Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

[1]  Juan Frausto Solís,et al.  MultiQuenching Annealing Algorithm for Protein Folding Problem , 2009, MICAI.

[2]  Shyi-Ming Chen,et al.  Multiple DNA sequence alignment based on genetic simulated annealing techniques , 2007 .

[3]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[4]  Juan Frausto-Solís,et al.  A Method to Establish the Cooling Scheme in Simulated Annealing Like Algorithms , 2004 .

[5]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[6]  Moon-Jung Chung,et al.  Multiple sequence alignment using simulated annealing , 1994, Comput. Appl. Biosci..

[7]  Juan Frausto Solís,et al.  ANDYMARK: An Analytical Method to Establish Dynamically the Length of the Markov Chain in Simulated Annealing for the Satisfiability Problem , 2006, SEAL.

[8]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[9]  Tao Jiang,et al.  On the Complexity of Multiple Sequence Alignment , 1994, J. Comput. Biol..

[10]  S. Dreyfus,et al.  Thermodynamical Approach to the Traveling Salesman Problem : An Efficient Simulation Algorithm , 2004 .

[11]  João Meidanis,et al.  Introduction to computational molecular biology , 1997 .

[12]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[13]  Juan Frausto Solís,et al.  Analytically Tuned Simulated Annealing Applied to the Protein Folding Problem , 2007, International Conference on Computational Science.

[14]  Robert Langridge,et al.  Mapping and interpreting biological information , 1991, CACM.

[15]  I. O. Bucak,et al.  An analysis of sequence alignment: Heuristic algorithms , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  D. Higgins,et al.  T-Coffee: A novel method for fast and accurate multiple sequence alignment. , 2000, Journal of molecular biology.

[17]  O. Gotoh.  An improved algorithm for matching biological sequences. , 1982, Journal of molecular biology.

[18]  Juan Frausto Solís,et al.  A Method to Establish the Cooling Scheme in Simulated Annealing Like Algorithms , 2004, ICCSA.

[19]  Uffe Kjærulff.  Optimal decomposition of probabilistic networks by simulated annealing , 1992 .

[20]  Ling Chen,et al.  An Efficient Ant Colony Algorithm for Multiple Sequences Alignment , 2007, Third International Conference on Natural Computation (ICNC 2007).

[21]  C. McDiarmid.  SIMULATED ANNEALING AND BOLTZMANN MACHINES A Stochastic Approach to Combinatorial Optimization and Neural Computing , 1991 .

[22]  Richard M. Karp,et al.  Mapping the genome: some combinatorial problems arising in molecular biology , 1993, STOC.

[23]  Lester Ingber,et al.  Simulated annealing: Practice versus theory , 1993 .