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

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,...

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Bibliographic Details
Main Authors: Schulze, Stefan (Author) , Oltmanns, Anne (Author) , Fufezan, Christian (Author) , Krägenbring, Julia (Author) , Mormann, Michael (Author) , Pohlschröder, Mechthild (Author) , Hippler, Michael (Author)
Format: Article (Journal)
Language:English
Published: 2020
In: Bioinformatics
Year: 2020, Volume: 36, Issue: 22/23, Pages: 5330-5336
ISSN:1367-4811
DOI:10.1093/bioinformatics/btaa1042
Online Access:Verlag, lizenzpflichtig, Volltext: https://academic.oup.com/bioinformatics/article/36/22-23/5330/6039119
Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/bioinformatics/btaa1042
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Author Notes:Stefan Schulze, Anne Oltmanns, Christian Fufezan, Julia Krägenbring, Michael Mormann, Mechthild Pohlschröder and Michael Hippler
Description
Summary: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 alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.Supplementary data are available at Bioinformatics online.
Item Description:Advance access publication: 26 December 2020
Gesehen am 13.09.2021
Physical Description:Online Resource
ISSN:1367-4811
DOI:10.1093/bioinformatics/btaa1042

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