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dc.contributor.authorOellrich, Anika
dc.contributor.authorWalls, Ramona L
dc.contributor.authorCannon, Ethalinda KS
dc.contributor.authorCannon, Steven B
dc.contributor.authorCooper, Laurel
dc.contributor.authorGardiner, Jack
dc.contributor.authorGkoutos, Georgios V
dc.contributor.authorHarper, Lisa
dc.contributor.authorHe, Mingze
dc.contributor.authorHoehndorf, Robert
dc.contributor.authorJaiswal, Pankaj
dc.contributor.authorKalberer, Scott R
dc.contributor.authorLloyd, John P
dc.contributor.authorMeinke, David
dc.contributor.authorMenda, Naama
dc.contributor.authorMoore, Laura
dc.contributor.authorNelson, Rex T
dc.contributor.authorPujar, Anuradha
dc.contributor.authorLawrence, Carolyn J
dc.contributor.authorHuala, Eva
dc.date.accessioned2015-03-16T05:29:38Z
dc.date.available2015-03-16T05:29:38Z
dc.date.issued2015-02-24
dc.identifier.citationAn ontology approach to comparative phenomics in plants 2015, 11 (1) Plant Methods
dc.identifier.issn1746-4811
dc.identifier.pmid25774204
dc.identifier.doi10.1186/s13007-015-0053-y
dc.identifier.urihttp://hdl.handle.net/10754/346699
dc.description.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. © Oellrich et al.; licensee BioMed Central.
dc.publisherSpringer Nature
dc.relation.urlhttp://www.plantmethods.com/content/11/1/10
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.titleAn ontology approach to comparative phenomics in plants
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalPlant Methods
dc.identifier.pmcidPMC4359497
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionWellcome Trust Sanger Institute, Wellcome Trust Genome Campus
dc.contributor.institutioniPlant Collaborative, University of Arizona
dc.contributor.institutionDepartment of Electrical and Computer Engineering Iowa State University
dc.contributor.institutionUSDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State University
dc.contributor.institutionDepartment of Agronomy, Agronomy Hall, Iowa State University
dc.contributor.institutionDepartment of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University
dc.contributor.institutionDepartment of Genetics, Development and Cell Biology, Roy J Carver Co-Laboratory, Iowa State University
dc.contributor.institutionDepartment of Computer Science, Aberystwyth University
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personHoehndorf, Robert
refterms.dateFOA2018-06-14T04:43:33Z
dc.date.published-online2015-02-24
dc.date.published-print2015


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