GlycoDraw : A Python Implementation for Generating High-Quality Glycan Figures

The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFG) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g., differential abundance data or glycomics mass spectra.

Example applications of GlycoDraw. (A) The annotate_figure function modifies
matplotlib-generated svg figures by replacing text labels with glycan images (B) Annotated heatmap
visualization of glycan abundance between two groups based on simulated example data. (C) Annotated
volcano plot visualization of differential glycan abundance quantification based on simulated example
data. (D) Example milk oligosaccharide biosynthetic network showing the relation between enzymatic
reactions and selected structures. (E) MS/MS spectrum of Neu5Gc(a2-3)Gal(b1-3)Gal(b1-4)Glc from
(Jin et al. 2023), showing the integration of the Domon-Costello nomenclature with GlycoDraw. Lowabundance peaks in grey were not assigned any fragment structure.