Implementation of Banded Smith-Waterman Sequence Alignment Algorithm on CPU and FPGA-Reference-Cited by-同舟云学术

Implementation of Banded Smith-Waterman Sequence Alignment Algorithm on CPU and FPGA

Published:2024-07-16 Issue: Volume: Page:
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Author:

Mukherji Prachi¹,Rajput Seema H.¹,Kendre Nandini¹,Mudaliar Vaishnavi¹

Affiliation:

1. Cummins College of Engineering for Women Pune

Abstract

Sequence alignment(SA) is a fundamental aspect in the field of bioinformatics, crucial for various applications such as DNA sequencing and protein structure prediction. It involves the process of comparing a new genome sequence with the sequences previously stored in a database. However, the computational demands of Smith-Waterman alignment can be substantial, particularly when analyzing large genomic datasets. To address this challenge, we present a comprehensive comparative study that explores the acceleration of Smith-Waterman sequence alignment using different hardware platforms: Central Processing Units (CPUs) and Field-Programmable Gate Arrays (FPGAs. In this study, we evaluate and contrast the performance and scalability of Smith-Waterman alignment on these platforms, considering CPU and FPGA-based implementation. We assess their computational capabilities and memory requirements for various sequence lengths and scoring parameters. Through extensive benchmarking and sequence analysis, particularly on heterogeneous CPU + FPGA platforms profiling, we provide insights into the advantages and limitations of each platform, shedding light on the trade-offs between computation speed and hardware cost.

Publisher

Springer Science and Business Media LLC

Reference7 articles.

1. Harris, B., Jacob, A. C., Lancaster, J. M., Buhler, J., Chamberlain, R. D., Smith-Waterman, A. B., Accelerator for Mercury, F. P. G. A., BLASTP, 2007 International Conference on Field Programmable Logic and Applications, Amsterdam, Netherlands, [2], S. A. M., Al Junid, Z. A., Majid, A. K., Halim, [3], L., Hasan, Z., Al-Ars, An Overview of Hardware-Based Acceleration of Biological Sequence Alignment, Computational Biology and Applied Bioinformatics, Prof. H. Lopes2011., [4], L., Hasan, Z., Al-Ars, An Overview of Hardware-Based Acceleration of Biological Sequence Alignment, Computational Biology and Applied Bioinformatics, Prof. H. Lopes2011. [5], Chen, P., Wang, C., Li, X., Zhou, X., Hardware acceleration for the banded Smith-Waterman algorithm with the cycled systolic array, 2013 International Conference on Field-Programmable Technology (FPT), Kyoto, Japan, 2013. [6] M. N., Isa, D. S., Nurdin, M. I., Ahmad, S. A. Z., Murad, S. N., Mohyar, M., Mahyiddin, A., Harun, R. C., Ismail, S. S., Jamuar, A., Bahari, X., Fei, Z., Dan, L., Lina, M., Xin, Z., Chunlei, P., Patil, P., Pattiwar, S., Khan, V., Panch, Speed-up of Sequence Alignment Algorithms on CUDA Compatible GPUs, International Journal of Research in Engineering, Science and Management, vol. 1, no. 12, December 2018. [9] Y.-L., Liao, Y. C., Li, N. C., Chen, Y. C., Lu, Adaptively Banded Smith-Waterman Algorithm for Long Reads and Its Hardware Accelerator, IEEE 29th International Conference on Application-specific Systems, Architectures and Processors, 2018. [10], Hakim, A., Kashtwari, A., Tiwari, R., Sharma, J., Performance Analysis of DNA Sequencing Using Smith-Waterman Algorithm on FPGA, Journal of VLSI Design ToolsTechnology, vol. 9, no. 2, 2019. [11] F. F. de Oliveira, Dias, L. A., Fernandes, M. A. C., Parallel Implementation of Smith-Waterman Algorithm on FPGA, 2021. [12], Li, L., Lin, J., & Wang, Z. (2007). PipeBSW: A Two-Stage Pipeline Structure for Banded Smith-Waterman Algorithm on FPGA, IEEE Computer Society Annual Symposium on VLSI, 2021. [13] P. Kingma, Performance of Smith-Waterman DNA sequence matching on a FPGA, University of Twente, Enschede, July 2022. [14] Z. Xia, Y. Cui, A. Zhang, T. Tang, L. Peng, C. Huang, C. Yang, X. Liao, A Review of Parallel Implementations for the Smith-Waterman Algorithm, Interdisciplinary Sciences, Computational Life Sciences, vol. 14, no. 1, 2022.

2. FPGA Acceleration for DNA Sequence Alignment;Caffarena G;Journal of Circuits Systems and Computers,2007

3. FPGA-Accelerated 3rd Generation DNA Sequencing;Wu Z;IEEE Transactions on Biomedical Circuits and Systems,2020

4. Feng, Y., Zhang, Y., Ying, C., Wang, D., & Du, C. (2015). Nanopore-Based Fourth-Generation DNA Sequencing Technology, Genomics, Proteomics & Bioinformatics, vol. 13, no. 1, pp. 4–16, 10.1016/j.gpb.2015.01.009.

5. Feng, Y., Zhang, Y., Ying, C., Wang, D., & Du, C. (2015). Nanopore-based fourth-generation DNA sequencing technology, Genomics, Proteomics & Bioinformatics, vol. 13, no. 1, pp. 4–16, Jan.