GENETIC MECHANISMS AND MOLECULAR GENETIC RESOURCES FOR MAIZE
Location: Plant Genetics Research
Title: Computable visually observed phenotype ontological framework for plants
| Harnsomburana, Jaturon - |
| Green, Jason - |
| Barb, Adrian - |
| Vincent, Leszek - |
| Shyu, Chi-Ren - |
Submitted to: BMC Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 24, 2011
Publication Date: July 22, 2011
Citation: Harnsomburana, J., Green, J., Barb, A., Schaeffer, M.L., Vincent, L., Shyu, C. 2011. Computable visually observed phenotype ontological framework for plants. BMC Bioinformatics. 12:260. Available: http://www.biomedcentral.com/1471-2105/12/260.
Interpretive Summary: The ability to search for and precisely compare similar phenotypic appearances within and across model species and crop plants has vast potential in plant breeding and in genetic and basic biology research. A main difficulty to achieving this capability lies in the fact that visually observed phenotypes often cannot be directly measured quantitatively or described precisely. Text descriptions are plagued by ambiguity, lack of detail and inconsistencies in spite of several structured controlled vocabularies (ontologies) that have been developed to standardize descriptions across model species and crop plants. This work provides a novel quantitative view that leverages existing ontologies, text descriptions and images of plant phenotypes in a computer-interpretable form. The applicability of the framework has been demonstrated in two different crops, maize and tomato, and shows how plant breeders and basic researchers of different crops can adopt the framework.
The ability to search for and precisely compare similar phenotypic appearances within and across differenct crop plants has vast potential in plant breeding, and in basic science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and make it interoperable across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant breeding and basic science community.