Making Glycan Data FAIR: Addressing the Interoperability Gap in Glycoscience

This Research Topic is part of our FAIR series. For other Research Topics in the series, please see below:FAIR Metabolomics: Open Datasets, Shared Standards, and the Road to AI-Ready DataFAIR RNA Data: Open Resources for Transcriptomics, Structure, and Regulatory Network ResearchFAIR Genomics: Standards, Repositories, and Reproducibility in Chromatin and Genome ResearchFAIR Mitochondrial Data: Open Resources for Integrative Research in Bioenergetics and DiseaseFAIR Structural Data in the Age of AI: From Deposition to DiscoveryFAIR Cell Biology Data: From Image Archives to Interoperable Signalling DatasetsGlycoscience faces one of the most acute FAIR data challenges in the molecular biosciences. The structural complexity of glycans, which are branched, non-template-driven, and highly context-dependent, has historically made standardised representation, database interoperability, and data sharing far more difficult than for proteins or nucleic acids. As a result, glycomics and glycoproteomics datasets are significantly underrepresented in FAIR data ecosystems relative to other omics fields.This Research Topic calls for contributions that advance FAIR data infrastructure for glycoscience, including the development and adoption of nomenclature standards such as GlyTouCan and SNFG, interoperable data formats for glycan structures and glycoproteomics experiments, and strategies for cross-database linking between glycan repositories and complementary biological resources. Implementation of the MIRAGE (Minimum Information Required for A Glycomics Experiment) reporting guidelines is a central focus. We also encourage submissions addressing the structured reporting of evidence basis for glycan structural assignments, enabling downstream users to assess confidence and reanalyse data with alternative interpretation approaches.We also encourage submissions addressing glycan mass spectrometry data deposition, glycoproteomics site-specific annotation, and the AI-readiness of glycan datasets for graph neural networks or glycan language models. FAIR² Data Articles presenting curated glycan or glycoproteomics datasets are especially welcome, as are methods demonstrating practical routes to interoperability that researchers can adopt immediately. KEY THEMES- Development and adoption of glycan nomenclature standards (GlyTouCan, SNFG, GlycoRDF)- MIRAGE reporting guidelines: implementation across journals and repositories- Interoperable data formats for glycan mass spectrometry and glycoproteomics experiments- Cross-database linking between glycan repositories (GlyConnect, UniCarbKB) and proteomics resources- Site-specific glycoproteomics annotation and FAIR deposition practices- Glycan biosynthesis pathway data sharing and pathway database integration- AI-ready glycan datasets for graph neural networks and glycan language models- Linking glycan structural data to biological function and clinical phenotypes Topic editors: Wei Wang Source: https://www.frontiersin.org/research-topics/82998/making-glycan-data-fair-addressing-the-interoperability-gap-in-glycoscience