Volume- 3
Issue- 1
Year- 2015
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Komal B. Deshmukh , M. U. Kharat
Bioinformatics is one of the interdisciplinary research area. In various genome projects, huge biological sequences are available. Biological sequence analysis is fundamental operation in Bioinformatics and the goal is to find the similarity region based on comparing method is called as sequence alignment. In sequences alignment two methods are reported that are local alignment and global alignment. Smith-Waterman (SW) algorithm is providing optimal local alignment which has quadratic time and space complexity. In several real time applications huge sequences are aligned. In ordered to increase the efficiency, biological sequences are aligned over Graphical Processing Unit (GPU). Various systems applies parallel algorithm with use of GPU to accelerate application. CUDA Align algorithm is used to obtain full optimal alignment of biological sequences and the idea behind this is to obtain coordinate point of optimal alignment, iteratively perform same process to increase number of coordin
[1] Edans Flavius de O. Sandes and Alba Cristina M.A. de Melo, Senior Member, IEEE, “Retrieving Smith-Waterman Alignments with Optimizations for Megabase Biological Sequences Using GPU,” IEEE transactions on parallel and distributed systems, vol. 24, no. 5, may 2013.
[2] T.F. Smith and M.S. Waterman, “Identification of Common Molecular Subsequences,” J. Molecular Biology, vol. 147, no. 1, pp. 195-197, Mar. 1981
[3] O. Gotoh, “An Improved Algorithm for Matching Biological Sequences,” J. Molecular Biology, vol. 162, no. 3, pp. 705-708, Dec. 1982.
[4] D.S. Hirschberg, “A Linear Space Algorithm for Computing Maximal Common Subsequences,” Comm. ACM, vol. 18, no. 6, pp. 341-343, 1975.
[5] E.W. Myers and W. Miller, “Optimal Alignments in Linear Space,” Computer Applications in the Biosciences, vol. 4, no. 1, pp. 11-17, 1988.
[6] S. Aluru, N. Futamura, and K. Mehrotra, “Parallel Biological Sequence Comparison Using Prefix Computations,” J. Parallel Distributed Computing, vol. 63, no. 3, pp. 264-272, 2003.
[7] A. Driga, P. Lu, J. Schaeffer, D. Szafron, K. Charter, and I. Parsons, “FastLSA: A Fast, Linear-Space, Parallel and Sequential Algorithm for Sequence Alignment,” Algorithmica, vol. 45, no. 3, pp. 337-375, 2006.
[8] Y. Liu, W. Huang, J. Johnson, and S. Vaidya, “GPU Accelerated Smith-Waterman,” Proc. Sixth Int’l Conf. Computational Science (ICCS), vol. 3994, pp. 188-195, 2006.
[9] W. Liu, B. Schmidt, G. Voss, A. Schroder, and W. Muller-Wittig, “Bio-Sequence Database Scanning on a GPU,” Proc. 20th Int’l Conf. Parallel and Distributed Processing (IPDPS), 2006.
[10] S. Manavski and G. Valle, “CUDA Compatible GPU Cards as Efficient Hardware Accelerators for Smith-Waterman Sequence Alignment,” BMC Bioinformatics, 9(Suppl 2), 2008.
[11] Y. Liu, D. Maskell, and B. Schmidt, “CUDASW++: Optimizing Smith-Waterman Sequence Database Searches for CUDA-Enabled Graphics Processing Units,” BMC Research Notes, vol. 2, no. 1, p. 73, 2009.
[12] E.F. de, O. Sandes, and A.C.M.A. de Melo, “CUDAlign: Using GPU to Accelerate the Comparison of Megabase Genomic Sequences,” Proc. 15th ACM SIGPLAN Symp. Principles and Practice of Parallel Programming (PPoPP), pp. 137-146, 2010.
Computer Department, MET BKC, Savitribai Phule Pune University, Nasik , India.
No. of Downloads: 5 | No. of Views: 1050