The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
The Maximum Weight Trace Problem in Multiple Sequence Alignment
CPM '93 Proceedings of the 4th Annual Symposium on Combinatorial Pattern Matching
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Applied Soft Computing
Chaos-differential evolution for multiple sequence alignment
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A parallel algorithm for multiple biological sequence alignment
IPCAT'12 Proceedings of the 9th international conference on Information Processing in Cells and Tissues
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Multiple sequence alignment is a basic tool in computational genomics. The art of multiple sequence alignment is about placing gaps. This paper presents a heuristic algorithm that improves multiple protein sequences alignment iteratively. A consistency-based objective function is used to evaluate the candidate moves. During the iterative optimization, well-aligned regions can be detected and kept intact. Columns of gaps will be inserted to assist the algorithm to escape from local optimal alignments. The algorithm has been evaluated using the BAliBASE benchmark alignment database. Results show that the performance of the algorithm does not depend on initial or seed alignments much. Given a perfect consistency library, the algorithm is able to produce alignments that are close to the global optimum. We demonstrate that the algorithm is able to refine alignments produced by other software, including ClustalW, SAGA and T-COFFEE. The program is available upon request.