# A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme
## 3 Performance Evaluation
### Compression Performance
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Metrics**} \\ \cline{3-6}  & & **C-Size** & **C-Mem** & **D-Mem** & **C-Time** & **D-Time** \\ \hline \multirow{4}{*}{NC\_017526} & Bind & 0.64 & 3.67 & 8.6 & 0.64 & 0.26 \\  & Deliminate & 0.63 & **3.66** & **6.55** & **0.37** & **0.23** \\  & MFCompress & 0.65 & 514.16 & 513.13 & 0.66 & 0.55 \\  & DNAC-SEB & **0.55** & 36.71 & 64.31 & 1.78 & 1.24 \\ \hline \multirow{4}{*}{NC\_017652} & Bind & 1.2 & 4.04 & 8.6 & 0.49 & 0.36 \\  & Deliminate & 1.19 & **4.02** & **6.55** & **0.44** & **0.25** \\  & MFCompress & 1.22 & 514.34 & 513.13 & 1.26 & 0.95 \\  & DNAC-SEB & **1.06** & 49.03 & 101.45 & 1.67 & 1.27 \\ \hline \multirow{4}{*}{sacCer3} & Bind & 3.16 & 5.75 & 8.12 & **2.78** & **2.41** \\  & Deliminate & 2.85 & **2.56** & **6.19** & 5.26 & 4.94 \\  & MFCompress & 2.77 & 473.9 & 473.78 & 4.32 & 3.5 \\  & DNAC-SEB & **2.48** & 5.09 & 13.54 & 26.57 & 9.71 \\ \hline \multirow{4}{*}{Ce10} & Bind & 23.17 & **37.92** & 7.54 & 11 & **7.27** \\  & Deliminate & 22.36 & 70.29 & **7.11** & **8.55** & 8.54 \\  & MFCompress & 21.67 & 433.92 & 431.49 & 20.16 & 19.04 \\  & DNAC-SEB & **19.75** & 68.75 & 212.75 & 73.13 & 45.04 \\ \hline \multirow{4}{*}{Chimpanzee} & Bind & 619.35 & 274.48 & 28.52 & 350.84 & 180.28 \\  & Deliminate & 590.48 & 284.02 & 29.44 & 257.02 & 172.83 \\  & MFCompress & 581.12 & 482.8 & 465.12 & 575.22 & 426.53 \\  & DNAC-SEB & 570.73 & 522.23 & 792.9 & 1445.15 & 1155.97 \\ \hline \multirow{4}{*}{Korea2009024} & Bind & 693.14 & **295.05** & **30.06** & 380.38 & 207.44 \\  & Deliminate & 604.16 & 305.41 & 31.07 & **303.15** & **194.99** \\  & MFCompress & 604.12 & 665.44 & 577.45 & 503.18 & 500.31 \\  & DNAC-SEB & **577.23** & 649.56 & 703.69 & 1479.57 & 1003.27 \\ \hline \multirow{4}{*}{Eukaryotic} & Bind & 466.98 & **387.81** & **39.12** & 285.24 & 154.01 \\  & Deliminate & 445.89 & 389.1 & 40.3 & **206.43** & **136.83** \\ \cline{1-1}  & MFCompress & 435.79 & 478.86 & 477.89 & 468.18 & 419.15 \\ \cline{1-1}  & DNAC-SEB & **423.32** & 1859 & 1349.05 & 1117.44 & 903.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of compressed data size, processing time and memory utilization using FASTA datasets. From Table 8 values, it is clear that the proposed method achieved a better compression ratio than other methods on all the applied dataset. In all cases, DNAAC-SEB generated low file size as compared to other algorithms and hence a better compression ratio. Using ERR005143_1 dataset, DNAAC-SEB shows an improvement of 4.78% and 3.52% to GTZ and Fqzcomp, respectively. The FASTQ file compressors, including Fqzcomp [11], Quip [53], DSRC [10] and gzip [27], were compared and the results are reported in Table 9.  
The results of FASTQ file compression with different compressors show that DNAAC-SEB outperformed existing tools in terms of compression ratios for all types of FASTQ benchmark datasets. From the experimental results, we could observe that the best compression ratio, minimum memory and maximum speed requirements were competing goals. Generally, the higher compression rates were achieved by compressors that were slower and required higher memory. For example, Gzip had the best performance in the case of memory and decompression speed usage but offered the lowest compression ratio, which provided valuable long-term space and bandwidth savings. When the data size was an important aspect, tools such as DNAAC-SEB were crucial.