Semantic annotation of Glycomics and Glycoproteomics methods

TL;DR

This paper explores using semantic annotations to better describe glycomics and glycoproteomics methods, aiming to improve metadata standards in the field.

Contribution

The study introduces a graph-based annotation approach combining multiple ontologies to semantically represent glycomics and glycoproteomics workflows.

Findings

Combining multiple ontologies improves annotation precision compared to using a single ontology.

Glycomics and glycoproteomics workflows are more complex than in other scientific fields.

Current ontologies miss some concepts but cover most experimental aspects.

Abstract

Glycomics and glycoproteomics represent the systematic exploration of glycan structures and glycoprotein compositions within biological systems, aiming to elucidate their roles in physiological and pathological processes, including cancer, inflammation and infectious diseases. To support this investigation, glycomics and glycoproteomics utilize a diverse array of methodologies from molecular biology, biochemistry, analytical chemistry and bioinformatics. In this study, we investigated the semantic representation experimental workflows in glycomics and glycoproteomics publications through graph-based annotation using combination of existing domain-relevant ontologies. Rather than adhering to evolving metadata standards, this investigation explored a broad spectrum of biomedical and analytical ontologies to identify optimal annotations for the generative (e.g. sample preparation and derivatization) and transformative (e.g. separation and detection) phases of the workflow. The results show that integrating several ontologies yields more precise annotations than relying on a single one. However, several challenges arose, particularly where methodological reporting lacked critical metadata, such as derivatization conditions or glycan release protocols. Furthermore, the annotations imply that methodologies in the glycomic and glycoproteomic fields are more complex, on average, than those in other scientific fields. The results suggests that, while some specific concepts are missing in the ontologies, a limited number of ontologies adequately encompass the majority of aspects related to glycomics and glycoproteomics experiments. These can serve as a foundation for community-wide metadata standards and direct future efforts to refine and expand the ontologies for glycoscience research.

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