Protein Interaction Prediction Through Efficient FPGA and GPU Implementation

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Dehghan Nayeri, Ali | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 56717 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Koohi, Somayyeh
Abstract:
Alignment of genetic sequences is a fundamental part of genetic and bio-science. Alignment of DNA and protein sequences has an effective role in accelerating and simplifying problems in Bioinformatics like predicting protein interactions. Smith-Waterman algorithm is a precise algorithm for performing local alignment, suffering from computation complexity. There are some implementations on CPU, GPU, and FPGA platforms in order to reduce the run time of this algorithm. FPGA implementation is considered because of low power consumption and high degree of parallelism. With using pipeline and hardware redundancy techniques, various architectures have been proposed and implemented. In the best results, the proposed architecture uses 230 MHz working clock frequency and its performance reaches up to 117 GCUPS. These results show our working frequency and performance in comparison with other similar works, has been increased by 15% and 11% respectively. Then we implemented our architecture on Xilinx ZC706 which has been tested and validated on real datasets like Kiba, PDBBinding, BindingDB, and Davis
Keywords:
Alignment ; Field Programmable Gate Array (FPGA) ; Graphics Procssing Unit (GPU) ; Protein Interaction Network ; Smith-Waterman Algorithm ; Genetic Sequence ; Hardware Implementation

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