A Novel Glycoproteomics Platform for High-Throughput Identification of Disease-Associated Glycoforms
Journal:
bioRxiv
Published Date:
Mar 9, 2026
Abstract
Glycosylation is a critical post-translational modification, and its aberrant forms are potent disease biomarkers; however, the comprehensive, site-specific identification of all glycosites and glycoforms across an entire proteome remains prohibitively slow and computationally demanding. To address this bottleneck and accelerate biomarker discovery, we introduce the Glycoproteomics Data Analysis Software (GDAS), a novel, high-throughput platform designed to provide confident, proteome-scale identification of disease-specific glycoforms. GDAS streamlines the analysis through a core, multi-step workflow: it initially employs an ultrafast open search (e.g., MSFragger-Glyco) on mass spectrometry data to rapidly screen and statistically reduce the vast proteome database to a manageable subset of significantly regulated glycoproteins, conserving computational resources for subsequent, in-depth, targeted N- and O-glycosylation analysis using specialized tools (e.g., GlycReSoft and O-Pair). Furthermore, a unique Final Analysis Module utilizes an advanced statistical and machine learning pipeline (incorporating Bootstrap/Bayesian methods, XGBoost, and Random Forest) to integrate quantitative results and generate a robust, comprehensive glycosylation score. We demonstrate GDAS's power to recognize biologically relevant glycosylation changes in targeted proteins by validating it using published Alzheimer's disease data. GDAS can be downloaded from https://github.com/yang-lab/GDAS.
