Hardware Acceleration of Smith Waterman Algorithm for Short Read DNA Alignment Using FPGA
The Field Programmable Gate Arrays (FPGAs) are highly attractive options for hardware implementations of Bioinformatics algorithms as they provide physical security and potentially higher performance than software solutions. The Smith Waterman algorithm is the most accurate local alignment algorithm for DNA and protein sequencing. However the existing parallel Smith Waterman algorithm needs extra memory space and this limits the size of a sequence to be aligned. As the data of biological banks expands exponentially, so we develop a new hardware parallel bi sequence and tri sequence alignment algorithm, using the strategy of divide and extend. This paper suggests hardware architecture for the Smith Waterman algorithm using Xilinx ZYNQ 7000 FPGA. Then we present the efficiency and utilization of our Parallel Smith Waterman over sequential algorithm. After that the simulated implementations of the algorithm presented to perform the most suitable hardware architecture within Xilinx ZYNQ 7000 FPGA which speeds up the algorithm by 28.4x with less Utilization and 29.4 more efficiency.
2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)