Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Proteomic Analysis Identifies ATE1-Dependent Arginylation Dysregulation across Meningioma Grades.

Journal of proteome research·2026

Same author

Machine learning-enhanced plasma proteomics discriminates pancreatic cancer-associated diabetes from type 2 diabetes mellitus.

Journal of proteomics·2026

Same author

Molecular Characterization of Calu-3 Cells from Submerged to Air-Liquid Interface to Model Lung Infections.

Journal of proteome research·2026

Same author

Rapid high-throughput antibody analysis using microwave-assisted digestion.

Journal of proteomics·2026

Same author

Emerging Technologies in Proteomics: Insights from the HUPO ETC Webinar Series.

Journal of proteome research·2026

Same author

<i>N</i>-Glycoproteomic Portraits of <i>Bothrops</i> Snake Venoms Reveal Evolutionarily Conserved and Divergent Phenotypes.

Journal of proteome research·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2025

A Quantitative Glycomics and Proteomics Combined Purification Strategy

A Quantitative Glycomics and Proteomics Combined Purification Strategy

Published on: March 8, 2016

Glycosort: A Computational Solution to Post-process Quantitative Large-Scale Intact Glycopeptide Analyses.

Lucas C Lazari¹, Veronica Feijoli Santiago¹, Gilberto S de Oliveira¹

¹Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.

Advances in Experimental Medicine and Biology

|February 27, 2024

Summary

This study introduces a Python script to automate glycoproteomic data analysis, integrating Byonic and MaxQuant outputs. The tool helps researchers efficiently assess protein glycosylation heterogeneity and site occupancy.

Keywords:

Computational platform Glycans Glycoproteomics Mass spectrometry Quantitative analysis

More Related Videos

Glycopeptide Capture for Cell Surface Proteomics

Glycopeptide Capture for Cell Surface Proteomics

Published on: May 9, 2014

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Jul 2, 2025

A Quantitative Glycomics and Proteomics Combined Purification Strategy

A Quantitative Glycomics and Proteomics Combined Purification Strategy

Published on: March 8, 2016

Glycopeptide Capture for Cell Surface Proteomics

Glycopeptide Capture for Cell Surface Proteomics

Published on: May 9, 2014

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Published on: February 7, 2025

Area of Science:

Biochemistry and Molecular Biology
Proteomics
Glycoscience

Background:

Protein glycosylation, a key post-translational modification, significantly impacts protein function and biological processes like cell recognition and immune response.
The immense diversity of glycan structures and their attachment sites creates complexity in understanding glycosylation's role.
Assessing glycosylation heterogeneity and site occupancy is crucial for deciphering glycan-mediated biological functions and alterations.

Purpose of the Study:

To develop an automated computational tool for analyzing complex glycoproteomic data.
To streamline the integration of identification and quantitative data from common glycoproteomics software.
To enable efficient calculation of site occupancy and comparison of glycan structures between sample groups.

Main Methods:

Development of a Python script to parse and integrate output files from Byonic and MaxQuant.
Implementation of algorithms for calculating glycan site occupancy percentages.
Facilitation of comparative analysis of glycan structures and site occupancies across different experimental groups.

Main Results:

The automated script successfully integrates Byonic and MaxQuant data for glycoproteomic analysis.
The tool enables accurate calculation of site occupancy percentages for various glycans.
Researchers can now efficiently compare glycan profiles and site occupancies between two groups, simplifying data interpretation.

Conclusions:

The developed Python script provides an effective solution for automating high-throughput quantitative glycoproteomic data analysis.
This tool significantly reduces manual data processing, allowing researchers to better understand glycosylation heterogeneity and its biological implications.
The script empowers researchers to organize and interpret complex glycoproteomic datasets more effectively.