# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 1 Introduction  
Genetic sequencing has been contributing valuable information in a wide variety of medical procedures. In preventative medicine, some genetic variations are shown to raise the risk of the development of certain diseases, such as breast cancer, heart disease, and type II diabetes. For some of these diseases, therapies or preventative strategies may provide a better prognosis and quality of life. With assisted reproductive technology, genetic sequencing can also be used to indicate whether an individual is a carrier of variants that will cause diseases in their children, such as cystic fibrosis, fragile X syndrome, and sickle cell anemia [1]. Collecting and analyzing genetic data is also crucial in biology studies, including biodiversity [2], evolution [3], and metabolic pathways [4]. It has even become a commodity of public interest, providing people with insights into their ancestry and personal phenotypes. Current sequencing technology is incapable of reading straightaway a complete human genome, which contains about 3.2 billion nucleotides in each DNA (deoxyribonucleic acid) strand. Because of this, the DNA molecules in an analysis sample need to be cleaved into smaller nucleotide sequences called fragments. The fragments that are sequenced,or translated into the digital characters A-T-C-G for computer processing, are then called reads. A set of reads contains redundancy in such a way that a complete genome can be mounted back with computer support in a process known as read assembly. A lot of attention has been given to read assembly methodologies and algorithms; this processing phase is currently considered a major bottleneck in the genome analysis pipeline [5], which also includes laboratory sampling, sequencing, and annotating. This happens because sequencing technologies have been increasing their output capacity at an exponential rate [6], whereas computational power has been slowly reaching the limits of transistor technology [7]. The speed of acquiring and interpreting genomic information is crucial in several applications, such as prenatal testing [8; 9] or infection outbreak investigations, i.e., the latest COVID-19 (coronavirus disease 2019) pandemic [10; 11]. The most efficient assembly method for long genomes is mapping and aligning the reads against an existing genome reference for the species, using it as a guide. The latest published human genome reference is the GRCh38.p14 (Genome Reference Consortium Human Build 38 patch release 14) [12]. In human clinical trials, this method requires the assembly to have a minimum coverage of 8-20 times the length of the genome or exome. This allows the distinction of variants with higher accuracy (i.e., statistically covers sequencing errors and mapping or aligning heuristics) and the identification of heterozygous states [13]. Several reference-guided assembly algorithms and programs have been proposed [14; 15; 16], and most of them apply the seed-and-extend strategy, illustrated in Figure 1. In the seeding stage, the algorithm finds exact or non-exact matches of very short sequences between the read and the reference. Given the fact that the seeding stage might result in a set of matches with a significant number of false positives, an intermediate filtering stage is often required to reduce the number of selected items. During the extending stage, guided by the matches that remain after filtering, each read sequence is aligned to one or more regions in the reference; therefore, it is also known as the alignment stage. The seeding and filtering strategies are, in general, specific for different algorithms, while the alignment strategies are mostly based on the classic Smith-Waterman-Gotoh (SWG) algorithm [17; 18], which uses dynamic programming to align two character strings. It was published in 1981-1990 and is still used to this day in  
Figure 1: The seed-and-extend reference-guided read assembly strategy. This is a simplified example of the steps adopted in Minimap2 [14].