Nothing Special   »   [go: up one dir, main page]

Skip to main content

Multiple Sequence Alignment by Improved Hidden Markov Model Training and Quantum-Behaved Particle Swarm Optimization

  • Conference paper
Life System Modeling and Intelligent Computing (ICSEE 2010, LSMS 2010)

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

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Frishman, D., Argos, P.: Knowledge-based protein secondary structure assignment. Proteins 23, 566–579 (1995)

    Article  Google Scholar 

  2. Mount, D.W.: Bioinformatics: Sequence and Genome Analysis Cold Spring. Harbor Laboratory Press (2001)

    Google Scholar 

  3. Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Res. 24, 1515–1524 (1996)

    Article  Google Scholar 

  4. Nicholas Jr., H.B., et al.: Strategies for multiple sequence alignment. Biotechniques 32, 572–574 (2002)

    MathSciNet  Google Scholar 

  5. Feng, D.-F., Doolittle, R.: Progressive sequence alignment as a prerequisitetto correct phylogenetic trees. Journal of Molecular Evolution 25, 351–360 (1987)

    Article  Google Scholar 

  6. Myers: Multiple sequence alignment using simulated annealing. Computer Applications in the Biosciences 4, 7 (1988)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. Thomsen, R.: A Clustal alignment improver using evolutionary algorithms, pp. 121–126 (2002)

    Google Scholar 

  10. Churchill, G.A.: Stochastic models for heterogeneous DNA sequences. Bull. Math. Biol. 51, 79–94 (1989)

    MATH  MathSciNet  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Solis, F.J., Wets, R.J.-B.: Minimization by Random Search Techniques. Math. of Oper. Res. 6, 19–30 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  19. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–285 (1989)

    Article  Google Scholar 

  20. Thompson, J.D., et al.: BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15, 87–88 (1999)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics