An ontology approach to comparative phenomics in plants

Persistent Link:
http://hdl.handle.net/10150/610328
Title:
An ontology approach to comparative phenomics in plants
Author:
Oellrich, A.; Walls, R. L.; Cannon, E. K.; Cannon, S. B.; Cooper, L.; Gardiner, J.; Gkoutos, G. V.; Harper, L.; He, M.; Hoehndorf, R.; Jaiswal, P.; Kalberer, S. R.; Lloyd, J. P.; Meinke, D.; Menda, N.; Moore, L.; Nelson, R. T.; Pujar, A.; Lawrence, C. J.; Huala, E.
Affiliation:
Wellcome 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; Phoenix Bioinformatics
Issue Date:
2015
Publisher:
BioMed Central
Citation:
Oellrich et al. Plant Methods (2015) 11:10 DOI 10.1186/s13007-015-0053-y
Journal:
Plant Methods
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 Information:
This 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 repository@u.library.arizona.edu.
Abstract:
BACKGROUND: 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.
EISSN:
1746-4811
PubMed ID:
25774204
PubMed Central ID:
PMC4359497
DOI:
10.1186/s13007-015-0053-y [doi]
Version:
Final published version
Additional Links:
http://plantmethods.biomedcentral.com/articles/10.1186/s13007-015-0053-y

Full metadata record

DC FieldValue Language
dc.contributor.authorOellrich, A.en
dc.contributor.authorWalls, R. L.en
dc.contributor.authorCannon, E. K.en
dc.contributor.authorCannon, S. B.en
dc.contributor.authorCooper, L.en
dc.contributor.authorGardiner, J.en
dc.contributor.authorGkoutos, G. V.en
dc.contributor.authorHarper, L.en
dc.contributor.authorHe, M.en
dc.contributor.authorHoehndorf, R.en
dc.contributor.authorJaiswal, P.en
dc.contributor.authorKalberer, S. R.en
dc.contributor.authorLloyd, J. P.en
dc.contributor.authorMeinke, D.en
dc.contributor.authorMenda, N.en
dc.contributor.authorMoore, L.en
dc.contributor.authorNelson, R. T.en
dc.contributor.authorPujar, A.en
dc.contributor.authorLawrence, C. J.en
dc.contributor.authorHuala, E.en
dc.date.accessioned2016-05-20T09:04:19Z-
dc.date.available2016-05-20T09:04:19Z-
dc.date.issued2015en
dc.identifier.citationOellrich et al. Plant Methods (2015) 11:10 DOI 10.1186/s13007-015-0053-yen
dc.identifier.pmid25774204en
dc.identifier.doi10.1186/s13007-015-0053-y [doi]en
dc.identifier.urihttp://hdl.handle.net/10150/610328-
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.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://plantmethods.biomedcentral.com/articles/10.1186/s13007-015-0053-yen
dc.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)en
dc.titleAn ontology approach to comparative phenomics in plantsen
dc.typeArticleen
dc.identifier.eissn1746-4811en
dc.contributor.departmentWellcome Trust Sanger Institute, Wellcome Trust Genome Campusen
dc.contributor.departmentiPlant Collaborative, University of Arizonaen
dc.contributor.departmentDepartment of Electrical and Computer Engineering, Iowa State Universityen
dc.contributor.departmentUSDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State Universityen
dc.contributor.departmentCrop Genome Informatics Lab, Iowa State Universityen
dc.contributor.departmentDepartment of Agronomy, Agronomy Hall, Iowa State Universityen
dc.contributor.departmentDepartment of Botany and Plant Pathology, Oregon State Universityen
dc.contributor.departmentDepartment of Genetics, Development and Cell Biology, Roy J. Carver Co-Laboratory, Iowa State Universityen
dc.contributor.departmentDepartment of Computer Science, Aberystwyth Universityen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technologyen
dc.contributor.departmentDepartment of Plant Biology, Michigan State Universityen
dc.contributor.departmentDepartment of Botany, Oklahoma State Universityen
dc.contributor.departmentBoyce Thompson Institute for Plant Researchen
dc.contributor.departmentPhoenix Bioinformaticsen
dc.identifier.journalPlant Methodsen
dc.identifier.pmcidPMC4359497en
dc.description.collectioninformationThis 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 repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen

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