from the start node to the end node, and the amino-acid sequences on the paths are the candidate peptide sequences. Finally, a more thorough scoring function is used to select the peptide sequence with the highest score from the candidate peptide sequences as the inferred peptide sequence of the spectrum. Lutefisk [8; 9; 10], SeqMS [11], SHERENGA [12], PepNovo [13], AUDENS [14], pNovo [15], and pNovo+ [16] are all based on the spectrum-graph method. The design of the scoring function and the construction of the spectrum graph directly affect the performance of the spectrum-graph method. However, if some of the fragment peaks are absent from a spectrum, then the full-length path from the start node to the end node on its spectrum graph are not connected and, consequently, the entire peptide sequence cannot be obtained. In addition, due to the complexity and diversity of tandem mass spectra, the scoring function cannot distinguish correct peptides from candidate peptide sequences well, resulting in a low peptide-recall rate. With the rapid accumulation of high-resolution MS/MS spectra and the development of computing technology, many researchers are increasingly deploying deep learning [17; 18; 19; 20; 21] to improve the performance of de novo peptide sequencing.  
In 2015, [PERSON] et al. [22] developed the Novor software. This method used the decision-tree model to consider the relative intensity, ranking, and isotopic cluster of peaks. Two models were obtained through training, namely the fragmentation-position-scoring model and the amino-acid-scoring model. According to another article, Novor is 7-37% higher than PEAKS [23] in terms of amino-acid accuracy. However, some subsequent articles [24; 25] reported that although Novor performed better on low-resolution collision-induced dissociation (CID) datasets than PEAKS, it performed poorly on high-resolution high-energy collision dissociation (HCD) datasets.  
In 2017, [PERSON] et al. first used deep-learning methods for de novo sequencing and developed DeepNovo [24]. This method combines CNN and long short-term memory (LSTM) models for data-dependent acquisition (DDA) mass-spectrum data, and the de novo peptide=sequencing performance exceeded those of the PEAKS, PepNovo, and Novor tools. However, this method is too complex, using two CNN models and a LSTM model. Additionally, DeepNovo needs to discretize the spectrum into a large vector in order to be applied to high-resolution mass-spectrum data, which increases the computational and memory overhead. In 2021, [PERSON] et al. proposed PointNovo [26]. This method uses an order invariant network structure (T-Net) and a LSTM. Although T-Net can use the global features of peaks and does not discretize the mass spectrum into a large vector, it does not use local features, such as the relationship between spectral peaks.  
In paper, we propose a de novo sequencing method, Denovo-GCN, based on GCN [27] and CNN. Denovo-GCN does not need to design a scoring function based on prior knowledge, and it is ready to be applied to high-resolution data without discretizing the spectrum into a very large vector. Denovo-GCN represents the spectrum as an undirected graph according to the mass difference between peaks. The spectrum graph is represented by an adjacency matrix that contains the relationship between spectral peaks. At the same time, when predicting the next amino acid, all the possible options are enumerated and the corresponding theoretical m/z is calculated. The feature of the node is expressed as the matching information of the theoretical m/z and the observed m/z. Denovo-GCN uses CNN to extract the features of the nodes. Furthermore, GCN aggregates the features of neighboring nodes through the adjacency matrix. Finally, Denovo-GCN provides an end-to-end training and prediction process. We evaluate our approach on publicly available nine-species benchmark data sets. Our experiment's results show that Denovo-GCN outperforms DeepNovo with a relative improvement of 13.7-25.5% in terms of the peptide-level recall.