OntologicalDiscovery.org: A web resource for the empirical discovery of phenotypic relations across species and experimental systems
- Baylor University
- University of Tennessee, Knoxville (UTK)
- ORNL
The Ontological Discovery Environment ( http://ontologicaldiscovery.org ) is a free, public Internet resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets. The intent of this resource is to allow the creation of user-defined phenotype categories based on naturally and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and semantics such as disease names and processes. By extracting the relationships of complex processes from the technology that produces those relationships, this resource meets a growing demand for data integration and hypothesis discovery across multiple experimental contexts, including broad species and phenotype domains. At a highly processed level, analyses of set similarity, distance and hierarchical relations are performed through a modular suite of tools. The core pivot point of analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to gene-set analysis which enables set-set matching of non-referential data. The central organizing metaphor of a gene set may be created, stored and curated by individual users, shared among virtual working groups, or made publicly available. Gene sets submission incorporates a variety of accession numbers, microarray feature IDs, and gene symbols from model organisms, allowing integration across experimental platforms, literature reviews and other genomic analyses. The sets themselves are annotated with several levels of metadata which may include an unstructured description, publication information and structured community ontologies for anatomy, process and function. Gene set translation to user chosen reference species through gene homology allows translational comparison of models regardless of the face validity of the experimental systems. In addition, computationally derived gene sets can be integrated into phenome interdependency and similarity hierarchy graphs, which are hierarchical trees of phenotypes based on the genes to which they are associated. This provides an empirical discovery of the natural phenotype ontology.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Mouse Genetics Research Facility
- Sponsoring Organization:
- USDOE Office of Science (SC); Work for Others (WFO)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 986769
- Journal Information:
- PLoS Computational Biology, Vol. 94, Issue 6
- Country of Publication:
- United States
- Language:
- English
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