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Related Concept Videos

Protein Glycosylation01:25

Protein Glycosylation

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Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
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Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference.

Elisa Benedetti1,2, Nathalie Gerstner2,3, Maja Pučić-Baković4

  • 1Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10022, USA.

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|July 8, 2020
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Summary

This study evaluates glycomics data normalization methods using Gaussian Graphical Models to assess biological accuracy. The Probabilistic Quotient method followed by log-transformation is recommended for all glycomics platforms.

Keywords:
data normalizationgaussian graphical modelsglycomics

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Area of Science:

  • Glycoscience
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput glycomics data exhibit technical variations requiring normalization.
  • Systematic evaluations of glycomics normalization strategies are lacking.
  • Gaussian Graphical Models (GGMs) can identify glycan synthesis pathways from glycomics data.

Purpose of the Study:

  • To systematically evaluate and compare different normalization strategies for glycomics data.
  • To introduce a novel method for assessing normalization quality using biological inference.
  • To provide data-driven recommendations for glycomics data normalization.

Main Methods:

  • Assessed 23 normalization combinations across six glycomics cohorts and three platforms (LC-ESI-MS, UHPLC-FLD, MALDI-FTICR-MS).
  • Utilized GGMs to quantify normalization quality based on the reconstruction of known glycosylation pathways.
  • Validated normalization performance by analyzing associations with age, a known factor influencing glycomics.

Main Results:

  • The 'Probabilistic Quotient' method combined with log-transformation demonstrated superior performance across all analyzed platforms.
  • This normalization approach effectively preserved biological signals relevant to glycosylation pathways.
  • The recommended method showed robust statistical associations with age, reinforcing its biological relevance.

Conclusions:

  • The Probabilistic Quotient method followed by log-transformation is the recommended normalization strategy for glycomics data, regardless of the experimental platform.
  • This approach offers a biologically validated method for improving the quality of glycomics datasets.
  • The findings provide essential guidance for researchers working with glycomics data to ensure accurate downstream analysis.