## 3 Results
As shown in Figure 7b, approximately 73% of the peptides identified by CorrDIA were also identified by DIA-Umpire, and approximately 88% of the peptides identified by DIA-Umpire were also identified by CorrDIA, a 21% increase compared to DIA-Umpire. As shown in Table 3, we recorded the number of identified mass spectra and the number of identified peptides using the two respective methods. We can see that the number of identified peptides accounted for 81 percent of the total number in the mass spectra by CorrDIA and only 69 percent when analyzed by DIA-Umpire. It was clear that a large number of the results in the mass spectra came from the same peptide in DIA-Umpire. In our method, by removing redundancy and removing isotopic peak clusters, the repetition rate of most interpreted pseudo-MS/MS spectra from the same peptide was greatly reduced. We also present the precursor ion information of the same identified sequence with a different precursor \(m/z\) interpreted by CorrDIA and DIA-Umpire. Some examples are provided in Table 4. We calculated the theoretical precursor \(m/z\) of the sequence, and we can see that the precursor interpreted by DIA-Umpire is not as accurate as CorrDIA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Spectra** & **Peptide** & **Proportion** \\ \hline CorrDIA & 34,989 & 28,368 & 81\% \\ \hline DIA-Umpire & 33,993 & 23,513 & 69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The number of mass spectra and peptides by identified CorrDIA and DIA-Umpire. Figure 7: (a) Venn diagram of peptide ions of HeLa dataset identified by CorrDIA and DIA-Umpire; (b) Venn diagram of peptide ions of human dataset identified by CorrDIA and DIA-Umpire. Finally, to verify the correctness of the peptides that CorrDIA individually identified and the correctness of our pseudo-MS/MS spectra algorithm, we used pFind to identify peptides again for the.mgf files of this part on the human dataset. In Figure 8, we show the distribution of q-value in the identification results by pFind. The abscissa represents the peptides. Using a false discovery rate of 1%, almost 85 percent of the peptide sequences in the differential set were also searched in pFind. Part of the theoretical precursor \(m/z\) of these peptides and the precursor \(m/z\) identified by CorrDIA for these peptides is also recorded in Table 5. It can be concluded that this part of the results was also correct. Different from DIA-Umpire, in our experiment, we took the MS/MS spectrum as the center. According to the above experiments, it can be concluded that the quality and quantity of the identification results were improved. Under the condition of setting the retention time interval value, the high similarity of chromatograms demonstrated increased efficiency. The number of recognized peptides increased. Additionally, under the control of a filter (1%), the correctness of the experiments was ensured. The accuracy of interpreted precursor ions also significantly increased when deleting the isotope peak cluster. The number of peptide results increased by 12% and 21%, and the repetition rate decreased by 12%. \begin{table}
\begin{tabular}{c c c c} \hline
**Sequence** & **Theoretical _m/z_** & **CorrDIA _m/z_** & **DIA-Umpire _m/z_ \\ \hline ALVSGKPAESSAVAATEK & 572.64 & 571.93 & 571.63 \\ \hline QLQQAQAAGAEQEVEK & 864.43 & 864.42 & 863.42 \\ \hline VAVEEVDEEGK & 602.29 & 601.79 & 601.28 \\ \hline LDASESLR & 445.73 & 445.23 & 444.73 \\ \hline LYKEELEQTYHAK & 551.28 & 550.61 & 550.28 \\ \hline \end{tabular}
\end{table} 
Table 4: The same identified sequence with a different precursor \(m/z\), interpreted by CorrDIA and DIA-Umpire. Figure 8: The Q-value curve of differential set part obtained by pFind.