Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.

Saved in:
Bibliographic Details
Title: Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.
Authors: Levin N; Rosalind Franklin Institute, Harwell Campus, OX11 0QX Didcot, U.K.; Department of Pharmacology, University of Oxford, OX1 3QT Oxford, U.K., Saylan CC; Computational Mass Spectrometry, Technical University of Munich, D-85354 Freising, Germany., Lapin J; Computational Mass Spectrometry, Technical University of Munich, D-85354 Freising, Germany., Demyanenko Y; Rosalind Franklin Institute, Harwell Campus, OX11 0QX Didcot, U.K.; Department of Pharmacology, University of Oxford, OX1 3QT Oxford, U.K., Yang KL; Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA., Sidda J; Rosalind Franklin Institute, Harwell Campus, OX11 0QX Didcot, U.K.; Department of Pharmacology, University of Oxford, OX1 3QT Oxford, U.K., Nesvizhskii AI; Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.; Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States., Wilhelm M; Computational Mass Spectrometry, Technical University of Munich, D-85354 Freising, Germany.; Munich Data Science Institute (MDSI), Technical University of Munich, D-85748 Garching, Germany., Mohammed S; Rosalind Franklin Institute, Harwell Campus, OX11 0QX Didcot, U.K.; Department of Biochemistry, University of Oxford, OX1 3QU Oxford, U.K.; Department of Chemistry, University of Oxford, OX1 3TA Oxford, U.K.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Jun 01. Date of Electronic Publication: 2025 Jun 01.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.05.28.656555