Skip to main content
Springer Nature Link
Log in

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Ao W, Gaudet J, Kent WJ, Muttumu S, Mango SE (2004) Environmentally induced foregut remodeling by PHA-4/FoxA and DAF-12/NHR. Science 305(5691):1743–1746

    Article  Google Scholar 

  2. Bailey TL, Elkan C (1995) Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Mach Learn 21(1–2):51–80

    Google Scholar 

  3. Che D, Song Y, Rashedd K (2005) MDGA: motif discovery using a genetic algorithm. In: Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO’05), pp 447–452

    Chapter  Google Scholar 

  4. Chen C, Schmidt B, Weiguo L, Müller-Wittig W (2008) GPU-MEME: using graphics hardware to accelerate motif finding in DNA sequences. In: Pattern recognition in bioinformatics. LNCS, vol 5265. Springer, Berlin, pp 448–459

    Chapter  Google Scholar 

  5. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  6. D’haeseleer P (2006) What are DNA sequence motifs? Nat Biotechnol 24(4):423–425

    Article  Google Scholar 

  7. Eskin E, Pevzner PA (2002) Finding composite regulatory patterns in DNA sequences. Bioinformatics 18(Suppl 1):S354–S363

    Article  Google Scholar 

  8. Favorov AV, Gelfand MS, Gerasimova AV, Ravcheev DA, Mironov AA, Makeev VJ (2005) A Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length. Bioinformatics 21(10):2240–2245

    Article  Google Scholar 

  9. Fogel GB, Porto VW, Varga G, Dow ER, Crave AM, Powers DM, Harlow HB, Su EW, Onyia JE, Su C (2008) Evolutionary computation for discovery of composite transcription factor binding sites. Nucleic Acids Res 36(21), e142: 1–14

    Article  Google Scholar 

  10. Fogel GB, Weekes DG, Varga G, Dow ER, Harlow HB, Onyia JE, Su C (2004) Discovery of sequence motifs related to coexpression of genes using evolutionary computation. Nucleic Acids Res 32(13):3826–3835

    Article  Google Scholar 

  11. Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the 5th international conference on genetic algorithms, San Francisco, CA, USA, pp 416–423

    Google Scholar 

  12. Frith MC, Hansen U, Spouge JL, Weng Z (2004) Finding functional sequence elements by multiple local alignment. Nucleic Acids Res 32(1):189–200

    Article  Google Scholar 

  13. González-Álvarez DL, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2010) Solving the motif discovery problem by using differential evolution with Pareto tournaments. In: IEEE congress on evolutionary computation (CEC’10), pp 4140–4147

    Google Scholar 

  14. González-Álvarez DL, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2011) Finding motifs in DNA sequences applying a multiobjective artificial bee colony (MOABC) algorithm. In: Evolutionary computation, machine learning and data mining in bioinformatics (EVOBIO’11). LNCS, vol 6623. Springer, Berlin, pp 89–100

    Chapter  Google Scholar 

  15. González-Álvarez DL, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2012) Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery. Eng Appl Artif Intell 26(1):326–341

    Google Scholar 

  16. González-Álvarez DL, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2012) A parallel multi-core team of multiobjective evolutionary algorithms to discover DNA motifs. In: 14th IEEE international conference on high performance computing and communications (HPCC’12), pp 17–24

    Google Scholar 

  17. González-Álvarez DL, Vega-Rodríguez MA, Gómez-Pulido JA, Sánchez-Pérez JM (2012) Predicting DNA motifs by using evolutionary multiobjective optimization. IEEE Trans Syst Man Cybern, Part C, Appl Rev 42(6):913–925

    Article  Google Scholar 

  18. Grundy W, Bailey T, Elkan C (1996) ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool. Comput Appl Biosci 12(4):303–310

    Google Scholar 

  19. van Helden J, Andre B, Collado-Vides J (1998) Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol 281(5):827–842

    Article  Google Scholar 

  20. van Helden J, Rios AF, Collado-Vides J (2000) Discovering regulatory elements in non-coding sequences by analysis of spaced dyads. Nucleic Acids Res 28(8):1808–1818

    Article  Google Scholar 

  21. Hertz GZ, Stormo GD (1999) Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 15(7–8):563–577

    Article  Google Scholar 

  22. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Turkey

  23. Kaya M (2009) MOGAMO: multi-objective genetic algorithm for motif discovery. Expert Syst Appl 36(2):1039–1047

    Article  Google Scholar 

  24. Liu FFM, Tsai JJP, Chen RM, Chen SN, Shih SH (2004) FMGA: finding motifs by genetic algorithm. In: Fourth IEEE symposium on bioinformatics and bioengineering (BIBE’04), pp 459–466

    Chapter  Google Scholar 

  25. Liu Y, Schmidt B, Liu W, Maskell D (2010) CUDA-MEME: accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units. Pattern Recognit Lett 31(14):2170–2177

    Article  Google Scholar 

  26. Liu Y, Schmidt B, Maskell D (2011) An ultrafast scalable many-core motif discovery algorithm for multiple GPUs. In: IEEE international symposium on parallel and distributed processing workshops and Ph.D. forum, pp 428–434

    Google Scholar 

  27. Lones MA, Tyrrell AM (2007) Regulatory motif discovery using a population clustering evolutionary algorithm. IEEE/ACM Trans Comput Biol Bioinform 4(3):403–414

    Article  Google Scholar 

  28. Maier D (1978) The complexity of some problems on subsequences and supersequences. J ACM 25(2):322–336

    Article  MathSciNet  MATH  Google Scholar 

  29. Mak T, Lam K (2004) Embedded computation of maximum-likelihood phylogeny inference using platform FPGA. In: IEEE computational systems bioinformatics conference, pp 512–514

    Google Scholar 

  30. Oliver T, Schmidt B, Nathan D, Clemens R, Maskell D (2005) Using reconfigurable hardware to accelerate multiple sequence alignment with ClustalW. Bioinformatics 21(16):3431–3432

    Article  Google Scholar 

  31. Pavesi G, Mauri G, Pesole G (2001) An algorithm for finding signals of unknown length in DNA sequences. Bioinformatics 17(Suppl 1):S207–S214

    Article  Google Scholar 

  32. Regnier M, Denise A (2004) Rare events and conditional events on random strings. Discrete Math Theor Comput Sci 6(2):191–214

    MathSciNet  MATH  Google Scholar 

  33. Roth FP, Hughes JD, Estep PW, Church GM (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 16(10):939–945

    Article  Google Scholar 

  34. Sandve G, Nedland M, Syrstad Ø, Eidsheim L, Abul O, Drabløs F (2006) Accelerating motif discovery: motif matching on parallel hardware. In: Algorithms in bioinformatics. LNCS, vol 4175. Springer, Berlin, pp 197–206

    Chapter  Google Scholar 

  35. Shao L, Chen Y (2009) Bacterial foraging optimization algorithm integrating tabu search for motif discovery. In: IEEE Iinternational conference on bioinformatics and biomedicine (BIBM’09), pp 415–418

    Chapter  Google Scholar 

  36. Shao L, Chen Y, Abraham A (2009) Motif discovery using evolutionary algorithms. In: International conference of soft computing and pattern recognition (SOCPAR’09), pp 420–425

    Chapter  Google Scholar 

  37. Sheskin DJ (2007) Handbook of parametric and nonparametric statistical procedures, 4th edn. Chapman & Hall/CRC Press, New York

    MATH  Google Scholar 

  38. Sinha S, Tompa M (2003) YMF: a program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 31(13):3586–3588

    Article  Google Scholar 

  39. Stine M, Dasgupta D, Mukatira S (2003) Motif discovery in upstream sequences of coordinately expressed genes. In: The 2003 congress on evolutionary computation (CEC’03), pp 1596–1603

    Chapter  Google Scholar 

  40. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  41. Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, New York

    Book  Google Scholar 

  42. Thijs G, Lescot M, Marchal K, Rombauts S, De Moor B, Rouzé P, Moreau Y (2001) A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 17(12):1113–1122

    Article  Google Scholar 

  43. Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ, Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Régnier M, Simonis N, Sinha S, Thijs G, Van Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 23(1):137–144

    Article  Google Scholar 

  44. Wingender E, Dietze P, Karas H, Knuppel R (1996) TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res 24(1):238–241

    Article  Google Scholar 

  45. Workman CT, Stormo GD (2000) ANN-Spec: a method for discovering transcription factor binding sites with improved specificity. In: Pacific symposium on biocomputing, pp 467–478

    Google Scholar 

  46. Yamaguchi Y, Miyajima Y, Maruyama T, Konagaya A (2002) High speed homology search using run-time reconfiguration. In: Field-programmable logic and applications: reconfigurable computing is going mainstream. LNCS, vol 2438. Springer, Berlin, pp 671–687

    Google Scholar 

  47. Yang XS (2009) Firefly algorithms for multimodal optimization. In: The 5th international symposium of stochastic algorithms: foundations and applications (SAGA’09). LNCS, vol 5792. Springer, Berlin, pp 169–178

    Chapter  Google Scholar 

  48. Zare-Mirakabad F, Ahrabian H, Sadeghi M, Hashemifar S, Nowzari-Dalini A, Goliaei B (2009) Genetic algorithm for dyad pattern finding in DNA sequences. Genes Genet Syst 84(1):81–93

    Article  Google Scholar 

  49. Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength pareto evolutionary algorithm. In: International conference on evolutionary and deterministic methods for design, optimization and control with applications (EUROGEN’02), pp 95–100

    Google Scholar 

  50. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 3(4):257–271

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the Contract TIN2012-30685 (BIO Project). Thanks also extend to the Fundación Valhondo for the economic support offered to David L. González-Álvarez.

Author information

Authors and Affiliations

  1. Department Technologies of Computers and Communications, ARCO Research Group, University of Extremadura, Escuela Politécnica, Campus Universitario s/n, 10003, Cáceres, Spain

    David L. González-Álvarez & Miguel A. Vega-Rodríguez

Authors
  1. David L. González-Álvarez
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Miguel A. Vega-Rodríguez
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to David L. González-Álvarez.

Rights and permissions

Reprints and permissions

About this article

Cite this article

González-Álvarez, D.L., Vega-Rodríguez, M.A. A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery. J Supercomput 66, 1576–1612 (2013). https://doi.org/10.1007/s11227-013-0951-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-013-0951-6

Keywords