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Updated: Jul 10, 2025

A Quantitative Glycomics and Proteomics Combined Purification Strategy

A Quantitative Glycomics and Proteomics Combined Purification Strategy

Published on: March 8, 2016

Decoding glycomics with a suite of methods for differential expression analysis.

Jon Lundstrøm¹, James Urban¹, Daniel Bojar¹

¹Department of Chemistry and Molecular Biology, University of Gothenburg, 41390 Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.

Cell Reports Methods

|November 22, 2023

Summary

This summary is machine-generated.

This study introduces a new framework for analyzing glycomics data, simplifying the complex process of differential glycan expression analysis. The open-source glycowork package offers robust tools for advancing glycomics research and clinical applications.

Keywords:

CP: systems biology bioinformatics carbohydrate computational biology glycan glycomics statistics

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Area of Science:

Glycoscience
Computational Biology
Bioinformatics

Background:

Glycomics offers insights into biological processes and diseases but faces challenges in data interpretation, particularly for differential expression analysis.
The complexity of glycan structures hinders comprehensive analysis and understanding of their roles in health and disease.

Purpose of the Study:

To develop a comprehensive framework for differential glycomics expression analysis.
To provide user-friendly, open-source tools for robust glycomics data interpretation and analysis.
To facilitate the translation of glycomics research into clinical and diagnostic applications.

Main Methods:

A framework integrating specialized methods for data normalization, imputation, and glycan motif extraction.
Differential expression analysis, motif enrichment, time series analysis, and meta-analytic capabilities.
Implementation within the open-source glycowork package for accessible and performant workflows.

Main Results:

Demonstrated the framework's efficacy using simulations and diverse glycomics datasets (N-, O-, lipid-linked, free glycans).
Successfully applied differential expression tests to human datasets, comparing cancer versus healthy tissues.
Validated the robustness and reliability of the proposed analytical approach.

Conclusions:

The developed framework and glycowork package enable robust and comprehensive differential expression analyses in glycomics.
This contributes to advancing glycomics research by overcoming data interpretation challenges.
Facilitates the translation of glycomics findings to clinical diagnostics and applications.