ArticleBioinformatics (Oxford, England)2019
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- ProteoRift Protocols: A Deep Learning Filtering Workflow for Peptide Identification in Mass Spectrometry Data.Methods in molecular biology (Clifton, N.J.) · 2027Article
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- ScopeNovo: a de novo peptide sequencing method using spectrum-spectrum similarity to refine the prediction accuracy.Bioinformatics (Oxford, England) · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
- Predominant mutated non-canonical tumor-specific antigens identified by proteogenomics demonstrate immunogenicity and tumor suppression in CRC.Cell genomics · 2026Article
- Pangenome-Informed Language Models for Synthetic Genome Sequence Generation.bioRxiv : the preprint server for biology · 2025Article
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.Nature communications · 2025Article
- De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models.Scientific data · 2024Article
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing.Molecular & cellular proteomics : MCP · 2024Article
- Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species.Nature communications · 2024Article
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides.Food chemistry: X · 2024Article
- A learned score function improves the power of mass spectrometry database search.Bioinformatics (Oxford, England) · 2024Article
- Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.ArXiv · 2024Article
- Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy.Cancer science · 2024Review
- Proteomics techniques in protein biomarker discovery.Quantitative biology (Beijing, China) · 2024Review
- MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer.Nature communications · 2024Article
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.Nature communications · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
motivationDe novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.
resultsIn order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences. AVAILABILITY AND IMPLEMENTATION: pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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