neo4jsbml
neo4jsbml imports Systems Biology Markup Language (SBML) models into Neo4j graph databases to represent biological entities and interactions as nodes and relationships for graph-based analysis.
Key Features:
- Integration with Neo4j: Maps SBML model components to Neo4j graph elements, representing biological entities as nodes and interactions as relationships.
- Graphical Data Representation: Translates hierarchical SBML organization into a graph format to reflect interdependencies within models.
- User-Defined Schema: Supports user-defined schemas to control which SBML elements are imported into the Neo4j database.
- Cypher Query Support: Enables traversal and analysis of imported models using Neo4j's Cypher query language.
- Selective Loading and Performance: Allows selective import of relevant data from SBML to optimize database content and performance.
Scientific Applications:
- Metabolic model analysis: Enables representation and analysis of metabolic models as graphs for exploration of reactions, metabolites, and their relationships.
- Model sharing and interoperability: Facilitates exchange of SBML-encoded models by converting them into Neo4j graph representations that preserve structure and relationships.
- Network visualization and traversal: Supports visualization-ready graph representations and graph traversal analyses of complex biological networks.
Methodology:
Imports SBML into Neo4j, maps SBML entities to nodes and interactions to relationships, translates SBML hierarchical structure into a graph format, supports user-defined schemas for selective loading, and enables traversal and analysis via Cypher.
Topics
Details
- Cost:
- Free of charge
- Tool Type:
- library
- Programming Languages:
- Python
- Added:
- 5/14/2024
- Last Updated:
- 5/14/2024
Operations
Publications
Gricourt G, Duigou T, Dérozier S, Faulon J. neo4jsbml: import systems biology markup language data into the graph database Neo4j. PeerJ. 2024;12:e16726. doi:10.7717/peerj.16726. PMID:38250720. PMCID:PMC10798154.
DOI: 10.7717/peerj.16726
PMID: 38250720
PMCID: PMC10798154
Funding: - A French government grant managed by the Agence Nationale de la Recherche under the France 2030 program: ANR-22-PEBB-0008