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Mass Spectrometric Analysis of Glycosphingolipid Antigens

Mass Spectrometric Analysis of Glycosphingolipid Antigens

Published on: April 16, 2013

Bioinformatics in glycomics: glycan characterization with mass spectrometric data using SimGlycan.

Arun Apte¹, Ningombam Sanjib Meitei

¹PREMIER Biosoft International, Palo Alto, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|November 3, 2009

Summary

SimGlycan automates the complex analysis of mass spectrometry (MS) data for glycan structure elucidation. This tool accurately characterizes known glycans and aids in the discovery of novel structures from MS/MS data.

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Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Published on: November 28, 2025

Area of Science:

Glycomics
Mass Spectrometry (MS)
Bioinformatics

Background:

Mass spectrometry (MS) is crucial for glycan structure characterization due to its high sensitivity and low sample requirements.
Manual interpretation of MS data for glycans is challenging due to the complexity of product ions and experimental variability.
Automated tools are needed to streamline glycan analysis from MS data.

Purpose of the Study:

To introduce SimGlycan, an automated software tool for glycan structure elucidation using mass spectrometry data.
To enable accurate characterization of known glycans and facilitate the discovery of novel glycan structures.
To provide a platform for advanced glycopeptide identification and 3D glycan structural analysis.

Main Methods:

SimGlycan accepts raw or standard mass spectrometry data files as input.
It employs MS/MS database searching and scoring techniques for glycan structure prediction.
The tool allows users to draw novel glycans and map them onto experimental spectra for comparison.

Main Results:

SimGlycan accurately characterizes associated glycan structures from MS data.
The software facilitates the prediction of novel glycan structures.
It enables assessment of the proximity between theoretical and experimental glycan structures.

Conclusions:

SimGlycan offers an accurate and efficient automated solution for glycan structure elucidation from MS data.
The tool simplifies the complex process of manual MS data interpretation.
SimGlycan serves as a foundation for developing future glycomics analysis tools.