A NICHED PARETO GENETIC ALGORITHM - For Multiple Sequence Alignment Optimization

Fernando José Mateus da Silva, Juan Manuel Sánchez Pérez, Juan Antonio Gómez Pulido, Miguel A. Vega Rodríguez

Abstract

The alignment of molecular sequences is a recurring task in bioinformatics, but it is not a trivial problem. The size and complexity of the search space involved difficult the task of finding the optimal alignment of a set of sequences. Due to its adaptive capacity in large and complex spaces, Genetic Algorithms emerge as good candidates for this problem. Although they are often used in single objective domains, its use in multidimensional problems allows finding a set of solutions which provide the best possible optimization of the objectives – the Pareto front. Niching methods, such as sharing, distribute these solutions in space, maximizing their diversity along the front. We present a niched Pareto Genetic Algorithm for sequence alignment which we have tested with six BAliBASE alignments, taking conclusions regarding population evolution and quality of the final results. Whereas methods for finding the best alignment are mathematical, not biological, having a set of solutions which facilitate experts’ choice, is a possibility to consider.

References

  1. Anbarasu, L. A., Narayanasamy, P. & Sundararajan, V. (2000) Multiple molecular sequence alignment by island parallel genetic algorithm. Current Science, 78, 858-863.
  2. Chellapilla, K. & Fogel, G. B. (1999) Multiple sequence alignment using evolutionary programming. IN Angeline, P. J., Michalewicz, Z., Schoenauer, M., Yao, X. & Zalzala, A. (Eds.) Proceedings of the 1999 Congress on Evolutionary Computation. Washington DC, USA, IEEE Press.
  3. Dayhoff, M. O., Schwartz, R. M. & Orcutt, B. C. (1978) A Model of Evolutionary Change in Proteins. Atlas of Protein Sequence and Structure. National Biomedical Research Foundation.
  4. De Jong, K. (1988) Learning with genetic algorithms: An overview. Mach Learning, 3, 121-138.
  5. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning Reading, MA, Addison-Wesley Publishing Company.
  6. Goldberg, D. E. & Richardson, J. (1987) Genetic algorithms with sharing for multimodal function optimization. Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application. Cambridge, Massachusetts, United States, L. Erlbaum Associates Inc.
  7. Holland, J. H. (1975) Adaptation in natural and artificial systems, Univ Mich Press. Ann Arbor.
  8. Horn, J., Nafpliotis, N. & Goldberg, D. E. (1994) A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence 1, 82- 87.
  9. Horng, J.-T., Lin, C.-M., Liu, B.-J. & Kao, C.-Y. (2000) Using Genetic Algorithms to Solve Multiple Sequence Alignments. IN Whitley, L. D., Goldberg, D. E., Cantu-Paz, E., Spector, L., Parmee, I. C. & Beyer, H.- G. (Eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000). Las Vegas, Nevada, USA, Morgan Kaufmann.
  10. Horng, J., Wu, L., Lin, C. & Yang, B. (2005) A genetic algorithm for multiple sequence alignment. Soft Computing, 9, 407-420.
  11. Lassmann, T. & Sonnhammer, E. L. L. (2002) Quality assessment of multiple alignment programs. FEBS Letters, 529, 126-130.
  12. Michalewicz, Z. (1996) Genetic algorithms + data structures = evolution programs - Third, Revised and Extended Edition, Springer.
  13. Notredame, C. & Higgins, D. G. (1996) SAGA: sequence alignment by genetic algorithm. Nucleic Acids Research, 24, 1515-1524.
  14. Notredame, C., O'Brien, E. A. & Higgins, D. G. (1997) RAGA: RNA sequence alignment by genetic algorithm. Nucleic Acids Research, 25, 4570-4580.
  15. Pal, S. K., Bandyopadhyay, S. & Ray, S. S. (2006) Evolutionary computation in bioinformatics: A review. IEEE Transactions on Systems Man and Cybernetics Part C-Appl and Rev, 36, 601-615.
  16. Shir, O. M. & Back, T. (2006) Niche radius adaptation in the cma-es niching algorithm. Lecture Notes in Computer Science, 4193, 142.
  17. Silva, F. J. M., Sánchez Pérez, J. M., Gómez Pulido, J. A. & Vega Rodríguez, M. Á. (2007) Alineamiento Múltiple de Secuencias utilizando Algoritmos Genéticos: Revisión. Segundo Congreso Español de Informática. Zaragoza, Spain, CEDI.
  18. Silva, F. J. M., Sánchez Pérez, J. M., Gómez Pulido, J. A. & Vega Rodríguez, M. Á. (2008) AlineaGA: A Genetic Algorithm for Multiple Sequence Alignment. IN Nguyen, N. T. & Katarzyniak, R. (Eds.) New Challenges in Applied Intelligence Technologies. Springer-Verlag.
  19. Silva, F. J. M., Sánchez Pérez, J. M., Gómez Pulido, J. A. & Vega Rodríguez, M. Á. (2009) AlineaGA - A Genetic Algorithm with Local Search Optimization for Multiple Sequence Alignment. Applied Intelligence, 1- 9.
  20. Thompson, J. D., Plewniak, F. & Poch, O. (1999) BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics, 15, 87-88.
  21. Wang, C. & Lefkowitz, E. J. (2005) Genomic multiple sequence alignments: refinement using a genetic algorithm. BMC Bioinformatics, 6.
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Paper Citation


in Harvard Style

José Mateus da Silva F., Manuel Sánchez Pérez J., Antonio Gómez Pulido J. and A. Vega Rodríguez M. (2010). A NICHED PARETO GENETIC ALGORITHM - For Multiple Sequence Alignment Optimization . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 323-329. DOI: 10.5220/0002729303230329


in Bibtex Style

@conference{icaart10,
author={Fernando José Mateus da Silva and Juan Manuel Sánchez Pérez and Juan Antonio Gómez Pulido and Miguel A. Vega Rodríguez},
title={A NICHED PARETO GENETIC ALGORITHM - For Multiple Sequence Alignment Optimization},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={323-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002729303230329},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A NICHED PARETO GENETIC ALGORITHM - For Multiple Sequence Alignment Optimization
SN - 978-989-674-021-4
AU - José Mateus da Silva F.
AU - Manuel Sánchez Pérez J.
AU - Antonio Gómez Pulido J.
AU - A. Vega Rodríguez M.
PY - 2010
SP - 323
EP - 329
DO - 10.5220/0002729303230329