RDF
Definition
Resource Description Framework (RDF) is a W3C standard for representing structured information as subject-predicate-object triples, forming a graph-based data model. In life sciences, RDF enables semantic integration of heterogeneous biological data by representing entities (genes, proteins, diseases) and their relationships in a machine-readable format. RDF uses URIs to uniquely identify resources and supports ontologies like Gene Ontology and Disease Ontology. This framework is fundamental to the Semantic Web and Linked Data initiatives, allowing researchers to query across distributed biological databases using SPARQL. RDF's graph structure naturally aligns with biological networks, making it essential for integrating multi-omics data, knowledge graphs, and biomedical ontologies.
Visualize RDF in Nodes Bio
Researchers can import RDF-formatted biological knowledge graphs into Nodes Bio to visualize complex relationships between genes, proteins, pathways, and diseases. The platform can parse RDF triples to automatically generate network structures where nodes represent biological entities and edges represent semantic relationships. This enables exploration of integrated data from multiple sources like UniProt, ChEMBL, and DisGeNET in a unified visual framework.
Visualization Ideas:
- Multi-source knowledge graphs integrating protein-disease-drug relationships from RDF repositories
- Ontology-based networks showing hierarchical relationships between biological concepts
- Cross-database entity networks linking genes, pathways, and phenotypes via RDF predicates
Example Use Case
A systems biology team investigating Alzheimer's disease integrates RDF data from multiple sources: protein interactions from UniProt, genetic variants from ClinVar, and pathway information from Reactome. They use SPARQL queries to extract relevant triples connecting APOE gene variants to amyloid-beta processing pathways. By visualizing this RDF-derived network, they identify previously overlooked connections between lipid metabolism proteins and neuroinflammatory markers, revealing potential therapeutic targets that span multiple biological processes.