Parallelization of Multiple Genome Alignment (original) (raw)
Abstract
In this work, we implement a genome alignment system which applies parallelization schemes to the ClustalW algorithm and the interface of database querying. Parallel construction of the distance matrices and parallelization of progressive alignment in the ClustalW algorithm are performed on PC-based Linux cluster with message-passing interface libraries. Achieved experiments show good speedup and significant parallel performance.
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References
- Vingron, M., Argos, P.: A fast and sensitive multiple sequence alignment algorithm. Comput. Appl. Biosci. 5, 115–121 (1989)
Google Scholar - Gupta, S.K., Kececioglu, J.D., Schäffer, A.A.: Improving the Practical Time and Space Efficiency of the Shortest-Paths Approach to Sum-of-Pairs Multiple Sequence Alignment. J. Comput. Bio. 2, 459–472 (1995)
Article Google Scholar - Zhang, C., Wong, A.K.C.: Toward efficient multiple molecular sequence alignment: a system of genetic algorithm and dynamic programming. IEEE Trans. Systems, Man and Cybernetics, Part B. 27, 918–932 (1997)
Article Google Scholar - Luo, J., Ahmad, I., Ahmed, M., Paul, R.: Parallel Multiple Sequence Alignment with Dynamic Scheduling. In: IEEE Int. Conf. Info. Tech.: Coding and Computing, vol. 1, pp. 8–13 (2005)
Google Scholar - Higgins, D.G., Sharp, P.M.: CLUSTAL: a Package for Performing Multiple Sequence Alignment in a Microcomputer. Gene. 73, 237–244 (1988)
Article Google Scholar - Kleinjung, J., Douglas, N., Herringa, J.: Parallelized multiple alignment. Bioinformatics 18, 1270–1271 (2002)
Article Google Scholar - Li, K.-B.: ClustalW-MPI: ClustalW analysis using distributed and parallel computing. Bioinformatics 19, 1585–1586 (2003)
Article Google Scholar - Li, Y., Sze, S.M., Chao, T.-S.: A Practical Implementation of Parallel Dynamic Load Balancing for Adaptive Computing in VLSI Device Simulation. Engineering with Computers 18, 124–137 (2002)
Article Google Scholar - Feng, D.F., Doolittle, R.F.: Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol. 25, 351–360 (1987)
Article Google Scholar - Saitou, N., Nei, M.: The neighbor-joining method: a new method for reconstructing phylogenetic trees. J. Mol. Evol. 4, 406–425 (1987)
Google Scholar - National Center for Biotechnology Information. [Online]. Available: http://www.ncbi.nlm.nih.gov/
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Authors and Affiliations
- Department of Communication Engineering,
Yiming Li - Microelectronics and Information Systems Research Center,
Yiming Li & Cheng-Kai Chen - Department of Computer and Information Science, National Chiao Tung University, Hsinchu City, Hsinchu, 300, Taiwan
Cheng-Kai Chen
Authors
- Yiming Li
- Cheng-Kai Chen
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Editors and Affiliations
- Department of Computer Science, St. Francis Xavier University, Antigonish, Canada
Laurence T. Yang - School of Computer Science/Welsh eScience Centre, Cardiff University, UK
Omer F. Rana - Dipartimento di Ingegneria dell’ Informazione - Second, University of Naples - Italy, Real Casa dell’Annunziata - via Roma, 29 81031, Aversa (CE), Italy
Beniamino Di Martino - Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA
Jack Dongarra
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, Y., Chen, CK. (2005). Parallelization of Multiple Genome Alignment. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds) High Performance Computing and Communications. HPCC 2005. Lecture Notes in Computer Science, vol 3726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557654\_102
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- DOI: https://doi.org/10.1007/11557654\_102
- Publisher Name: Springer, Berlin, Heidelberg
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