# A New Lossless DNA Compression Algorithm Based on A Single-Block Encoding Scheme
## 3 Performance Evaluation
### Compression Performance  
The performance of our algorithm was evaluated in terms of compression ratio (CR), memory usage, as well as compression and decompression time. CR was obtained by dividing the number of bits in compressed data by the number of bits in the original data [52]. To assess the efficiency of the proposed method, its compression performance was compared with the other lossless DNA compressors, namely Biocompress-2 [5], DNACompress [32], DNAPack [16], improved RLE [41], DNA-COMPACT (DNA-COMP) [42], Modified HuffBit [48] and others in terms of CR using a real DNA dataset. Moreover, FS4 CP [57] and OPGC [49], as well as the OBComp algorithm [50] were also included in the comparison. Traditional compression algorithms such as gzip [27], bzip2 [28] and arithmetic [47] coding and some of the transformation techniques such as BWT and MTF were also used to compress the DNA sequences. The obtained compression ratios of the DNA-SEB against state-of-the-art algorithms are shown in Tables 3 and 4. It should be noted that in Tables 4-9, the best results in terms of different compression metrics are shown with a bold font. Regarding arithmetic coding and the results shown on the Table 5, the method provided a small factor of compressibility, better than bzip2 5.92%. At the same time, context tree weighting (CWT) [58] managed to perform better than other methods, except our algorithm. The DNAC-SBE outperformed other text compression algorithms on all the applied DNA datasets. The combination of different transformation methods also failed to compress the DNA sequence efficiently because the DNA sequences followed no clear rules like those of text data, which could not provide proper compression results. The percentage reduction of compressing genomic FASTA data sequences using DNAC-SEB and different methods (that do not reorder the input sequences) in comparison to 7z are reported in Table 6. Original file sizes and compressed data sizes in megabytes with 7z are also shown in columns 2 and 3 in the same table. From the results, we observed that the percentage reduction attained by DNAC-SEB was higher than Bind, Deliminate, MFCompress and SeqCompress. DNAC-SEB gave the best results for all the used datasets and SeqCompress gave the second to best percentage reduction for all datasets except for the eukaryotic. Other aspects result such as compressed size, execution time and memory usage using different FASTA genomic data sequences are reported in Table 7. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequence** & **CH-** & **HUMD-** & **HUM-** & **HEHC-** & **MPO-** & **VAC-** & **MTP-** & **HUMH-** \\  & **MPXX** & **YTROP** & **HBB** & **MVCG** & **MTCG** & **CG** & **ACGA** & **PRTB** \\ \hline Length & 121024 & 38770 & 73308 & 229354 & 186609 & 191737 & 100314 & 56737 \\ Biocompress2 & 1.684 & 1.926 & 1.880 & 1.848 & 1.937 & 1.761 & 1.875 & 1.907 \\ CTW+LZ & 1.669 & 1.916 & 1.808 & 1.841 & 1.900 & 1.761 & 1.856 & 1.843 \\ GenCompress & 1.671 & 1.923 & 1.820 & 1.847 & 1.901 & 1.761 & 1.862 & 1.847 \\ DNACompress & 1.672 & 1.911 & 1.789 & 1.849 & 1.892 & 1.758 & 1.856 & 1.817 \\ DNAPack & 1.660 & 1.909 & 1.777 & 1.835 & 1.893 & 1.758 & - & 1.789 \\ GeNML & 1.662 & 1.909 & 1.752 & 1.842 & 1.882 & 1.764 & 1.844 & 1.764 \\ XM & 1.656 & 1.910 & 1.751 & 1.843 & 1.878 & 1.765 & 1.845 & 1.736 \\ Genbit & 2.225 & 2.234 & 2.226 & - & 2.24 & 2.237 & 2.243 & 2.240 \\ Improved RLE & - & 1.948 & 1.911 & 1.885 & 1.961 & 1.862 & - & - \\ DNA-COMP & 1.653 & 1.930 & 1.836 & 1.808 & 1.907 & - & 1.849 & - \\ FS4 CP & 1.996 & 2.068 & - & 2.00 & 1.98 & 1.967 & 1.978 & 1.981 \\ OBComp & 1.652 & 1.910 & 1.911 & 1.862 & 1.971 & 1.850 & 1.831 & 1.910 \\ OPGC & 1.643 & 1.904 & 1.748 & 1.838 & 1.868 & 1.762 & 1.878 & 1.726 \\ Modified Huffbit & 1.931 & 2.040 & 2.09 & 2.14 & 1.94 & 2.003 & 1.944 & 2.231 \\ DNAC-SBE & **1.604** & **1.724** & **1.717** & **1.741** & **1.721** & **1.671** & **1.650** & **1.720** \\ \hline \hline \end{tabular}
\end{table}