University of Washington
Seattle, USA · 34 authors
University of Washington (Seattle, USA): 34 authors and 34 papers in the de novo peptide sequencing catalog.
Departments
- Department of Chemistry
- Department of Electrical and Computer Engineering
- Department of Genome Sciences
- Paul G. Allen School of Computer Science and Engineering
Papers (34)
- AI proteomics: from protein identification to virtual cells (2026, Nature Methods)
- CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo (2026, bioRxiv)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026, Nature Methods (Registered Report))
- Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025, Journal of Proteome Research)
- Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2025, Cell Systems)
- A procedure for controlling the false discovery rate of de novo peptide sequencing (2025, bioRxiv)
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025, bioRxiv)
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2025, Nature Methods)
- MARLOWE: Taxonomic Characterization of Unknown Samples for Forensics Using De Novo Peptide Identification (2025, bioRxiv)
- Foundation model for mass spectrometry proteomics (2025, arXiv)
- MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics (2025, Journal of Proteome Research)
- Deep Learning Methods for De Novo Peptide Sequencing (2024, Mass Spectrometry Reviews)
- Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2024, bioRxiv)
- Wrangling a de novo sequencing benchmark (2024, Springer Nature Communities (Behind the Paper))
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (2024, Scientific Data)
- Accounting for Digestion Enzyme Bias in Casanovo (2024, Journal of Proteome Research)
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models (2024, ChemRxiv)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024, Nature Communications)
- A transformer model for de novo sequencing of data independent acquisition mass spectrometry data (2024, bioRxiv)
- A learned score function improves the power of mass spectrometry database search (2024, bioRxiv)
- Detection of Short Peptides as Putative Biosignatures of Psychrophiles via Laser Desorption Mass Spectrometry (2023, Astrobiology)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023, bioRxiv)
- De novo mass spectrometry peptide sequencing with a transformer model (2022, ICML 2022)
- De novo mass spectrometry peptide sequencing with a transformer model (2022, bioRxiv)
- Protein cycling in the eastern tropical North Pacific oxygen‐deficient zone: A de novo‐discovery peptidomic approach (2022, Limnology and Oceanography)
- Deep Learning in Proteomics (2020, Proteomics)
- Assessing Protein Sequence Database Suitability Using De Novo Sequencing (2020, Molecular & Cellular Proteomics)
- 2018 YPIC Challenge: A Case Study in Characterizing an Unknown Protein Sample (2019, Journal of Proteome Research)
- De Novo Sequencing and Homology Searching (2012, Molecular & Cellular Proteomics)
- Informatics for protein identification by mass spectrometry (2005, Methods)
- Searching Sequence Databases via De Novo Peptide Sequencing by Tandem Mass Spectrometry (2002, Molecular Biotechnology)
- Implementation and Uses of Automated de Novo Peptide Sequencing by Tandem Mass Spectrometry (2001, Analytical Chemistry)
- Sequence database searches via de novo peptide sequencing by tandem mass spectrometry (1997, Rapid Communications in Mass Spectrometry)
- Computer program (SEQPEP) to aid in the interpretation of high-energy collision tandem mass spectra of peptides (1989, Biological Mass Spectrometry)