Walls, R. L.
Cannon, E. K.
Cannon, S. B.
Gkoutos, G. V.
Kalberer, S. R.
Lloyd, J. P.
Nelson, R. T.
Lawrence, C. J.
AffiliationWellcome Trust Sanger Institute, Wellcome Trust Genome Campus
iPlant Collaborative, University of Arizona
Department of Electrical and Computer Engineering, Iowa State University
USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University
Crop Genome Informatics Lab, Iowa State University
Department of Agronomy, Agronomy Hall, Iowa State University
Department of Botany and Plant Pathology, Oregon State University
Department of Genetics, Development and Cell Biology, Roy J. Carver Co-Laboratory, Iowa State University
Department of Computer Science, Aberystwyth University
Computer, Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology
Department of Plant Biology, Michigan State University
Department of Botany, Oklahoma State University
Boyce Thompson Institute for Plant Research
MetadataShow full item record
CitationOellrich et al. Plant Methods (2015) 11:10 DOI 10.1186/s13007-015-0053-y
Rights© 2015 Oellrich et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
Collection InformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at email@example.com.
AbstractBACKGROUND: Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS: We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS: The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.
PubMed Central IDPMC4359497
VersionFinal published version
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