Accelerating DNA pairwise sequence alignment using FPGA and a customized convolutional neural network. (June 2021)

• Record Type: Journal Article
• Authors: Rashed, Amr Ezz El-Din; Obaya, Marwa; Moustafa, Hossam El~Din
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: 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); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107112
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: An implementation based on a look-up-table (LUT) to accelerate DNA sequence alignment algorithms under certain limitations is presented. Unlike other studies, this ROM-based hardware implementation requires only O(N/4) calculation steps to obtain the complete result The derivation of 254 patterns is presented for a global alignment array for all the input combinations. This implementation represents a new use of classical ML and deep CNN for global sequence alignment. Our implementation is valid for extremly long RNA/DNA sequences and applicable to software and hardware design. Abstract: 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. Graphical abstract: Image, graphical abstract