How it works
Five intensities, one reproducible classification
Upload a CSV containing the intensities at m/z 126, 138, 144, 168, and 186. HexNAcQuest normalizes each row and applies the same fitted model used by the original Shiny application.
Static, private glycoform analysis
HexNAcQuest
Distinguish O-GlcNAc and O-GalNAc signals from diagnostic oxonium-ion intensities using the published logistic regression model.
Privacy
Your data stays in your browser
The CSV parser and prediction model are downloaded as static website files. Your selected data is not uploaded to oglcnac.org, shinyapps.io, or a prediction API.
You can save the result as CSV after prediction.
References
Li W. et al. HexNAcQuest: A Tool to Distinguish O-GlcNAc and O-GalNAc. Journal of the American Society for Mass Spectrometry 2022, 33 (10), 2008–2012. DOI: 10.1021/jasms.2c00172
Hou C. et al. Integrating HexNAcQuest with glycoproteomics data analysis software to distinguish HexNAc isomers on peptides. Methods in Molecular Biology 2024, 2836, 67–76. DOI: 10.1007/978-1-0716-4007-4_5