An ontology approach to comparative phenomics in plants

Authors Organisations
  • Anika Oellrich(Author)
    Wellcome Trust Sanger Institute
  • Ramona L. Walls(Author)
    University of Arizona
  • Ethalinda K. S. Cannon(Author)
    Iowa State University of Science and Technology
  • Steven B. Cannon(Author)
    Iowa State University of Science and Technology
  • Laurel Cooper(Author)
    Oregon State University
  • Jack Gardiner(Author)
    Iowa State University of Science and Technology
  • Georgios Gkoutos(Author)
  • Lisa Harper(Author)
    Iowa State University of Science and Technology
  • Mingze He(Author)
    Iowa State University of Science and Technology
  • Robert Hoehndorf(Author)
  • Pankaj Jaiswal(Author)
    Oregon State University
  • Scott R. Kalberer(Author)
    Iowa State University of Science and Technology
  • John P. Lloyd(Author)
    Michigan State University
  • David Meinke(Author)
    Oklahoma State University - Stillwater
  • Naama Menda(Author)
    Boyce Thompson Institute for Plant Research
  • Laura Moore(Author)
    Oregon State University
  • Rex T. Nelson(Author)
    Iowa State University of Science and Technology
  • Anuradha Pujar(Author)
    Boyce Thompson Institute for Plant Research
  • Carolyn J. Lawrence(Author)
    Iowa State University of Science and Technology
  • Eva Huala(Author)
    Phoenix Bioinformatics
Type Article
Original languageEnglish
Article number11
JournalPlant Methods
Volume11
Issue number10
DOI
Publication statusPublished - 25 Feb 2015
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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.

Keywords

  • similarity score, semantic similarity, phene, plant phenotype, phenotype ontology