ArticleBMC bioinformatics2016
STON: exploring biological pathways using the SBGN standard and graph databases.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- StonPy: a tool to parse and query collections of SBGN maps in a graph database.Bioinformatics (Oxford, England) · 2023Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.Briefings in bioinformatics · 2021Article
- An overview of graph databases and their applications in the biomedical domain.Database : the journal of biological databases and curation · 2021Article
- Harmonizing semantic annotations for computational models in biology.Briefings in bioinformatics · 2019Review
- Notions of similarity for systems biology models.Briefings in bioinformatics · 2018Article
- Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.NPJ systems biology and applications · 2018Article
- Reactome graph database: Efficient access to complex pathway data.PLoS computational biology · 2018Article
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Authors and funding
8 authors.
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Abstract
backgroundWhen modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks.
resultsWe present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database.
conclusionSTON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database.
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