## 4 Discussion  
Among the existing de novo peptide-sequencing methods, the methods based on spectrum graphs need to design a scoring function. The information taken into account by the scoring function is not comprehensive due to the complexity and diversity of tandem-mass-spectrum data, making it challenging to recall the correct peptide sequences. At present, the method based on deep learning is mainly used to extract features directly from spectrum, but does not consider the correlation between peaks, and the spectrum needs to be vectorized into a very large vector in order to be applied to high-resolution spectrum data. Based on these problems, we proposed Denovo-GCN, which applies graph convolutional neural networks to de novo peptide sequencing for the first time, and combines convolutional neural networks for feature extraction. Finally, an end-to-end training-and-testing framework was completed.  
First of all, we compared Denovo-GCN with DeepNovo, which also uses deep learning from de novo sequencing, on the standard benchmark data set. Our experiments show that Denovo-GCN is better than DeepNovo at amino-acid precision and recall and peptide recall. In addition, in order to compare the two methods in more detail, we described the precision-recall curve and closely analyzed the differences between their results. Denovo-GCN has advantages in distinguishing similar peptide sequences, recalling more complete and correct peptide sequences instead of recalling parts of the peptide sequence.  
Graph convolutional neural networks can combine the method of spectrum graphs with the method of deep learning. This paper presented the first attempt in this area, with  
Figure 9: (**a**) Comparison of peptide recall of different designs on Bacillus datasets. (**b**) Comparison of peptide recall of different designs for different peptide lengths on Bacillus datasets.  
excellent results. The graph convolutional neural networks are based on the adjacency matrix to aggregate the features of the first-order neighboring nodes. However, the features of adjacent spectral peaks can aggregate, which we need to explore in future research. The question of how to better combine the method of spectrum graphs with the method of deep learning is worthy of further exploration by researchers.  
Conceptualization, H.W.; methodology, H.W. and R.W. ([PERSON]); software, [PERSON] ([PERSON]); validation, R.W. ([PERSON]) and H.W.; formal analysis, H.W., [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]). and X.Z.; resources, H.W.; data curation, [PERSON] ([PERSON]), X.Z. and R.W. ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, H.W.; visualization, [PERSON] ([PERSON]); supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
Support Program for Outstanding Youth Innovation Teams in Higher Educational Institutions of Shandong Province (2019 KJN048).  
Not applicable.  
All training and testing datasets can be downloaded from the link provided by DeepNovo [15]. The source data can be downloaded from the ProteomeXcharge (link: [[http://www.proteomexchange.org/](http://www.proteomexchange.org/)]([http://www.proteomexchange.org/](http://www.proteomexchange.org/))) with the accession number PXD005025, PXD004948, PXD004325, PXD004565, PXD004536, PXD004947, PXD003868, PXD004467, PXD004424.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.