massMatchR : a Shiny application for glycomics mass spectrometry data analysis

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ID: 329579
2026
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Abstract
MOTIVATION: The identification and analysis of glycans using MALDI-TOF mass spectrometry is a critical task in glycomics research, yet it often requires complex data interpretation and manual processing. Existing software tools frequently lack automated solutions for efficient glycan annotation, data structuring and grouping, and semi-quantitative evaluation, making large-scale glycan analysis challenging. To address these limitations, we developed massMatchR, an open-source software tool designed to streamline the identification and quantification of glycans in MALDI-TOF spectra. RESULTS: massMatchR automates glycan identification by mapping experimentally detected m/z values from preprocessed MALDI-TOF-MS datasets to glycans from a user-defined database. The software provides both visual and tabular outputs, enabling rapid and accurate glycan interpretation. Additionally, massMatchR facilitates the export of structured data tables (e.g., Microsoft Excel) with predefined fields for m/z and intensity, supporting further analysis. A key feature of the software is its ability to perform semi-quantitative evaluation based on relative intensity calculations, allowing for comparisons across multiple samples. Freely available at http://www.imb.savba.sk/soft/massMatchR/ and on GitHub implemented in R, massMatchR offers a fast, reproducible, and customizable computational framework for glycomics and MALDI-TOF -based glycan analysis. AVAILABILITY: An implementation code is available on Github at https://github.com/bekegbor/massMatchR and on Zenodo at https://doi.org/10.5281/zenodo.20697072. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7214097522 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gábor Beke, Ľuboš Kľúčár, Zuzana Pakanová, Rebeka Kodríková, Péter Baráth, Marek Nemčovič
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag700
URL
https://doi.org/10.1093/bioinformatics/btag700
Keywords Keywords not found

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