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DCA based algorithms for multiple sequence alignment (MSA)

Author

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  • Hoai Le Thi
  • Tao Pham Dinh
  • Moulay Belghiti

Abstract

In the last years many techniques in bioinformatics have been developed for the central and complex problem of optimally aligning biological sequences. In this paper we propose a new optimization approach based on DC (Difference of Convex functions) programming and DC Algorithm (DCA) for the multiple sequence alignment in its equivalent binary linear program, called “Maximum Weight Trace” problem. This problem is beforehand recast as a polyhedral DC program with the help of exact penalty techniques in DC programming. Our customized DCA, requiring solution of a few linear programs, is original because it converges after finitely many iterations to a binary solution while it works in a continuous domain. To scale-up large-scale (MSA), a constraint generation technique is introduced in DCA. Preliminary computational experiments on benchmark data show the efficiency of the proposed algorithm DCAMSA, which generally outperforms some standard algorithms. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Hoai Le Thi & Tao Pham Dinh & Moulay Belghiti, 2014. "DCA based algorithms for multiple sequence alignment (MSA)," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 501-524, September.
    • Handle: RePEc:spr:cejnor:v:22:y:2014:i:3:p:501-524
    DOI: 10.1007/s10100-013-0324-5
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    References listed on IDEAS

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    1. Le An & Pham Tao, 2005. "The DC (Difference of Convex Functions) Programming and DCA Revisited with DC Models of Real World Nonconvex Optimization Problems," Annals of Operations Research, Springer, vol. 133(1), pages 23-46, January.
    2. Harvey J. Greenberg, 2007. "Integer Quadratic Programming Models in Computational Biology," Operations Research Proceedings, in: Karl-Heinz Waldmann & Ulrike M. Stocker (ed.), Operations Research Proceedings 2006, pages 83-95, Springer.
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