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Accelerating DNA pairwise sequence alignment using FPGA and a customized convolutional neural network
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DOI：10.1016/j.compeleceng.2021.107112.png)
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Abstract
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An optimized software and hardware digital implementation of two widely used DNA sequence alignment algorithms based on lookup table(LUT) is illustrated in this study. These algorithms are the best means for identifying similar regions between sequences. The proposed implementation relies on the complete parallelization of these foundational algorithms under certain limitations to overcome most of the problems of dynamic programming and hardware implementation. The proposed method takes O(N/4) calculation steps, where N is the length of each sequence with a minimum value of four (i.e., N = 4,8,12,...). A performance comparison between the state of art and our proposed algorithm is conducted for software and hardware implementation. Combinational circuits are used for FPGA-based hardware implementation of DNA sequence alignment algorithms. Performance and device resource usage are evaluated for different hardware designs. A customized convolution neural network model is used to implement global alignment and achieve 98.3% accuracy.
Keywords:
Bioinformatics
DNA
Pairwise sequence alignment (PWSA)
Field programmable gate array (FPGA)
Espresso algorithm
Smith-Waterman (SW) algorithm
Needleman-Wunsch (NW) algorithm
Convolution neural network (CNN)
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