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ARS Home » Midwest Area » Ames, Iowa » Corn Insects and Crop Genetics Research » Research » Publications at this Location » Publication #312793

Research Project: SoyBase and the Legume Clade Database

Location: Corn Insects and Crop Genetics Research

Title: An ontology approach to comparative phenomics in plants

Author
item Oellrich, Anika - Wellcome Trust Sanger Institute
item Walls, Ramona - University Of Arizona
item Cannon, Ethalinda - Iowa State University
item Cannon, Steven
item Cooper, Laurel - Oregon State University
item Gardiner, Jack - Iowa State University
item Gkoutos, Georgios - Aberystwyth University
item Harper, Elisabeth
item He, Mingze - Iowa State University
item Hoehndorf, Robert - King Abdullah University Of Science And Technology
item Jaiswal, Pankaj - Oregon State University
item Kalberer, Scott
item Lloyd, John - Michigan State University
item Meinke, David - Oklahoma State University
item Menda, Naama - Boyce Thompson Institute
item Moore, Laura - Oregon State University
item Nelson, Rex
item Pujar, Anuradha - Boyce Thompson Institute
item Lawrence, Carolyn - Iowa State University
item Huala, Eva - Phoenix Bioinformatics

Submitted to: Plant Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/5/2015
Publication Date: 2/25/2015
Citation: Oellrich, A., Walls, R.L., Cannon, E., Cannon, S.B., Cooper, L., Gardiner, J., Gkoutos, G.V., Harper, E.C., He, M., Hoehndorf, R., Jaiswal, P., Kalberer, S.R., Lloyd, J., Meinke, D., Menda, N., Moore, L., Nelson, R., Pujar, A., Lawrence, C.J., Huala, E. 2015. An ontology approach to comparative phenomics in plants. Plant Methods. 11:10. DOI: 10.1186/s13007-015-0053-y.

Interpretive Summary: Improving crop plants through plant breeding is really about changing plant characteristics or "traits" such as yield, seed nutritional aspects, or resilience against environmental stresses. Breeders and researchers working in different crops may work on similar traits without being aware of the comparable work across the various species, especially if the terms used to describe these traits are quite different in the different species, or if the same biological phenomena appear different in different species. For example, fruit size in tomato may be related to some aspect of reproductive biology in the model plant Arabidopsis, but the terms used to describe these traits would probably be different - maybe to the point of being unrecognizable. Researchers therefore need to have well-defined terms for describing all aspects of plant growth, development, and anatomy. The name for a system of such "well-defined terms" for characteristics is an "ontology." In the research described in this paper, ontology terms were used to describe the traits of plants with known mutations, in six different species: rice, maize, soybean, tomato, and the model plants Arabidopsis thaliana and Medicago truncatula. These ontology-based descriptions were then used by a computer program to identify which plant traits were most similar across these species. This work will help plant breeders and researchers to make more efficient use of research being done across a wide range of species - and in turn to produce improved plant varieties for farmers and consumers.

Technical Abstract: Plant phenotypes (observable characteristics) are described using many different formats and specialized vocabularies or "ontologies". Similar phenotypes in different species may be given different names. These differences in terms complicate phenotype comparisons across species. This research describes methods to formally describe phenotypes for six plant species: maize (Zea mays), rice (Oryza sativa), soybean (Glycine max), tomato (Solanum lycopersicum), and the model plants Arabidopsis thaliana and Medicago truncatula. The work applied the same ontologies across all six species, and used a particular syntax that could be computationally analyzed. To explore if this approach could allow reasoning across species based on phenotype similarity, Phenotypes from plants with known mutations and the known causal genes were first converted into a common format using established ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. The phenotypes were then compared and evaluated using a computer reasoning system to identify similarity among phenotypes across these species. These similarity evaluations were then compared with similarity measures derived from other comparisons, including gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. This study concludes that the combination of established ontology terms and the syntax were generally sufficient for describing complex phenotypes, and that the computer reasoning system was able to correctly identify similar phenotypes across species using these ontologies and syntax. These tools should enhance future efforts to explore the relationship between phenotypic similarity and gene function and sequence similarity among flowering plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and human health.