MapReduce Model Using FPGA Acceleration for Chromosome Y Sequence Mapping

Genome assemblies sequenced by a Whole Genome Shotgun (WGS) project predict an organism's function and history. Sequence alignment is the foundation of bioinformatics by a computational search through large genome sequence databases, which generally requires enormous amounts of memory and takes a long execution time. In this paper, an Optimized Smith-Waterman algorithm based on the Gotoh algorithm with an affine gap for accuracy alignment, the divide and conquer technique, and the MapReduce framework implemented to establish a parallel process. This model was implemented on Virtex 7 field-programmable gate arrays (FPGAs). These techniques provide a better performance, reduce the hardware requirements, improve the accuracy, increase the computational throughput, and accelerate the alignment process for big data available in a complete Y chromosome. The hardware proposed system can achieve high performance, low time consumption 1.699 ns, and decrease FPGA utilization for big data alignments Y chromosome is used as an example.