# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 2 Materials and Methods
### Collecting Read-Assembly Data
#### 2.2.2 Collecting Long-Read Datasets  
Three long-read human datasets, two real and one synthetic, were selected and prepared for the comparison between the alignment algorithms. Real datasets can provide insights into the real runtime and accuracy behaviors of Minimap2. Two datasets were selected based on size, which is expected to be over the minimum coverage for clinical applications (\(\geq\)8\(\times\)), and on origination from current and known sequencing technologies. The datasets were selected from ethnicities that lack representation in the most renowned databases; therefore, it is expected for these to diverge more from the genome reference used. This divergence can expose assembly algorithms to more situational adversities. The first dataset was collected from real human ONT (Oxford Nanopore Technologies) reads at the European Nucleotide Archive (ENA) [34], from a project that sequenced and produced the Yoruba reference genome NA19240, using 5 PromethION flow cells [35]. The reads have on average 16,900 bases according to ENA's base and read counts (28,528,692,209 and 1,688,000, respectively) and are referred to as \"real ONT\" reads in this work. The second dataset was collected from real PacBio Sequel II reads at the NCBI (National Center for Biotechnology Information) [36]. The reads were collected from a Sri Lankan individual. The reads have an average length of 13,329 bases according to NCBI's base and read counts (52.2 \(\times\) 10\({}^{9}\) and 3,916,231) and are referred to as \"real PacBio\" reads in this work. The third read set to be assembled with Minimap2 was generated with the PBSIM tool [37], which simulates a set of human long reads, produced by PacBio's technology. The model-based simulator collects stretches of a reference sequence, mimicking the read length distribution, and adds variants and sequencing errors to them. The simulated long reads were extracted from the GRCh38's primary assembly file, with an error profile sampled from the model file \"m131017_060208_42213_\".1.\"\", which was downloaded at [38]. This group of reads will be referred to as the \"simulated\" reads. Simulated reads are useful for evaluating the mapping accuracy, due to the lack of truth in real data because, from the read sets alone, it is not possible to prove the original position of each read. Minimap2's paper presented this process to compare their mapping accuracy with other assembly tools [14]. For each of the 193 sequences in the GRCh38 reference's primary assembly, PBSIM outputs the simulated reads in files with FASTQ format, while the read origins and alignments in relation to the reference are in files with MAF format, which are useful for evaluating the mapping accuracy. The tool provides the merging of all output files in corresponding formats, resulting in 121 GB of data in total. The coverage depth is set to 20, and the default read length thresholds were from 100 to 25,000; the mean length, deviation, mean accuracy, and accuracy deviation were sampled from the model file given. Some simulated reads had lengths outside the determined threshold and were filtered out. Simulation statistics are presented in Table 1. Over seven million reads were generated in total, which corresponds to a 20\(\times\) coverage depth. The read lengths' mean, standard deviation, minimum, and maximum values are, respectively, 8310, 106.26, 100, and 24,988. The reads' similarity rate to the reference strand is, on average, 0.85 with 0.00017 s.d. The substitution, insertion, and deletion rates are 0.015, 0.090, and 0.046, respectively. Note that, in the sample given, there were almost twice as many insertions than deletions. The read length histograms of the simulated and two real datasets were obtained, and the results are displayed in Figure 3. The average read length alone sometimes cannot provide all of the information. For example, the real PacBio dataset and the real ONT dataset have close mean lengths (13,329 and 16,900), but their length distributions are