GlycoMsHelper: A Simple and Traceable Workflow for Glycomic Mass Spectrometry Data Interpretation

Glycosylation is a fundamental post-translational and lipid modification that plays critical roles in diverse cellular processes. Although mass spectrometry (MS) is the primary platform for glycomic analysis, large-scale interpretation of glycomic MS data remains heavily dependent on expert manual annotation, creating a major bottleneck. Key challenges include effective spectral denoising and reliable identification of glycan-derived tandem MS (MS2) spectra. Here, we present GlycoMsHelper, a simple and flexible R-based workflow for positive-ion mode glycomic MS composition analysis. GlycoMsHelper integrates multiple denoising strategies with logical expression-based recognition of diagnostic fragment ion patterns to identify glycan-derived MS2 spectra. Candidate spectra are matched against glycan libraries constructed using curated biosynthetic constraints, generating traceable outputs for downstream validation. By defining appropriate parameters, the workflow can be applied to diverse glycan classes, including N-glycans, O-glycans, and glycosphingolipids (GSLs). Because each module operates independently, produces standardized outputs, and is supported by a graphical user interface, GlycoMsHelper can be readily integrated into existing glycomic analysis workflows. In an N-glycan MS dataset containing 43 expert-curated compositions, GlycoMsHelper correctly identified 42 compositions. Application to GSL and O-glycan datasets further demonstrated its versatility and enabled identification of glycan compositions overlooked during manual annotation. GlycoMsHelper is particularly useful for exploratory analysis of newly acquired or previously uncharacterized datasets lacking established reference libraries, providing an accessible and practical solution for semi-automated composition-level interpretation of glycomic MS data.