Parallel divide and conquer bio-sequence comparison based on smith-waterman algorithm
- 51 Downloads
Tools for pair-wise bio-sequence alignment have for long played a central role in computation biology. Several algorithms for bio-sequence alignment have been developed. The Smith-Waterman algorithm, based on dynamic programming, is considered the most fundamental alignment algorithm in bioinformatics. However the existing parallel Smith-Waterman algorithm needs large memory space, and this disadvantage limits the size of a sequence to be handled. As the data of biological sequences expand rapidly, the memory requirement of the existing parallel Smith-Waterman algorithm has become a critical problem. For solving this problem, we develop a new parallel bio-sequence alignment algorithm, using the strategy of divide and conquer, named PSW-DC algorithm. In our algorithm, first, we partition the query sequence into several subsequences and distribute them to every processor respectively, then compare each subsequence with the whole subject sequence in parallel, using the Smith-Waterman algorithm, and get an interim result, finally obtain the optimal alignment between the query sequence and subject sequence, through the special combination and extension method. Memory space required in our algorithm is reduced significantly in comparison with existing ones. We also develop a key technique of combination and extension, named the C&E method, to manipulate the interim results and obtain the final sequences alignment. We implement the new parallel bio-sequences alignment algorithm, the PSW-DC, in a cluster parallel system.
Keywordsbiological sequence alignment dynamic programming divide and conquer parallel
Unable to display preview. Download preview PDF.
- 4.Altschul, S. F., Gish, W., Miller, W. et al., Basic local alignment search tool, Journal of Molecular Biology, 1990, 215: 403–410.Google Scholar
- 6.Phil Green. http://bozeman.bvt.washington.edu/phrap/phrap.docs/phrap.html. 1996.Google Scholar
- 8.Lander, E., Protein sequence comparison on a data parallel computer, in Proceedings of the 1988 International Conference on Parallel Processing, 1988, 257–263.Google Scholar
- 9.Galper, A. R., Brutlag, D. L., Parallel similarity search and alignment with the dynamic programming method, Technical Report, California: Stanford University, 1990.Google Scholar
- 10.Qiao Xiangzhen, Li Zhao, Zhu Mingfa, Parallel computation for dynamic programming, 2nd Int. ICSC Symposium on Computational Intelligence Methods & Applications (CIMA), Bangor, Wales, UK, 2001.Google Scholar
- 12.Mount, D. W., Bioinformatics: Sequence and Genome Analysis, New York: Cold Spring Harbor Laboratory Press, 2001.Google Scholar