. 28th ACM Symp. Theory Computing, pp. 212–219 (1996)
Williams, C.P., Clearwater, S.H.: Explorations in quantum computing. Springer, Berlin (1998)
Han, K.H., Kim, J.H.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proc. 2000 Congr. Evolutionary Computation, La Jolla, CA, vol. 2, pp. 1354–1360 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meshoul, S., Layeb, A., Batouche, M. (2005). A Quantum Evolutionary Algorithm for Effective Multiple Sequence Alignment. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_26
Download citation
DOI: https://doi.org/10.1007/11595014_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
Online ISBN: 978-3-540-31646-6
eBook Packages: Computer ScienceComputer Science (R0)