Abstract
Multiple sequence alignment (MSA), known as NP-complete problem, is one of the basic problems in computational biology. Presently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization (QPSO) is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and QPSO for the MSA is proposed. In order to evaluate the approach protein sequences are taken. Finally, compared with other algorithms, the results show that the proposed algorithm not only finds out perfect profile HMM, but also produces the optimal alignment of multiple sequences.
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
Frishman, D., Argos, P.: Knowledge-based protein secondary structure assignment. Proteins 23, 566–579 (1995)
Mount, D.W.: Bioinformatics: Sequence and Genome Analysis Cold Spring. Harbor Laboratory Press (2001)
Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res. 24, 1515–1524 (1996)
Nicholas Jr., H.B., et al.: Strategies for multiple sequence alignment. Biotechniques 32, 572–574 (2002)
Feng, D.-F., Doolittle, R.: Progressive sequence alignment as a prerequisitetto correct phylogenetic trees. Journal of Molecular Evolution 25, 351–360 (1987)
Myers: Multiple sequence alignment using simulated annealing. Computer Applications in the Biosciences 4, 7 (1988)
Licheng, J., Lei, W.: A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 30, 552–561 (2000)
e-Jung Lee, S.-F.S., Chuang, C.-C., Liu, K.-H.: Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Applied Soft Computing 8 (2008)
Thomsen, R.: A Clustal alignment improver using evolutionary algorithms, pp. 121–126 (2002)
Churchill, G.A.: Stochastic models for heterogeneous DNA sequences. Bull. Math. Biol. 51, 79–94 (1989)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77, 257–286 (1989); Loytynoja, A., Milinkovitch, M.C.: A hidden Markov model for progressive multiple alignment. Bioinformatics 19, 1505–1513 (2003)
Mamitsuka, H.: Finding the biologically optimal alignment of multiple sequences. Artif. Intell. Med. 35, 9–18 (2005); Krogh, A., et al.: Hidden Markov models in computational biology. Applications to protein modeling. J. Mol. Biol. 235, 1501–1531 (1994)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Jun, S., et al.: Particle swarm optimization with particles having quantum behavior. In: Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 325–331 (2004)
Jun, S., et al.: A global search strategy of quantum-behaved particle swarm optimization. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 111–116 (2004)
Jun, S., et al.: Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: 2005 IEEE International Conference on Systems, Man and Cybernetics 2005, vol. 4, pp. 3049–3054 (2005)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Solis, F.J., Wets, R.J.-B.: Minimization by Random Search Techniques. Math. of Oper. Res. 6, 19–30 (1981)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–285 (1989)
Thompson, J.D., et al.: BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15, 87–88 (1999)
Thompson, J.D., et al.: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl. Acids Res. 22, 4673–4680 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Long, H., Ding, Y., Sun, J., Xu, W. (2010). Multiple Sequence Alignment by Improved Hidden Markov Model Training and Quantum-Behaved Particle Swarm Optimization. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-15615-1_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15614-4
Online ISBN: 978-3-642-15615-1
eBook Packages: Computer ScienceComputer Science (R0)