GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

Toan K Phung; Cassandra L Pegg; Benjamin L Schulz

doi:10.3762/bjoc.16.180

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

Beilstein J Org Chem. 2020 Sep 1:16:2127-2135. doi: 10.3762/bjoc.16.180. eCollection 2020.

Authors

Toan K Phung^#¹, Cassandra L Pegg^#¹, Benjamin L Schulz^{1

2}

Affiliations

¹ School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, 4072, Australia.
² ARC Training Centre for Biopharmaceutical Innovation, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia.

^# Contributed equally.

Abstract

Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

Keywords: N-glycosylation; O-glycosylation; Python; glycoproteomics; mass spectrometry.

Grants and funding

This work was funded by an Australian Research Council Discovery Project DP160102766 to BLS, an Australian Research Council Industrial Transformation Training Centre IC160100027 to BLS, and a National Health and Medical Research Council Ideas Grant APP1186699 to BLS and CLP.