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

<p>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. 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 algae <em>Chlamydomonas reinhardtii </em>and the archaeon <em>Haloferax volcanii</em>. SugarPy also facilitated the novel characterization of glycoproteins from the red alga <em>Cyanidioschyzon merolae</em>.</p> <p>Provided here are, for each species:</p> <ul> <li>input files (mzML)</li> <li>SugarPy result files</li> </ul> <p>In addition, for <em>Homo sapiens</em> and <em>Chlamydomonas reinhardtii</em>, the following is included:</p> <ul> <li>SugarQb result files</li> <li>pGlyco result files</li> <li>MSFragger-Glyco result files</li> </ul> <p>Furthermore, a SugarPy example_data folder is provided that can be used with the SugarPy example scripts.</p> <p>The source code for SugarPy can be found on GitHub: https://github.com/SugarPy/SugarPy</p>