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GlycoMsHelper: A Simple and Traceable Workflow for Glycomic Mass Spectrometry Data Interpretation
Wei-Ze Kong1,2, Yann Guerardel2,3, Morihisa Fujita1,2
1The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan.
Glycobiology
|July 28, 2026
Summary
GlycoMsHelper is a new R-based workflow that semi-automates glycomic mass spectrometry (MS) data analysis. It accurately identifies glycan compositions, overcoming bottlenecks in manual interpretation for N-glycans, O-glycans, and glycosphingolipids.
Area of Science:
- Glycomics
- Mass Spectrometry
- Computational Biology
Background:
- Glycosylation is crucial for cellular processes, but glycomic analysis via mass spectrometry (MS) is hindered by manual data interpretation.
- Challenges include spectral denoising and identifying glycan-derived tandem MS (MS2) spectra.
Purpose of the Study:
- To develop GlycoMsHelper, an R-based workflow for semi-automated glycomic MS composition analysis.
- To improve the efficiency and accuracy of interpreting glycomic MS data, particularly for large-scale studies.
Main Methods:
- GlycoMsHelper integrates denoising strategies and pattern recognition for MS2 spectra identification.
- It matches spectra against glycan libraries built with biosynthetic constraints.
- The workflow is adaptable for N-glycans, O-glycans, and glycosphingolipids (GSLs).
Main Results:
- GlycoMsHelper correctly identified 42 out of 43 expert-curated N-glycan compositions.
- It demonstrated versatility on GSL and O-glycan datasets, identifying previously missed compositions.
- The tool provides traceable outputs for downstream validation.
Conclusions:
- GlycoMsHelper offers a practical solution for semi-automated glycomic MS data interpretation.
- It is particularly valuable for exploratory analysis of new or uncharacterized datasets.
- The workflow's modular design and GUI facilitate integration into existing glycomic analysis pipelines.
