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Protein database search of hybrid alignment algorithm based on GPU parallel acceleration

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Abstract

In biological research, alignment of protein sequences by computer is often needed to find similarities between them. Although results can be computed in a reasonable time for alignment of two sequences, it is still very central processing unit (CPU) time-consuming when solving massive sequences alignment problems such as protein database search. In this paper, an optimized protein database search method is presented and tested with Swiss-Prot database on graphic processing unit (GPU) devices, and further, the power of CPU multi-threaded computing is also involved to realize a GPU-based heterogeneous parallelism. In our proposed method, a hybrid alignment approach is implemented by combining Smith–Waterman local alignment algorithm with Needleman–Wunsch global alignment algorithm, and parallel database search is realized with compute unified device architecture (CUDA) parallel computing framework. In the experiment, the algorithm is tested on a lower-end and a higher-end personal computers equipped with GeForce GTX 750 Ti and GeForce GTX 1070 graphics cards, respectively. The results show that the parallel method proposed in this paper can achieve a speedup up to 138.86 times over the serial counterpart, improving efficiency and convenience of protein database search significantly.

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References

  1. Mount DW (2004) Bioinformatics: sequence and genome analysis. Cold Spring Harbor Laboratory Press, Cold Spring Harbor

    Google Scholar 

  2. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol. Biol 48(3):443–453

    Article  Google Scholar 

  3. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147(1):195–197

    Article  Google Scholar 

  4. Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  Google Scholar 

  5. Pearson WR (1990) Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol 183:63–98

    Article  Google Scholar 

  6. Keckler SW, Dally WJ, Khailany B et al (2011) GPUs and the future of parallel computing. IEEE Micro 31(5):7–17

    Article  Google Scholar 

  7. Nickolls J, Dally WJ (2010) The GPU computing era. IEEE Micro 30(2):56–69

    Article  Google Scholar 

  8. Nvidia (2016) Geforce GTX 1070. Nvidia. http://www.geforce.com/hardware/10series/geforce-gtx-1070

  9. Nvidia (2014) CUDA C programming guide v6.5. Nvidia. http://docs.nvidia.com/cuda/pdf/CUDA_C_Programming_Guide

  10. AMD (2013) AMD accelerated parallel processing OpenCL programming guide. AMD. http://developer.amd.com/wordpress/media/2013/07/AMD_Accelerated_Parallel_Processing_OpenCL_Programming_Guide-rev-2.7

  11. Nickolls J, Buck I, Garland M et al (2008) Scalable parallel programming with CUDA. Queue 6(2):40–53

    Article  Google Scholar 

  12. Eddy SR (2004) Where did the BLOSUM62 alignment score matrix come from? Nat Biotechnol 22(8):1035–1036

    Article  Google Scholar 

  13. Yong-xian Wang, Zheng-hua Wang (2011) Introduction to bioinformatics. Tsinghua University Press, Beijing

    Google Scholar 

  14. Gotoh O (1982) An improved algorithm for matching biological sequences. J Mol Biol 162(3):705–708

    Article  Google Scholar 

  15. Polyanovsky VO, Roytberg MA, Tumanyan VG (2011) Comparative analysis of the quality of a global algorithm and a local algorithm for alignment of two sequences. Algorithms Mol Biol 6(1):1–12

    Article  Google Scholar 

  16. Liu Y, Huang W, Johnson J et al (2006) Gpu accelerated smith-waterman. In: Alexandrov VN, van Albada GD, Sloot PMA, Dongarra J (eds) Computational ScienceCICCS 2006. Springer, Berlin, pp 188–195

  17. Manavski SA, Valle G (2008) CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC Bioinf 9(2):1

    Google Scholar 

  18. Khajeh-Saeed A, Poole S, Perot JB (2010) Acceleration of the Smith–Waterman algorithm using single and multiple graphics processors. J Comput Phys 229(11):4247–4258

    Article  MathSciNet  MATH  Google Scholar 

  19. Blazewicz J, Frohmberg W, Kierzynka M et al (2011) Protein alignment algorithms with an efficient backtracking routine on multiple GPUs. BMC Bioinf 12(1):1

    Article  Google Scholar 

  20. Siriwardena TRP, Ranasinghe DN (2010) Accelerating global sequence alignment using CUDA compatible multi-core GPU. In: 2010 Fifth International Conference on Information and Automation for Sustainability, vol 2010, pp 201–206

  21. Kirk DB, Wen-mei WH (2009) Programming massively parallel processors: a hands-on approach. Morgan Kaufmann, Burlington

    Google Scholar 

  22. Cook S (2012) CUDA programming: a developer’s guide to parallel computing with GPUs. Morgan Kaufmann, Waltham

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the reviewers for their precious comments. This paper is supported by the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2015CL020).

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Authors and Affiliations

  1. Equipment Division, Weifang Medical University, Weifang, 261053, Shandong, China

    Wei Zhou

  2. School of Bioscience and Technology, Weifang Medical University, Weifang, 261053, Shandong, China

    Zhanxiu Cai & Bo Lian

  3. Computer Staff Room, Weifang Medical University, Weifang, 261053, Shandong, China

    Jincai Wang

  4. School of Clinical Medicine, Weifang Medical University, Weifang, 261053, Shandong, China

    Jianping Ma

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  1. Wei Zhou
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  2. Zhanxiu Cai
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Correspondence to Wei Zhou.

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Zhou, W., Cai, Z., Lian, B. et al. Protein database search of hybrid alignment algorithm based on GPU parallel acceleration. J Supercomput 73, 4517–4534 (2017). https://doi.org/10.1007/s11227-017-2030-x

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  • DOI: https://doi.org/10.1007/s11227-017-2030-x

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