An ontology approach to comparative phenomics in plants

Handle URI:
http://hdl.handle.net/10754/346699
Title:
An ontology approach to comparative phenomics in plants
Authors:
Oellrich, Anika; Walls, Ramona L; Cannon, Ethalinda KS; Cannon, Steven B; Cooper, Laurel; Gardiner, Jack; Gkoutos, Georgios V; Harper, Lisa; He, Mingze; Hoehndorf, Robert ( 0000-0001-8149-5890 ) ; Jaiswal, Pankaj; Kalberer, Scott R; Lloyd, John P; Meinke, David; Menda, Naama; Moore, Laura; Nelson, Rex T; Pujar, Anuradha; Lawrence, Carolyn J; Huala, Eva
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. © Oellrich et al.; licensee BioMed Central.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computational Bioscience Research Center (CBRC)
Citation:
An ontology approach to comparative phenomics in plants 2015, 11 (1) Plant Methods
Publisher:
BioMed Central
Journal:
Plant Methods
Issue Date:
25-Feb-2015
DOI:
10.1186/s13007-015-0053-y
PubMed ID:
25774204
PubMed Central ID:
PMC4359497
Type:
Article
ISSN:
1746-4811
Additional Links:
http://www.plantmethods.com/content/11/1/10
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorOellrich, Anikaen
dc.contributor.authorWalls, Ramona Len
dc.contributor.authorCannon, Ethalinda KSen
dc.contributor.authorCannon, Steven Ben
dc.contributor.authorCooper, Laurelen
dc.contributor.authorGardiner, Jacken
dc.contributor.authorGkoutos, Georgios Ven
dc.contributor.authorHarper, Lisaen
dc.contributor.authorHe, Mingzeen
dc.contributor.authorHoehndorf, Roberten
dc.contributor.authorJaiswal, Pankajen
dc.contributor.authorKalberer, Scott Ren
dc.contributor.authorLloyd, John Pen
dc.contributor.authorMeinke, Daviden
dc.contributor.authorMenda, Naamaen
dc.contributor.authorMoore, Lauraen
dc.contributor.authorNelson, Rex Ten
dc.contributor.authorPujar, Anuradhaen
dc.contributor.authorLawrence, Carolyn Jen
dc.contributor.authorHuala, Evaen
dc.date.accessioned2015-03-16T05:29:38Zen
dc.date.available2015-03-16T05:29:38Zen
dc.date.issued2015-02-25en
dc.identifier.citationAn ontology approach to comparative phenomics in plants 2015, 11 (1) Plant Methodsen
dc.identifier.issn1746-4811en
dc.identifier.pmid25774204en
dc.identifier.doi10.1186/s13007-015-0053-yen
dc.identifier.urihttp://hdl.handle.net/10754/346699en
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.en
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.plantmethods.com/content/11/1/10en
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.en
dc.titleAn ontology approach to comparative phenomics in plantsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalPlant Methodsen
dc.identifier.pmcidPMC4359497en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionWellcome Trust Sanger Institute, Wellcome Trust Genome Campusen
dc.contributor.institutioniPlant Collaborative, University of Arizonaen
dc.contributor.institutionDepartment of Electrical and Computer Engineering Iowa State Universityen
dc.contributor.institutionUSDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State Universityen
dc.contributor.institutionDepartment of Agronomy, Agronomy Hall, Iowa State Universityen
dc.contributor.institutionDepartment of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State Universityen
dc.contributor.institutionDepartment of Genetics, Development and Cell Biology, Roy J Carver Co-Laboratory, Iowa State Universityen
dc.contributor.institutionDepartment of Computer Science, Aberystwyth Universityen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoehndorf, Roberten

Related articles on PubMed

All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.