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A Hybrid Genetic Algorithm Using Dynamic Distance in Mutation Operator for Solving MSA Problem

  • Rohit Kumar YadavEmail author
  • Haider Banka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)

Abstract

In this paper, a hybrid Genetic Algorithm for solving multiple sequence alignment problems is proposed. Two new mechanisms have been introduced, i.e., one to generate the initial population and the second one is used during mutation operation. Here, the initial populations have been generated by Needleman Wunsch pair-wise alignment method. In the second step, the UPGMA method is used to generate the Guide tree with the help of two different matrix such as dynamic distance and edit distance matrix. The performance of the proposed method has been tested on publicly available benchmark datasets (i.e. Bali base) with some of the existing methods such as PRRP, CLUSTALX, SB−PIMA, MULTALIGN, SAGA, RBT-GA. We find that proposed method is better in most of cases and where it is not better at least close to best solution.

Keywords

Multiple sequence alignment Genetic algorithm Pair-wise alignment Edit distance Dynamic distance 

REFERENCES

  1. 1.
    Das, S., Abraham, A., Konar, A.: Swarm intelligence algorithms in bioinformatics. Stud. Comput. Intell. 94, 113–147 (2008)Google Scholar
  2. 2.
    Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1, 337–348 (1994)CrossRefGoogle Scholar
  3. 3.
    Feng, D.F., Doolittle, R.F.: Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evolution 25, 351–360 (1987)CrossRefGoogle Scholar
  4. 4.
    Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTALW: 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)CrossRefGoogle Scholar
  5. 5.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. In: Proceedings of the IEEE. vol. 77, pp. 257–285 (1989)Google Scholar
  6. 6.
    Baldi, P., Chauvin, Y., Hunkapiller, T., McClure, M.A.: Hidden Markov Models of biological primary sequence information. Proc. Natl. Acad. Sci. U.S.A. 91, 1059–1063 (1994)CrossRefGoogle Scholar
  7. 7.
    Krogh, A., Brown, M., Mian, I.S., Sjolander, K., Haussler, D.: Hidden Markov models in computational biology: applications to protein modeling. J. Mol. Biol. 235, 1501–1531 (1994)CrossRefGoogle Scholar
  8. 8.
    Eddy, S.R.: Profile hidden Markov models. Bioinformatics 14, 755–763 (1998)CrossRefGoogle Scholar
  9. 9.
    Kim, J., Pramanik, S., Chung, M.J.: Multiple sequence alignment using simulated annealing. Bioinformatics 10, 419–426 (1994)CrossRefGoogle Scholar
  10. 10.
    Lukashin, A.V., Engelbrecht, J., Brunak, S.: Multiple alignment using simulated annealing: branch point definition in human mRNA splicing. Nucl. Acids Res. 20, 2511–2516 (1992)CrossRefGoogle Scholar
  11. 11.
    Chellapilla, K., Fogel, G.B.: Multiple sequence alignment using evolutionary programming. In: Proceedings of the First Congress on Evolution Composition, pp. 445–452 (1999)Google Scholar
  12. 12.
    Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucl. Acids Res. 24, 1515–1524 (1996)CrossRefGoogle Scholar
  13. 13.
    Thomsen, R.: Evolving the topology of hidden markov models using evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.G., Fernández-Villacañas, J.L., Schwefel, H.P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 861–870. Springer, Heidelberg (2002)Google Scholar
  14. 14.
    Taheri, J., Zomaya, A.Y.: RBT-GA: a novel metaheuristic for solving the multiple sequence alignment problem. BMC Genom. 10, 1–11 (2009)CrossRefGoogle Scholar
  15. 15.
    Dayhoff, M.O., Schwartz, R.M., Orcutt, B.C.: A model of evolutionary change in proteins. Atlas Protein Seq. Struct. 5, 345–351 (1978)Google Scholar
  16. 16.
    Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F., Higgins, D.G.: The CLUSTAL − X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucl. Acids Res. 25, 4876–4882 (1997)CrossRefGoogle Scholar
  17. 17.
    Naznin, F., Sarker, R., Essam, D.: Progressive alignment method using genetic algorithm for multiple sequence alignment. IEEE Trans. Evol. Comput. 16, 615–631 (2012)CrossRefGoogle Scholar
  18. 18.
    Yadav, R.K., Banka, H.: Genetic algorithm with improved mutation operator for multiple sequence alignment. In: Mandal, J.K., Satapathy, S.C., Sanyal, M.K., Sarkar, P.P., Mukhopadhyay, A. (eds.) Information Systems Design and Intelligent Applications, pp. 515–524. Springer, New Delhi (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian School of MinesDhanbadIndia

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