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Updated: Feb 10, 2026
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Glycan Profiling of Plant Cell Wall Polymers using Microarrays
Published on: December 17, 2012
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Application of network smoothing to glycan LC-MS profiling
Joshua Klein1, Luis Carvalho1,2, Joseph Zaia1,3
1Program for Bioinformatics, Boston University, Boston, MA, USA.
Bioinformatics (Oxford, England)
|May 24, 2018
Summary
We developed a new algorithm to identify glycan compositions from mass spectrometry data by analyzing relationships between glycans. This method improves sensitivity and accuracy for glycomics analysis.
Area of Science:
- Glycomics
- Mass Spectrometry
- Computational Biology
Background:
- Glycosylation is a complex post-translational modification.
- Liquid chromatography coupled mass spectrometry (LC-MS) is crucial for high-throughput analysis.
- Existing tools lack sensitivity due to not considering glycan relationships.
Purpose of the Study:
- To develop an algorithm for accurate glycan composition assignment from LC-MS data.
- To improve the sensitivity of glycomics analysis by incorporating glycan network relationships.
- To provide a more representative solution for identifying biological molecules.
Main Methods:
- Developed a novel algorithm leveraging biosynthetic network relationships among glycans.
- Optimized likelihood scoring functions based on glycan chemical properties.
- Employed network Laplacian regularization and optional prior information for smoothing.
Main Results:
- Identified as many or more glycan compositions compared to previous methods.
- Demonstrated increased sensitivity through network regularization.
- The method is adaptable to various glycan families (N-glycans, O-glycans, heparan sulfate).
Conclusions:
- The developed algorithm enhances glycan identification from LC-MS data.
- Incorporating glycan network relationships significantly improves analytical sensitivity.
- The method offers a robust and adaptable tool for glycomics research.
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