Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification.

ABSTRACT: Glycosylation changes are closely related to various diseases, including cancer. The quantitative analysis of site-specific glycans at proteomics scale remains challenging due to low glycopeptide spectra interpretation. Here, we present GlyPep-Quant, a tool for sensitive quantification and identification of site-specific glycans. Using a well-trained machine learning model, GlyPep-Quant quantified 25.1%-178.9% more site-specific glycans without missing values than pGlycoQuant, MSFragger-Glyco, and Skyline. To utilize identified information from previous large-scale dataset, an MS1 feature library-based "virtual match-between-runs" quantification scheme was developed, enabling over eightfold more site-specific glycan identification/quantification than conventional MS2-based methods. Enhance

SUBMITTER: Zhu H 

PROVIDER: S-EPMC12259847 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature