## 3 Results
### Performance of Tag Index Generation Algorithm  
In this experiment, five tag index generation algorithms are compared. Protein databases of 10 species are selected for testing. Table 2 shows the storage cost constructed by different methods on protein databases of ten species. STIP has the lowest storage cost of indexes, achieving optimal space performance. Compared with Open-pFind, STIP reduces index space consumption by 58.88% to 78.18%. Compared with MODplus, STIP reduces storage cost by 38.51% to 74.09%. Compared with TIIP, STIP reduces the index space consumption by 73.87% to 83.16%. The storage cost of the 5-mer tag index generated by the five methods is shown in Figure 6. Compared with PIPI2, the index space consumption constructed by STIP on the _Danio rerio_ dataset increased by 2.38%. The reason for this result is that the _Danio rerio_ dataset has fewer tag types and a smaller database size. On the other nine species datasets, the spatial performance of STIP is significantly higher than that of PIPI2, verifying that STIP has obvious advantages in protein databases with larger scale. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Storage Cost (MB)**} \\ \cline{2-6}  & **Open-pFind** & **MODplus** & **TIIP** & **PIPI2** & **STIP** \\ \hline _Homo sapiens_ & 532.58 & 325.99 & 818.92 & 246.10 & 196.38 \\ _Oryza sativa_ & 452.81 & 276.92 & 691.34 & 189.75 & 167.24 \\ _A. thaliana_ & 489.47 & 302.00 & 754.53 & 216.79 & 184.32 \\ _Rattus norgeicus_ & 353.15 & 233.26 & 554.32 & 152.64 & 143.44 \\ _Mouse_ & 284.66 & 191.50 & 444.13 & 124.52 & 116.05 \\ _Bos taurus_ & 286.29 & 197.07 & 452.56 & 126.91 & 116.57 \\ _Danio rerio_ & 281.56 & 193.07 & 448.25 & 113.08 & 115.78 \\ _Drosophila_ & 194.16 & 136.96 & 303.42 & 95.40 & 77.38 \\ _melanogaster_ & 107.04 & 79.53 & 161.72 & 75.28 & 34.01 \\ _P. falciparum_ & 46.25 & 38.95 & 59.93 & 10.39 & 10.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of storage cost of index constructed by Open-pFind, MODplus, TIIP, PIPI2, and STIP on protein databases of ten species. Figure 6: Storage cost of tag index generated by Open-pFind, MODplus, TIIP, PIPI2, and STIP on FASTA files from ten species. To test the performance of STIP in constructing indexes on protein databases of different sizes, we used a random sampling method to select proteins from the NCBI nr dataset and construct protein databases of different sizes. We set the tag length from 1 to 6, and the space consumption of the index is shown in Figure 7.  
Analyzing the experimental results, it can be found that the index space corresponding to 3-mer tags is the smallest. The larger index space corresponding to shorter tags is because short tags often appear more frequently in protein databases, so it needs to be recorded in tag indexes. The larger index space corresponding to longer tags is due to the greater number of types of long tags. The 3-mer tag, composed of 20 amino acids, has 8000 types, while the 6-mer tag has as many as \(6.4\times 10^{7}\) types, resulting in a higher consumption of tag index space for the latter. In addition, STIP successfully constructed an index for the NCBI nr protein database, with an index storage space of 179.8 GB and a compression rate of 24.8%. This is a task that existing protein database index construction algorithms cannot accomplish, and this result verifies the ability of STIP on large-scale protein databases. To investigate the robustness and reliability of the STIP algorithm, we have conducted a series of ablation experiments. The experimental results are shown in Figure 8. After removing tag sequence encoding, index dimensionality reduction, delta encoding, and dynamic bit width encoding from STIP, compared with the tag index construction algorithm that removes all compression methods, they, respectively, save 15.36%, 20.09%, 58.77%, and 62.16% of storage cost.