DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Michael Ku Yu; Jianzhu Ma; Keiichiro Ono; Fan Zheng; Samson H Fong; Aaron Gary; Jing Chen; Barry Demchak; Dexter Pratt; Trey Ideker

doi:10.1016/j.cels.2019.02.003

DDOT: A Swiss Army Knife for Investigating Data-Driven Biological Ontologies

Cell Syst. 2019 Mar 27;8(3):267-273.e3. doi: 10.1016/j.cels.2019.02.003. Epub 2019 Mar 13.

Authors

Affiliations

¹ Department 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.
² Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
³ Department of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
⁴ Department 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; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA. Electronic address: tideker@ucsd.edu.

Abstract

Systems biology requires not only genome-scale data but also methods to integrate these data into interpretable models. Previously, we developed approaches that organize omics data into a structured hierarchy of cellular components and pathways, called a "data-driven ontology." Such hierarchies recapitulate known cellular subsystems and discover new ones. To broadly facilitate this type of modeling, we report the development of a software library called the Data-Driven Ontology Toolkit (DDOT), consisting of a Python package (https://github.com/idekerlab/ddot) to assemble and analyze ontologies and a web application (http://hiview.ucsd.edu) to visualize them. Using DDOT, we programmatically assemble a compendium of ontologies for 652 diseases by integrating gene-disease mappings with a gene similarity network derived from omics data. For example, the ontology for Fanconi anemia describes known and novel disease mechanisms in its hierarchy of 194 genes and 74 subsystems. DDOT provides an easy interface to share ontologies online at the Network Data Exchange.

Keywords: disease network; fanconi anemia; gene ontology; hierarchical; interaction network; multi-scale.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Biological Ontologies*
Computational Biology / methods*
Gene Ontology
Gene Regulatory Networks*
Humans
Software*

Abstract

Publication types

MeSH terms

Grants and funding