# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 3 Results
### GACT-X and Minimap2's ksw Alignment Accuracy  
Following the method indicated in Section 2.6, with the software version of Minimap2 running on the simulated reads, 99.45% of the reads were mapped to the correct RefSeq. From these, over 99.6% of the reads were mapped with an overlap greater than 90% of the expected mapping region.  
To provide a qualitative view, Table 4 shows the alignment accuracy of GACT-X with respect to ksw's accuracy--for each dataset, the percentage of alignment cases presenting a better or worse accuracy is based on the alignment score differences. Three score columns are defined: lower accuracy (<0 difference); equal accuracy (0 difference); and higher accuracy (>0 difference). For the simulated dataset, 3.90% of GACT-X's results had a lower alignment score compared to Minimap2's ksw function, 95.42% had the same score, and 0.68% had a higher score. For the real ONT dataset, 41.21% of GACT-X's results had a lower alignment score than ksw, 47.79% had the same score, and 11.00% had a higher score. For the real PacBio dataset, 14.90% of GACT-X's results had a lower alignment score than ksw, 66.83% had the same score, and 18.28% had a higher score.  
Table 5 presents the distribution of query (read data) alignment lengths in the ranges 0-80, 80-90, 90-100, and 100 percent of the query. The alignment length indicates the quantity (proportion) of bases in the read data that is used effectively in the final alignment; the closer to 100 percent, the better. Note that the alignment length can be shorter because semi-global alignment is performed by both algorithms, which allows early interruption to not compromise the alignment score. For the simulated dataset, Minimap2's ksw presented 1.06%, 0.22%, 60.97%, and 37.62% of the cases with base alignment results within those ranges, respectively, whereas GACT-X presented 1.52%, 0.23%, 55.21%, and 43.04%. For  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GACT-X ksw Score Difference** & \textless{}0 & 0 & \textgreater{}0 \\ \hline
**Simulated PacBio** & 3.90\% & 95.42\% & 0.68\% \\ \hline
**Real ONT** & 41.21\% & 47.79\% & 11.00\% \\ \hline
**Real PacBio** & 14.90\% & 66.83\% & 18.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimap2’s ksw and GACT-X’s alignment score differences for three datasets.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Average** & **Real Time** & **CPU Time** & **Dataset Size** & **Throughput** & **Chaining** & **Extending** \\  & **Length (nt)** & **(Hours) * & **(Hours) * & **(Gbases)** & **(kbases/s)** & **Time ** & **Time ** \\ \hline Simulated PacBio Reads & 8300 & 2:46 & 42:41 & 61.99 & 403.46 & 8\% & 49\% \\ \hline Real PacBio Reads & 13,300 & 4:55 & 88:21 & 52.20 & 164.11 & 50\% & 25\% \\ \hline Real ONT Reads & 16,900 & 2:14 & 34:27 & 28.53 & 230.00 & 27\% & 42\% \\ \hline \hline \end{tabular}  
* 40 threads, complete dataset. ** 1 thread, first 200,000 reads.  
\end{table}
Table 3: Minimap2’s throughput and bottlenecks running on long-read datasets.  
the real ONT dataset, ksw presented 21.56%, 2.59%, 73.57%, and 2.29% of the cases with base alignment results within those ranges, respectively, whereas GACT-X presented 36.24%, 3.64%, 58.47%, and 1.64%. For the real PacBio dataset, ksw presented 18.42%, 0.76%, 13.15%, and 67.67% of the cases with base alignment results within those ranges, respectively, whereas GACT-X presented 12.01%, 2.71%, 19.59%, and 65.69%.