## 3 Results  
We used the previously described data sets and performance metrics to evaluate Denovo-GCN and compared it with the neural-network-based method, DeepNovo. For a fair comparison with DeepNovo, we set the same post-translational modifications (PTMs), including the fixed-modification carbamidomethylation of C, variable modification oxidation of M, and deamidation of NQ. To characterize the performance of DeepNovo, we relied on the results published in a previous article [15]. Figure 3: Calculation process. Figure 4 shows the total precision and recall of DeepNovo and Denovo-GCN on nine species datasets. It can be seen that Denovo-GCN is better than DeepNovo. Denovo-GCN outperformed DeepNovo, with mean improvements of 0.023 and 0.028 in amino-acid precision and recall, respectively. Figure 4c shows that Denovo-GCN outperforms DeepNovo with a mean improvement of 0.068 in peptide recall. It is important to note that on the Candidatus endoloripes (C. endoloripes) dataset, Denovo-GCN performed somewhat worse than DeepNovo in precision and recall for amino acids, although it displayed a substantially greater peptide recall than DeepNovo. This demonstrates that while DeepNovo can recall more amino acids for a peptide sequence than Denovo-GCN, the latter can recall the right peptide sequence. This occurs due to the close proximity of the mass of an individual amino acid or to the combination of two amino acids in a peptide sequence. For example, the masses of N (deamidation; asparagine with deamidation modification, 115.02695) and D (asparatic acid, 115.02694) were quite close to each other, as were those of AG (alanine and glycine, 128.05857) and Q (glutamine, 128.05858). In consideration of three variable modifications (deamidation on NQ oxidation on M), there are 177 cases mentioned above. Therefore, DeepNovo can recall most amino acids for a peptide sequence, but it is not good as Denovo-GCN at distinguishing these amino acids or amino-acid combinations with very similar masses. Since biologists usually prefer to obtain a complete and correct peptide sequence rather than a portion of the right peptide sequence, this is an advantage in practical applications. We performed a more detailed analysis of the test results from Denovo-GCN and DeepNovo on the C. endoloripes dataset. The test results from both methods were split into three groups: correct peptides, incorrect peptides due to interchange of two amino acids or isobaric combination of amino acids, and other incorrect peptides. Although there was no significant increase or decrease in the proportion of correct peptides and other incorrect peptides, as shown in Figure 5a, it can be seen from Figure 5b that the number of correct peptides identified by Denovo-GCN alone was 12,199, which is more than two times that of DeepNovo. We selected the false peptides with one or two amino acids determined by  
Figure 4: (**a**) Amino-acid-recall comparisons between Denovo-GCN and DeepNovo on the nine species data. (**b**) Amino-acid-precision comparisons between three types of software on the nine species data. (**c**) Comparisons between three types of software on the nine species data. DeepNovo and Denovo-GCN. It can be seen from Figure 5c that among them, about 27% of the peptides from the Denovo-GCN gave the correct sequence. Although this is still a small proportion, it was twice that of the DeepNovo. This proves that Denovo-GCN is better than DeepNovo at recalling complete and correct peptide sequences. In order to evaluate the performances of Denovo-GCN and DeepNovo better, we describe the precision-recall curve on nine species datasets. The curves were computed by sorting the predicted peptides according to their confidence scores. For the amino-acid-level curves, all the amino acids within a given peptide received equal scores. It can be seen from Figure 6 that the curve of the Denovo-GCN was generally above that of the DeepNovo curve.