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1 D IEEJ Transactions on Industry Applications Vol.136 No.10 pp DOI: /ieejias NW Accelerating Techniques for Sequence Alignment based on an Extended NW Algorithm Jin Okaze, Non-member, Chikatoshi Yamada, Member, Kei Miyagi, Non-member, Shuichi Ichikawa, Non-member The NW (Needleman-Wunsch) algorithm is a method of sequence alignment in bioinformatics. The NW algorithm can be applied for global sequence alignment, which is a way of arranging the sequences of DNA to identify regions of similarity. However, the NW algorithm requires a huge number of calculations compared with the SW (Smith- Waterman) algorithm. Many studies have focused on analyzing the output of multiple sequences quickly in three dimensions. However, such methods cannot obtain similarities for whole sequences. In this article, we extend the NW algorithm to three dimensions. The proposed method is expected to provide a fast analysis of high precision data sequences. Needleman-Wunsch Keywords: sequence alignmentneedleman-wunsch algorithm 1. 2 Fig. 1 FASTA BLASTBasic Local Alignment Search Tool (1) GPUGraphic Processing Unit GPGPU National Institute of Technology, Okinawa College 905, Henoko, Nago, Okinawa , Japan Toyohashi University of Technology 1-1, Hibarigaoka, Tempaku-cho, Toyohashi, Aichi , Japan Fig. 1. A sequence alignment. General-purpose computing on GPUGPGPU (2)(4) SWSmith- Waterman GPU (5) SW NWNeedleman-Wunsch GPU 3 2. NWNeedleman-Wunsch 21 2 DNA 1 c 2016 The Institute of Electrical Engineers of Japan. 686
2 NW NW Fig SPSum of Pair (1) (1) A B A B s(a, A) = match = 2 s(a, B) = mismatch = 1 (1) s(a, ) = s(, A) = gap = 2 n m X = x 1, x 2,...,x i Y = y 1,y 2,...,y j NW (2) SP 1 (3) NW O(mn) NW(0, 0) = 0 NW(i, 0) = gapi (2) NW(0, j) = gapj NW(i, j) NW(i 1, j 1) + s(x i,y j ) = max NW(i 1, j) + gap (3) NW(i, j 1) + gap 23 NW X = ABCD Y = ACD 2 NW (3) (i, j) = (0, 0) (i, j) = (4, 3) 1 Fig. 3 Fig. 4 (i, j) = (4, 3) (i, j) = (3, 2)(i, j) = (2, 1)(i, j) = (1, 1)(i, j) = (0, 0) (i, j) = (2, 1) (i, j) = (1, 1) X 1 Y Y X = ABCD Y = ACD Fig. 3. Results of score calculation. Fig. 2. Score table. Fig. 4. Traceback. 687 IEEJ Trans. IA, Vol.136, No.10, 2016
3 X = ABCD Y = A CD Score = 4 3. ClustalW (6) MAFFT (7) T-COFFEE (8) GPU CUDASW++2.0 (9) DB 4. NW 3 Fig. 5. Fig. 6. 3D score table. Location of gap NW 3 3 Fig NW 3 NW SP 2 NW(i, j, k) Fig. 6 Fig (4) (4) ABC A B C s(a, A, A) = match = 2 s(a, A, B) = s(a, B, C) = mismatch = 1 (4) s(a, A, ) = s(a,, ) = gap = 2 NW(0, 0, 0) = 0 NW(i, j, 0) = gap(i + j) (5) NW(0, j, k) = gap( j + k) NW(i, 0, k) = gap(k + i) NW(i, j, k) Fig. 7. Location of Match/Mismatch. NW(i 1, j 1, k 1) + s(x i,y j, z k ) NW(i 1, j 1, k) + gap NW(i 1, j, k 1) + gap = max NW(i, j 1, k 1) + gap (6) NW(i, j, k 1) + gap NW(i, j 1, k) + gap NW(i 1, j, k) + gap nml X = x 1, x 2,...,x i Y = y 1,y 2,...,y j Z = z 1, z 2,...,z k (5) Fig. 6 Fig. 7 (6) NW O(mnl) 42 X = BD Y = ABCD Z = ABD 3 3 NW (6) (i, j, k) = (0, 0, 0) (i, j, k) = (2, 4, 3) 1 Fig. 8 Fig. 8 (i, j, k) = (2, 4, 3)(i, j, k) = (2, 3, 3)(i, j, k) = (1, 2, 2) (i, j, k) = (1, 1, 1)(i, j, k) = (0, 0, 0) (i, j, k) = (2, 4, 3) (i, j, k) = (2, 3, 3) X Z (i, j, k) = (1, 2, 2) (i, j, k) = (1, 1, 1) 688 IEEJ Trans. IA, Vol.136, No.10, 2016
4 Fig. 8. Score table and traceback. X X = BD Y = ABCD Z = ABD X = B D Y = ABCD Z = AB D Score= 2 5. GPU Fig. 9. Configuration of GPU architecture. NW GPU 51 GPGPU 1 GPUGraphics Processing Unit 1 CPU CPU GPGPU GPU 52 CUDA CUDA GPGPU NVIDIA C CPU GPU Fig. 9 CUDA 16 KB Fig. 10. Dependencies of elements. 4 (10) (11) 53 CUDA NW CUDA 3 NW(i, j, k) Fig. 10 Fig. 10 Fig. 11 STEP 54 CPU GPU Table IEEJ Trans. IA, Vol.136, No.10, 2016
5 Fig. 13. Execution time of CPU/GPU(32-96). Fig. 11. Table 1. OS CPU GPU Memory bandwidth floating-point performance (single-precision) Processing flow. floating-point performance (double-precision) developmental environment Fig. 12. Environment. Windows7 Enterprise 64bit Intel Core i7-4770k 3.50 GHz NVIIA GeForce GTX TITAN 288 [GB/s] 4.5TFLOPS 1.3TFLOPS CUDA7.5 Execution time of CPU/GPU. CPU GPU Fig. 12 Fig Fig. 13 Fig. 12 GPU CPU Fig. 13 CPU GPU NW GPU CPU 5.25 No NCBI: BLAST: Basic Local Alignment Search Tool, nih.gov/blast.cgi 2 CUDA GPU,, pp (2008) 3 GPU,, p.74 (2007) 4 GPU,, p.1 (2012) 5 GPU 3 Smith-Waterman,, pp (2011) 6 DNA Data Bank of Japan (DDBJ): ClustalW, jp/ 7 debian: Multiple alignment program for amino acid or nucleotide sequences, 8 Swiss Institute of Bioinformatics (SIB): T-Coffee, 9 Y. Liu, B. Schmidt, and D.L. Maskell: CUDASW++2.0: enhanced Smith- Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions, BMC Research Notes, pp.1 12 (2010) 10 J. Sanders + E. Kandrot, CUDA BY EXAMPLE An Introduction to General-Purpose GPU Programming,, pp.6 9 (2011) 11 CUDA GPU,, pp (2010) IEEJ Trans. IA, Vol.136, No.10, 2016
6 IEEE VLSI ERATO 1991 LSI LSI IEEEsenior member ACM 691 IEEJ Trans. IA, Vol.136, No.10, 2016
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website by the author(s) under the agreement with the IPSJ.
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THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE.,, 464 8601 470 0393 101 464 8601 E-mail: matsunagah@murase.m.is.nagoya-u.ac.jp, {ide,murase,hirayama}@is.nagoya-u.ac.jp,
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