Bioinformatics Solutions (Canada)
Waterloo, Canada · 21 authors
Bioinformatics Solutions (Canada) (Waterloo, Canada): 21 authors and 32 papers in the de novo peptide sequencing catalog.
Papers (32)
- High-accuracy glycan de novo prediction for N- and O-linked glycopeptides across multiple fragmentation techniques (2026, Nature Communications)
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026, Nature Biotechnology)
- Mass spectrometry-based de novo sequencing reveals non-canonical neoantigens with antitumor efficacy in hepatocellular carcinoma (2026, JHEP Reports)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Sequencing of Polyclonal Antibodies by Integrating Intact Mass, Middle–Down, and De Novo Bottom–Up Mass Spectrometry (2025, Molecular & Cellular Proteomics)
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025, Research Square)
- De Novo sequencing-assisted homology search for DIA data analysis enables low abundance peptide variants discovery (2025, bioRxiv)
- 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)
- 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)
- De novo sequencing of multiple SILAC-based tandem mass spectra (2022, 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC))
- Discovering and Validating Neoantigens by Mass Spectrometry-based Immunopeptidomics and Deep Learning (2022, bioRxiv)
- A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics (2022, Nature Communications)
- 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)
- Complete De Novo Assembly of Monoclonal Antibody Sequences (2016, Scientific Reports)
- De Novo Sequencing Assisted Approach for Characterizing Mixture MS/MS Spectra (2016, IEEE Transactions on NanoBioscience)
- An Approach for Matching Mixture MS/MS Spectra with a Pair of Peptide Sequences in a Protein Database (2015, Lecture Notes in Computer Science)
- PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012, Molecular & Cellular Proteomics)
- Better score function for peptide identification with ETD MS/MS spectra (2010, BMC Bioinformatics)
- 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)
- An Effective Algorithm for the Peptide De Novo Sequencing from MS/MS Spectrum (2003, Lecture Notes in Computer Science)