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DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies
Michael Ku Yu1, Jianzhu Ma2, Keiichiro Ono2
1Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Graduate Program in Bioinformatics and Systems Biology, University of California, San Diego, La Jolla, CA 92093, USA; Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
Cell Systems
|March 18, 2019
Summary
We developed the Data-Driven Ontology Toolkit (DDOT) to organize omics data into interpretable models for systems biology research. DDOT facilitates the assembly and visualization of disease ontologies, aiding in the discovery of novel biological mechanisms.
Area of Science:
- Systems biology
- Computational biology
- Bioinformatics
Background:
- Systems biology necessitates integrating large-scale omics data into interpretable models.
- Previous work established methods to structure omics data into hierarchical "data-driven ontologies."
Purpose of the Study:
- To develop and release a software library, the Data-Driven Ontology Toolkit (DDOT), for broad application in systems biology.
- To facilitate the assembly, analysis, and visualization of data-driven ontologies.
Main Methods:
- Developed a Python package for ontology assembly and analysis.
- Created a web application for ontology visualization.
- Integrated gene-disease mappings with omics-derived gene similarity networks to build ontologies.
Main Results:
- Assembled a compendium of 652 disease ontologies using DDOT.
- Demonstrated the utility of DDOT with an example ontology for Fanconi anemia, detailing 194 genes and 74 subsystems.
- Enabled sharing of ontologies via the Network Data Exchange.
Conclusions:
- DDOT provides a user-friendly platform for creating and sharing biological knowledge.
- The toolkit supports the discovery of known and novel disease mechanisms through data-driven ontologies.
- Facilitates systems biology modeling by organizing complex omics data.
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