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Updated: Oct 24, 2025
A Quantitative Glycomics and Proteomics Combined Purification Strategy
Published on: March 8, 2016
gQuant, an Automated Tool for Quantitative Glycomic Data Analysis
Jiangming Huang1,2, Biyun Jiang1, Mingqi Liu1
1The Fifth People's Hospital, Fudan University, and the Shanghai Key Laboratory of Medical Epigenetics, The International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Abstract:
MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.
