Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.
Bioinformatics
Carbohydrates
Computational biology
Machine learning
Mass spectrometry
Journal
Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327
Informations de publication
Date de publication:
24 Aug 2024
24 Aug 2024
Historique:
received:
28
06
2024
accepted:
09
08
2024
revised:
06
08
2024
medline:
24
8
2024
pubmed:
24
8
2024
entrez:
24
8
2024
Statut:
aheadofprint
Résumé
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers for reduced glycans in negative ion mode. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.
Identifiants
pubmed: 39180595
doi: 10.1007/s00216-024-05500-9
pii: 10.1007/s00216-024-05500-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Vetenskapsrådet
ID : BioMS
Informations de copyright
© 2024. The Author(s).
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