SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

Motivation Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii . SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae . Availability The source code is freely available on GitHub ( https://github.com/SugarPy/SugarPy ), and its implementation in Python ensures support for all operating systems. Contact [email protected] and [email protected] Supplementary information Supplementary data are available online.