Island Injection Genetic Algorithm with Relaxed Coordination for the Multiple Sequence Alignment Problem

  • Lidia Araujo Miranda
  • Marcos Fagundes Caetano
  • Luiza Jaques
  • Jan Mendonca Correa
  • Alba Cristina Magalhaes Alves de Melo
  • Jacir Luiz Bordim
Part of the Studies in Computational Intelligence book series (SCI, volume 422)

Abstract

Multiple sequence alignment (MSA) is an important problem in Bioinformatics since it is often used to identify evolutionary relationships and predict secondary/tertiary structure, among others. MSAs are usually scored with the Sum-of-Pairs (SP) function and the exact SP MSA is known to be NP-Hard. Therefore, heuristic methods are used to tackle this problem. In this chapter, we propose and evaluate a parallel island injection genetic algorithm to solve the MSA problem. Unlike the other strategies, our parallel solution uses two types of interconnected archipelagoes, each with distinct types of individuals. Also, we added a relaxed coordination mechanism among the archipelagoes that contributes to reduce the execution time of our strategy. The results obtained with real protein data sets show that our strategy is able to obtain better results, when compared to the traditional island model. Also, we were able to reduce considerably the execution time, when compared to the sequential version.

Keywords

Genetic Algorithm Execution Time Good Individual Island Model Parallel Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ambarasu, L.A., Narayanasamy, P., Sundararajan, V.: Multiple Molecular Sequence Alignment by Island Parallel Genetic Algorithm. Current Science 78(7), 858–863 (2000)Google Scholar
  2. 2.
    Babbar, M., Minsker, B.S., Goldberg, D.: A Multiscale Island Injection Genetic Algorithm for Optimal Groundwater Remediation Design. Journal of Water Res. Plan & Man 132(5), 341–350 (2006)CrossRefGoogle Scholar
  3. 3.
    Cantu-Paz, E.: Implementing Fast and Flexible Parallel Genetic Algorithms. Practical Handbook of Genetic Algorithms 3, 65–84 (1998)Google Scholar
  4. 4.
    Eddy, S.: HMMER User’s Guide v. 2.3.2. Washington University School of Medicine (2003)Google Scholar
  5. 5.
    Finn, R., et al.: The Pfam Protein Families Database. Nucleic Acids Research 36, D281–D288 (2008)CrossRefGoogle Scholar
  6. 6.
    Heringa, J., Notredame, C., Higgins, D.G.: T-COFFEE: a Novel Method for Fast and Accurate Multiple Sequence Alignment. Journal of Mol. Biol. 302(1), 205–217 (2000)CrossRefGoogle Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 387p. Springer (1999)Google Scholar
  8. 8.
    Mount, D.: Bioinformatics: Sequence and Genome Analysis, 692p. C. S. Harbor Lab Press (2004)Google Scholar
  9. 9.
    Morgenstern, B., et al.: Multiple DNA and Protein Sequence Alignment Based on Segment-to-Segment Comparison. Proc. of Natl. Acad. Sci., USA, 12098–12103 (1996)Google Scholar
  10. 10.
    Nguyen, H.D., Yoshihara, I., Yamamori, K., Yasunaga, M.: Aligning Multiple Protein Sequences by Parallel Hybrid Genetic Algorithm. Genome Informatics 13, 123–132 (2002)Google Scholar
  11. 11.
    Notredame, C., Higgins, D.G.: SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research 24(8), 407–422 (1996)CrossRefGoogle Scholar
  12. 12.
    Thompson, J.D., Higgins, D.G., Gibson, T.J.: Clustal W: Improving the Sensitivity of Progressive Multiple Sequence Alignment Through Sequences Weighting, Position-Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  13. 13.
    Thompson, J.D., Koehl, P., Ripp, R., Poch, O.: BAliBASE 3.0: Latest Developments of Multiple Sequence Alignment Benchmark. Proteins: Structure, Function and Bioinformatics 61(1), 127–136 (2005)CrossRefGoogle Scholar
  14. 14.
    Wang, C., Lefkowitz, E.J.: Genomic Multiple Sequence Alignments: “Refinements using a Genetic Algorithm”. BMC Bioinformatics 6 (2005)Google Scholar
  15. 15.
    Wang, T., Jiang, T.: On the Complexity of Multiple Sequence Alignment. J. Comp. Biol. 1(4), 337–348 (1994)CrossRefGoogle Scholar
  16. 16.
    Silva, F.J.M., Perez, J.M.S., Pulido, J.A.G., Rodriguez, M.A.V.: Parallel AlineaGA: an Island Parallel Evolutionary Algorithm for Multiple Sequence Alignment. In: Proc. of the Int. Conf. on Soft Computing and Pattern Recognition (SoCPar), pp. 279–284 (2010)Google Scholar
  17. 17.
    Notredame, C., Obrien, E.A., Higgins, D.G.: RAGA: RNA Sequence Alignment by Genetic Algorithm. Nucleid Acids Research 25(22), 4570–4580 (1997)CrossRefGoogle Scholar
  18. 18.
    Miranda, L.A., Caetano, M.F., Melo, A.C.M.A., Correa, J.M., Bordim, J.L.: Multiple Biological Sequence Alignment with a Parallel Island Injection Genetic Algorithm. In: Proc. of the IEEE Int. Conf. on High Performance Computing and Communications (HPCC), pp. 314–321 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lidia Araujo Miranda
    • 1
  • Marcos Fagundes Caetano
    • 1
  • Luiza Jaques
    • 1
  • Jan Mendonca Correa
    • 1
  • Alba Cristina Magalhaes Alves de Melo
    • 1
  • Jacir Luiz Bordim
    • 1
  1. 1.Department of Computer ScienceUniversity of Brasilia (UnB)BrasiliaBrazil

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