Accelerating DNA pairwise sequence alignment using FPGA and a customized convolutional neural network

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.

Automated summary

This study presents optimized software and hardware digital implementations of two widely used DNA sequence alignment algorithms based on lookup table (LUT), aiming to identify similar regions between sequences. The proposed method relies on complete parallelization and certain limitations to overcome most problems associated with dynamic programming and hardware implementation. Performance and resource usage of different hardware designs are evaluated, with a customized convolution neural network model achieving 98.3% accuracy in global alignment.