Semantic annotation of Glycomics and Glycoproteomics methods

Area of Science:

• * Glycoscience
• * Bioinformatics
• * Analytical Chemistry

Background:

• * Glycomics and glycoproteomics systematically explore glycan structures and glycoprotein compositions.
• * These fields aim to understand roles in cancer, inflammation, and infectious diseases.
• * Diverse methodologies from molecular biology, biochemistry, and bioinformatics are employed.

Purpose of the Study:

• * To investigate semantic representation of experimental workflows in glycomics and glycoproteomics publications.
• * To identify optimal annotations for workflow phases using graph-based annotation and ontologies.
• * To assess the adequacy of existing ontologies for glycoscience research.

Main Methods:

• * Graph-based annotation of experimental workflows using domain-relevant ontologies.
• * Exploration of biomedical and analytical ontologies for generative and transformative phases.
• * Analysis of methodological reporting for metadata completeness.

Main Results:

• * Integrating multiple ontologies provided more precise annotations than single ontologies.
• * Methodological reporting often lacked critical metadata (e.g., derivatization, glycan release).
• * Glycomics and glycoproteomics methodologies appear more complex than in other scientific fields.

Conclusions:

• * A limited set of ontologies adequately covers most aspects of glycomics and glycoproteomics experiments.
• * Some specific concepts are missing in current ontologies.
• * Findings can inform community-wide metadata standards and ontology refinement for glycoscience.