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A novel two-level particle swarm optimization approach for efficient multiple sequence alignment

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Abstract

This paper presents two-level particle swarm optimization (TL-PSO) algorithm as an effective framework for providing the solution of complex natured problems. Proposed approach is employed to solve a challenging problem of bioinformatics i.e. multiple sequence alignment (MSA) of proteins. The major challenge in MSA is the increasing complexity of the problem as soon as the number of sequences increases and average pairwise sequence identity (APSI) score decreases. Proposed TLPSO-MSA firstly maximizes the matched columns in level one followed by maximization of pairwise similarities in level two at the gbest solutions of level one. TLPSO-MSA efficiently handles the premature convergence and trapping in local optima related issues. The benchmark dataset for MSA of protein sequences are extracted from BAliBASE3.0. The special features of proposed algorithm is its prediction accuracy at very lower APSI scores. Proposed approach significantly outperforms the compared state-of-art competitive algorithms i.e. ALIGNER, MUSCLE, T-Coffee, MAFFT, ClustalW, DIALIGN-TX, ProbAlign and standard PSO algorithm. The claim is supported by the statistical significance testing using one way ANOVA followed by Bonferroni post-hoc analysis.

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References

  1. Blum C, Li X (2008) Swarm intelligence in optimization. In: Blum C et al (eds) Swarm intelligence: introduction and applications. Springer, Berlin, Heidelberg, pp 43–85

    Chapter  Google Scholar 

  2. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948

  3. Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7:109–124

    Article  MATH  MathSciNet  Google Scholar 

  4. Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Nat Comput 6:467–484

    Article  MATH  MathSciNet  Google Scholar 

  5. Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl, 2008 Art. ID 685175. doi:10.1155/2008/685175

  6. Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13:2997–3006

    Article  Google Scholar 

  7. Esmin, AAA, Coelho RA, Matwin S (2013) A review on particle swarm optimization algorithm and its variants to clustering highdimensional data. Artif Intell Rev 1–23. doi:10.1007/s10462-013-9400-4

  8. Sedighizadeh D, Masehian E (2009) An particle swarm optimization method, taxonomy and applications. Int J Comput Theory Eng 5:486–502

    Article  Google Scholar 

  9. Das S, Abraham A, Konar A (2008) Swarm intelligence algorithms in bioinformatics. In: Kelemen A et al (eds) Swarm intelligence algorithms in bioinformatics, vol 94. Springer, Berlin, Heidelberg, pp 113–147

    Google Scholar 

  10. Bucak IO, Uslan V (2010) An analysis of sequence alignment: heuristic algorithms. In: 32nd Annual international conference of the IEEE EMBS, Argentina, pp 1824–1827

  11. Notredame C (2002) Recent progress in multiple sequence alignment: a survey. Pharmacogenomics 3(1):131–144

    Article  Google Scholar 

  12. Carillo H, Lipman D (1988) The multiple sequence alignment problem in biology. Soc Ind Appl Math 48:1073–1082

    Article  Google Scholar 

  13. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specic gap penalties and weight matrix choice. Nucl Acids Res 22:4673–4680

    Article  Google Scholar 

  14. Mandoiu I, Zelikovsky A (2008) Bioinformatics algorithms: techniques and applications. Wiley, Hoboken

    Book  Google Scholar 

  15. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarity in the amino acid sequences of two proteins. J Mol Biol 48:443–453

    Article  Google Scholar 

  16. Stoye J, Moulton V, Dress AW (1997) DCA: an efficient implementation of the divide-and-conquer approach to simultaneous multiple sequence alignment. Comput Appl Biosci 13(6):625–626

    Google Scholar 

  17. Notredame C, Higgins DG, Heringa J (2000) T-COFFEE: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1):205–217

    Article  Google Scholar 

  18. Morgenstern B (1999) DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment. Bioinformatics 15(3):211–218

    Article  Google Scholar 

  19. Subramanian AR, Menkhoff JW, Kaufmann M, Morgenstern B (2005) DIALIGN-T: an improved algorithm for segment-based multiple sequence alignment. Bioinformatics 6:66

    Google Scholar 

  20. Mount DW (2004) Bioinformatics sequence and genome analysis, 2nd edn. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

  21. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092

    Article  Google Scholar 

  22. Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  23. Kim J, Pramanik S, Chung MJ (1994) Multiple sequence alignment using simulated annealing. Comput Appl Biosci 10(4):419–426

    Google Scholar 

  24. Chen Y, Pan Y, Chen J, Liu W, Chen L (2006) Multiple sequence alignment by ant colony optimization and divide-and-conquer. In: Computational science-ICCS 2006, 3992, Springer, pp 646–653

  25. Bucak IO, Uslan V (2011) Sequence alignment from the perspective of stochastic optimization: a survey. Turk J Electr Eng 19(1):157–173

    Google Scholar 

  26. Heringa J (1999) Two strategies for sequence comparison: profile-preprocessed and secondary structure-induced multiple alignment. Comput Chem 23:341–364

    Article  Google Scholar 

  27. Brocchieri L, Karlin S (1998) Asymetric-iterated multiple alignment of protein sequences. J Mol Biol 276:249–264

    Article  Google Scholar 

  28. Lalwani S, Kumar R, Gupta N (2013) A review on particle swarm optimization variants and their applications to multiple sequence alignment. J Appl Math Bioinform 3(2):87–124

    Google Scholar 

  29. Glover FW, Kochenberger GA (2003) Handbook of metaheuristics. International series in operations research and management science. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  30. Trianni V, Nolfi S, Dorigo M (2008) Evolution, self-organization and swarm robotics. In: Blum C et al (eds) Swarm intelligence: introduction and applications. Springer, Berlin, Heidelberg, pp 163–191

    Chapter  Google Scholar 

  31. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

  32. Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications, information science reference. Hershey, New York

    Book  Google Scholar 

  33. Sun J, Lai CH, Wu XJ (2012) Particle swarm optimisation: classical and quantum perspectives. CRC Press, Boca Raton

    Google Scholar 

  34. Lalwani S, Kumar R, Gupta N (2013) A study on inertia weight schemes with modified particle swarm optimization algorithm for multiple sequence alignment. In: 6th IEEE international conference on contemporary computing, Noida, India, pp 283–288

  35. Zablocki FBR (2007) Multiple sequence alignment using Particle swarm optimization. MS dissertation, University of Pretoria

  36. Toscano-Pulido G, Reyes-Medina AJ, Ramrez-Torres JG (2011) A statistical study of the effects of neighborhood topologies in particle swarm optimization, Computational Intelligence, SCI 343. Springer, Berlin, Heidelberg

    Google Scholar 

  37. Kennedy J, Mendes R (2002) Population structure and particle performance. In: Proceedings of the IEEE congress on evolutionary computation, Washington, DC, pp 1671–1676

  38. Setubal JC, Meidanis J (1997) Introduction to computational biology. Brooks/Cole, Pacific Grove

    Google Scholar 

  39. Chellapilla K, Fogel GB (1999) Multiple sequence alignment using evolutionary programming. In: Proceedings of the 1999 congress on evolutionary computation, Washington, DC, pp 445–452

  40. Thompson JD, Koehl P, Ripp R, Poch O (2005) BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61(1):127–136

    Article  Google Scholar 

  41. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. PNAS 89(92):10915–10919

    Article  Google Scholar 

  42. Thompson JD, Plewniak F, Poch O (1999) A comprehensive comparison of multiple sequence alignment programs. Nucl Acids Res 27(13):2682–2690

    Article  Google Scholar 

  43. Kelil A, Wang S, Brzezinski R, Fleury A (2007) CLUSS: clustering of protein sequences based on a new similarity measure. BMC Bioinform 8:286

    Article  Google Scholar 

  44. Edgar RC (2004) MUSCLE. A multiple sequence alignment method with reduced time and space complexity. BMC Bioinform 5:113

    Article  Google Scholar 

  45. Katoh K, Misawa K, Kuma K, Miyata T (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucl Acids Res 30:3059–3066

    Article  Google Scholar 

  46. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) ClustalW and ClustalX version 2. Bioinformatics 23(21):2947–2948

    Article  Google Scholar 

  47. Subramanian AR, Kaufmann M, Morgenstern B (2008) DIALIGN-TX: Greedy and progressive approaches for segment-based multiple sequence alignment. Algorithms Mol Biol 3(6)

  48. Roshan U, Libesay DR (2006) Probalign: multiple sequence alignment using partition function posterior probabilities. Bioinformatics 22(22):2715–2721

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Acknowledgments

The authors wish to thank the Executive Director, Birla Institute of Scientific Research for the support given during this work. We are thankful to Dr. Krishna Mohan for his valuable suggestions throughout the work. We gratefully acknowledge financial support by BTIS-sub DIC (supported by DBT, Govt. of India) to one of us (S. L.) and Advanced Bioinformatics Centre (supported by Govt. of Rajasthan) at Birla Institute of Scientific Research for infrastructure facilities for carrying out this work.

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Authors and Affiliations

  1. R & D, Advanced Bioinformatics Centre, Birla Institute of Scientific Research, Jaipur, India

    Soniya Lalwani

  2. Department of Mathematics, Malaviya National Institute of Technology, Jaipur, India

    Soniya Lalwani & Nilama Gupta

  3. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Rajesh Kumar

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  1. Soniya Lalwani
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  2. Rajesh Kumar
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  3. Nilama Gupta
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Correspondence to Rajesh Kumar.

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Lalwani, S., Kumar, R. & Gupta, N. A novel two-level particle swarm optimization approach for efficient multiple sequence alignment. Memetic Comp. 7, 119–133 (2015). https://doi.org/10.1007/s12293-015-0157-y

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