GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types

Area of Science:

• Glycomics
• Biochemistry
• Computational Biology

Background:

• Glycans are crucial sugar-based polymers modifying biomolecules and mediating biological processes.
• Glycans serve as important biomarkers for various diseases.
• Accurate and rapid glycan identification is essential for glycomics research.

Purpose of the Study:

• Introduce GlyCombo, an open-source software for rapid glycan identification.
• Evaluate GlyCombo's performance across diverse datasets.
• Highlight GlyCombo's advantages over existing methods.

Main Methods:

• Developed GlyCombo, a software tool for assigning monosaccharide combinations to glycan precursor masses.
• Utilized LC-MS/MS data for glycan identification.
• Compared GlyCombo's performance against GlycoMod and analyzed specific features like multiple adduct search and off-by-one error anticipation.

Main Results:

• GlyCombo demonstrated MS vendor, derivatization, and glycan-type neutrality across six diverse datasets.
• Compositional assignments with GlyCombo were faster than the predominant approach, GlycoMod.
• Unique features reduced unassigned MS2 scans by 40% in a benchmark dataset.
• Glycan feature identification comprehensiveness was shown in Skyline.

Conclusions:

• GlyCombo is a versatile and efficient tool for glycan identification in mass spectrometry-based glycomics.
• The software offers significant improvements in speed and accuracy compared to existing tools.
• GlyCombo enhances glycomics analysis pipelines and facilitates biomarker discovery.