Partitioned optimization algorithms for multiple sequence alignment

Authors:
Yixin Chen;Yi Pan;Juan Chen;Wei Liu;Ling Chen
Affiliations:
Washington University in St. Louis, St. Louis, MO;Georgia State University, Atlanta, GA;Yangzhou University, Yangzhou 225009, China;Yangzhou University, Yangzhou 225009, China;Yangzhou University, Yangzhou 225009, China
Venue:
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Year:
2006

Citing 0
Cited 3

An Ant-Based Model for Multiple Sequence Alignment

Large-Scale Scientific Computing
Multiple sequence alignment by ant colony optimization and divide-and-conquer

ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Review article: Computational intelligence techniques in bioinformatics

Computational Biology and Chemistry

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Abstract

Multiple sequence alignment is an important and difficult problem in molecular biology and bioinformatics. In this paper, we propose a partitioning approach that significantly improves the solution time and quality by utilizing the locality structure of the problem. The algorithm solves the multiple sequence alignment in three stages. First, an automated and suboptimal partitioning strategy is used to divide the set of sequences into several subsections. Then a multiple sequence alignment algorithm based on ant colony optimization is used to align the sequences of each subsection. Finally, the alignment of original sequences can be obtained by assembling the result of each subsection. The ant colony algorithm is highly optimized in order to avoid local optimal traps and converge to global optima efficiently. Experimental results show that the algorithm can significantly reduce the running time and improve the solution quality on large-scale multiple sequence alignment benchmarks.