An Efficient Ant Colony Algorithm for DNA Motif Finding

  • Hoang X. Huan
  • Duong T. A. Tuyet
  • Doan T. T. Ha
  • Nguyen T. Hung
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 326)


Finding motifs in gene sequences is one of the most important problems of bioinformatics and belongs to NP-hard type. This paper proposes a new ant colony optimization algorithm based on consensus approach, in which a relax technique is applied to find the location of the motif. The efficiency of the algorithm is evaluated by comparing it with the state-of-the-art algorithms.


Ant Colony Optimization MEMETIC motif finding problem 


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  1. 1.
    Bandyopadhyay, S., Sahni, S., Rajasekaran, S.: Pms6: A faster algorithm for motif discovery. In: Proceedings of the second IEEE Int. Conf. on Computational Advances in Bio and Medical Sciences, pp. 1–6 (2012)Google Scholar
  2. 2.
    Bouamama, S., Boukerram, A., Al-Badarneh, A.F.: Motif finding using ant colony optimization. In: Dorigo, M., et al. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 464–471. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Chen, X.S., Ong, Y.S., Lim, M.H.: Research frontier: memetic computation - past, present & future. IEEE Computational Intelligence Magazine 5(2), 24–36 (2011)Google Scholar
  4. 4.
    Claeys, M., Storms, V., Sun, H., Michoel, T., Marchal, K.: MotifSuite: workflow for probabilistic motif detection and assessment. Bioinformatics 28(14), 1931–1932 (2012), doi:10.1093/bioinformatics/bts293Google Scholar
  5. 5.
    Dinh, H., Rajasekaran, S., Davila, J.: qPMS7: A fast algorithm for finding the (l; d)-motif in DNA and protein sequences. PLoS One 7(7), e41425 (2012)Google Scholar
  6. 6.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  7. 7.
    Eskin, E., Pevzner, P.: Finding composite regulatory patterns in DNA sequences. Bioinformatics S1, 354–363 (2002)CrossRefGoogle Scholar
  8. 8.
    Hoang Xuan, H., Do Duc, D., Manh Ha, N.: An efficient two-phase ant colony optimization algorithm for the closest string problem, pp. 188–197 (2012)Google Scholar
  9. 9.
    Xuan, H., NguyenLinh, T., Do Duc, D., Huu Tue, H.: Solving the traveling salesman problem with ant colony optimization: arevisit and new efficient algorithms. REV Journal on Electronics and Communications 2(3-4), 121–129 (2012)Google Scholar
  10. 10.
    Keith, M.K.: A simulated annealing algorithm for finding consensus sequences. J. Bioinformatics 18, 1494–1499 (2002)CrossRefGoogle Scholar
  11. 11.
    Liu, W., Chen, H., Chen, L.: An ant colony optimization based algorithm for identifying gene regulatory elements. Comp. in Bio. and Med. 43(7), 922–932 (2013)CrossRefGoogle Scholar
  12. 12.
    Lo, N.W., Changchien, S.W., Chang, Y.F., Lu, T.C.: Human promoter prediction based on sorted consensus sequence patterns by genetic algorithms. Proc. of the Int. Congress on Biological and Medical Engineering D3I-1540, 111–112 (2002)Google Scholar
  13. 13.
    Moscato, P.: Onevolution, search, optimization, genetic algorithms and martial arts: towards memeticalgorithms. Tech. Rep.Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)Google Scholar
  14. 14.
    Neuwald, A., Liu, J., Lawrence, C.: Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Science, 1618–1632 (1995)Google Scholar
  15. 15.
    Pisanti, N., Carvalho, A.M., Marsan, L., Sagot, M.-F.: RISOTTO: Fast extraction of motifs with mismatches. In: Correa, J.R., Hevia, A., Kiwi, M. (eds.) LATIN 2006. LNCS, vol. 3887, pp. 757–768. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Stormo, G.D., Hartzell, G.W.: Identifying protein-binding sites from unaligned DNA fragments. Proc. Natl. Acad. Sci. USA 86(4), 1183–1187 (1989)CrossRefGoogle Scholar
  17. 17.
    Stormo, G.D., Hartzell, G.W.: Identifying protein-binding sites from unaligned DNA fragments. Proc. Natl. Acad. Sci. USA 86(4), 1183–1187 (1989)CrossRefGoogle Scholar
  18. 18.
    Thompson, W., Eric, C.R., Lawrence, E.L.: Gibbsrecursive sampler: finding transcription factor binding sites. J. Nucleic Acids Research 31, 3580–3585 (2003)CrossRefGoogle Scholar
  19. 19.
    Yang, C.H., Liu, Y.T., Chuang, L.Y.: DNA motif discovery based on ant colony optimization and expectation maximization. In: Proc. of IMECS, pp. 169–174 (2011)Google Scholar
  20. 20.
  21. 21.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hoang X. Huan
    • 1
  • Duong T. A. Tuyet
    • 1
  • Doan T. T. Ha
    • 1
  • Nguyen T. Hung
    • 1
  1. 1.University of Engineering and TechnologyVNUHanoiVietnam

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