GlyTrait: A Versatile Bioinformatics Tool for Glycomics Analysis

We developed GlyTrait, a Python-based framework designed to enhance Glycomics analysis through the innovative calculation and interpretation of derived traits from <i>N</i>-glycome data. Glycomics research often grapples with the interpretability and biological relevance of Glycomics data. GlyTrait automates the derivation of biologically significant traits, shifting focus from mere glycan abundances in compositional or structural Glycomics studies to functional glycan properties such as branching and fucosylation. Furthermore, with the well-designed formula grammar, custom-derived traits can be materialized without any knowledge of coding. Finally, subsequent statistical and interpretable machine learning analyses provide robust insights into the glycosylation patterns associated with disease states. GlyTrait's efficacy is demonstrated through the reanalysis of published glycoengineered CHO cell lines and visceral leishmaniasis patient data, alongside a newly conducted pilot study for hepatocellular carcinoma (HCC) <i>N</i>-glycan biomarker discovery. We are confident in GlyTrait's potential to become an indispensable tool for the Glycomics community.