University of Waterloo
Waterloo, Canada · 18 authors
University of Waterloo (Waterloo, Canada): 18 authors and 29 papers in the de novo peptide sequencing catalog.
Departments
- David R. Cheriton School of Computer Science
- Department of Electrical and Computer Engineering
- Department of Statistics and Actuarial Science
- Department of Systems Design Engineering
- School of Computer Science
Papers (29)
- AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026, Nature Biotechnology)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Advancing Proteomic Analyses with Graph-Based Deep Learning: Protein Inference and DIA De Novo Peptide Sequencing (2025)
- Diffusion Decoding for Peptide De Novo Sequencing (2025, arXiv)
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025, Research Square)
- Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing (2024, arXiv)
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024, Molecular & Cellular Proteomics)
- De novo protein sequencing of antibodies for identification of neutralizing antibodies in human plasma post SARS-CoV-2 vaccination (2024, Nature Communications)
- NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024, bioRxiv)
- A complete mass spectrometry-based immunopeptidomics pipeline for neoantigen identification and validation (2023, Research Square)
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023, Nature Machine Intelligence)
- Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics (2023, Nature Communications)
- PaSER Novor: Real-time de novo sequencing for 4D-Proteomics applications (2023)
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021, Nature Machine Intelligence)
- Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines (2020, Nature Machine Intelligence)
- Peptide Sequencing with Deep Learning (2020)
- Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines (2019, bioRxiv)
- DeepNovoV2: Better de novo peptide sequencing with deep learning (2019, arXiv)
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018, Nature Methods)
- Protein identification with deep learning: from abc to xyz (2017, arXiv)
- De novo peptide sequencing by deep learning (2017, PNAS)
- Novor: Real-Time Peptide de Novo Sequencing Software (2015, Journal of the American Society for Mass Spectrometry)
- PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012, Molecular & Cellular Proteomics)
- De Novo Sequencing and Homology Searching (2012, Molecular & Cellular Proteomics)
- ADEPTS: Advanced peptide de novo sequencing with a pair of tandem mass spectra (2010, Journal of Bioinformatics and Computational Biology)
- SPIDER: software for protein identification from sequence tags with de novo sequencing error (2005, Journal of Bioinformatics and Computational Biology)
- An effective algorithm for peptide de novo sequencing from MS/MS spectra (2005, Journal of Computer and System Sciences)
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003, Rapid Communications in Mass Spectrometry)