Sequence Alignment Algorithms in Hardware Implementation: A Systematic Mapping of the Literature

One of the primary activities of the bioinformatics is the alignment of sequences, which can find similar patterns between two sequences and determine their structure, their control information, or even their own functions. The growth of databases increases the computational effort spent to execute sequence alignment algorithms. Consequently, it can take a considerably long time to run these algorithms on general purpose processors. This paper aims to map and analyze articles related to the implementation of sequence alignment algorithms in FPGAs and GPUs to identify the most recent findings on the subject, as well as possible gaps that may lead to further investigations. The systematic mapping led to the selection of twenty-three articles using FPGA and the GPU as the hardware platform. It also identified six sequence alignment algorithms: Needleman-Wunsch, Smith-Waterman, HMM, BLAST, BWA e FMIndex. The present work was able to evaluate how often these hardware and algorithms are being used in scientific researches and their benefits in terms of processing time and energy consumption.