Advertisement

Progress in Artificial Intelligence

, Volume 6, Issue 3, pp 195–210 | Cite as

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

  • Cristian Zambrano-Vega
  • Antonio J. NebroEmail author
  • José García-Nieto
  • José F. Aldana-Montes
Regular Paper

Abstract

Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a three-objective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones MOCell, GWASF-GA, and NSGA-III. Our study concludes that NSGA-II provides the best overall performance.

Keywords

Multiple sequence alignment Multi-objective optimization Metaheuristics Comparative study 

Notes

Acknowledgements

The first author acknowledges Universidad Técnica Estatal de Quevedo (Ecuador) for supporting his doctoral stays at Departamento de Lenguajes y Ciencias de la Computación of Universidad de Málaga (Spain).

References

  1. 1.
    Abbasi, M., Paquete, L., Pereira, F.: Local search for multiobjective multiple sequence alignment. In: Ortuño, F., Rojas, I. (eds.) Bioinformatics and Biomedical Engineering, Lecture Notes in Computer Science, vol. 9044, pp. 175–182. Springer, NewYork (2015)Google Scholar
  2. 2.
    Bacon, D.J., Anderson, W.F.: Multiple sequence alignment. J. Mol. Biol. 191(2), 153–161 (1986)CrossRefGoogle Scholar
  3. 3.
    Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., Bourne, P.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)CrossRefGoogle Scholar
  4. 4.
    da Silva, F.J.M., Pérez, J.M.S., Pulido, J.A.G., Rodríguez, M.A.V.: Parallel niche pareto alineaga—an evolutionary multiobjective approach on multiple sequence alignment. J. Integr. Bioinf. 8(3), 174 (2011)Google Scholar
  5. 5.
    Dayhoff, M., Schwartz, R., B.C. Orcutt, B.: A model of evolutionary change in proteins. In: Atlas of Protein Sequences and Structure 5, 345–352 (1978)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)CrossRefGoogle Scholar
  8. 8.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of non-parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)CrossRefGoogle Scholar
  9. 9.
    Durillo, J.J., Nebro, A.J.: jMetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)CrossRefGoogle Scholar
  10. 10.
    Edgar, R.: Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32(5), 1792–1797 (2004)CrossRefGoogle Scholar
  11. 11.
    Handl, J., Kell, D., Knowles, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(2), 279–292 (2007)CrossRefGoogle Scholar
  12. 12.
    Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)CrossRefGoogle Scholar
  13. 13.
    Kaya, M., Sarhan, A., Abdullah, R.: Multiple sequence alignment with affine gap by using multi-objective genetic algorithm. Comput. Methods Progr. Biomed. 114(1), 38–49 (2014)CrossRefGoogle Scholar
  14. 14.
    Kemena, C., Taly, J., Kleinjung, J., Notredame, C.: Strike: evaluation of protein msas using a single 3d structure. Bioinformatics 27(24), 3385–3391 (2011)CrossRefGoogle Scholar
  15. 15.
    Kukkonen, S., Deb, K.: Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In: IEEE International Conference on Evolutionary Computation, CEC 2006, part of WCCI 2006, Vancouver, BC, Canada, 16–21 July 2006, pp. 1179–1186 (2006)Google Scholar
  16. 16.
    Lassmann, T., Sonnhammer, E.L.: Kalign—an accurate and fast multiple sequence alignment algorithm. BMC Bioinf. 6(1), 1–9 (2005)CrossRefGoogle Scholar
  17. 17.
    Nebro, A., Durillo, J.J., Vergne, M.: Redesigning the jMetal multi-objective optimization framework. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO Companion ’15, pp. 1093–1100. ACM, New York, NY (2015)Google Scholar
  18. 18.
    Nebro, A., Durillo, J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: a cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst. 24(7), 723–725 (2009)CrossRefzbMATHGoogle Scholar
  19. 19.
    Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: a cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst. 24(7), 723–725 (2009)CrossRefzbMATHGoogle Scholar
  20. 20.
    Ortuño, F., Valenzuela, O., Rojas, F., Pomares, H., Florido, J., Urquiza, J., Rojas, I.: Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns. Bioinformatics (Oxford, England) 29(17), 2112–2121 (2013)CrossRefGoogle Scholar
  21. 21.
    Rani, R.R., Ramyachitra, D.: Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm. Biosystems 150, 177–189 (2016)CrossRefGoogle Scholar
  22. 22.
    Rubio-Largo, A., Vega-Rodriguez, M., Gonzalez-Alvarez, D.: A hybrid multiobjective memetic metaheuristic for multiple sequence alignment. IEEE Trans. Evol. Comput. 99, 1–16 (2015)Google Scholar
  23. 23.
    Rubio-Largo, A., Vega-Rodríguez, M., González-Álvarez, D.: Hybrid multiobjective artificial bee colony for multiple sequence alignment. Appl. Soft Comput. 41, 157–168 (2016)CrossRefGoogle Scholar
  24. 24.
    Saborido, R., Ruiz, A.B., Luque, M.: Global WASF-GA: an evolutionary algorithm in multiobjective optimization to approximate the whole pareto optimal front. Evol. Comput. (2016) (In Press)Google Scholar
  25. 25.
    Seeluangsawat, P., Chongstitvatana, P.: A multiple objective evolutionary algorithm for multiple sequence alignment. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation. GECCO ’05, pp. 477–478. ACM, New York, NY (2005)Google Scholar
  26. 26.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, Boca Raton (2007)zbMATHGoogle Scholar
  27. 27.
    Soto, W., Becerra, D.: A multi-objective evolutionary algorithm for improving multiple sequence alignments. In: Campos. S. (ed.) Advances in Bioinformatics and Computational Biology. Lecture Notes in Computer Science, vol. 8826, pp. 73–82. Springer, NewYork (2014)Google Scholar
  28. 28.
    Thompson, J., Koehl, P., Poch, O.: Balibase 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61, 127–136 (2005)CrossRefGoogle Scholar
  29. 29.
    Van Walle, I., Lasters, I., Wyns, L.: Sabmarka benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21(7), 1267–1268 (2005)CrossRefGoogle Scholar
  30. 30.
    Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1(4), 337–348 (1994)CrossRefGoogle Scholar
  31. 31.
    Zhu, H., He, Z., Jia, Y.: A novel approach to multiple sequence alignment using multiobjective evolutionary algorithm based on decomposition. IEEE J. Biomed. Health Inf. 20(2), 717–727 (2016)CrossRefGoogle Scholar
  32. 32.
    Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)CrossRefGoogle Scholar
  33. 33.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Cristian Zambrano-Vega
    • 1
  • Antonio J. Nebro
    • 2
    Email author
  • José García-Nieto
    • 2
  • José F. Aldana-Montes
    • 2
  1. 1.Ingeniería en SistemasUniversidad Técnica Estatal de QuevedoQuevedoEcuador
  2. 2.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain

Personalised recommendations