An ontology approach to comparative phenomics in plants

Abstract

BackgroundPlant 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.ResultsWe 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0053-y) contains supplementary material, which is available to authorized users.

The quest for understanding phenotypic variation via integrated approaches in the field environment

“…However, while mechanisms to report experimental design metadata based on shared vocabularies are comparatively mature, methods to accurately describe phenotypic descriptions are just now emerging. Oellrich et al (2015) report a proof of concept wherein plant phenotypes for six species were curated as entity quality (EQ) statements to enable analysis by semantic reasoning. For example, dwarf plants are generally short (due to reduced internode length) with leaves that are both reduced in length and increased in width.…”

The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants

“…To test this assumption we used a dataset of formal phenotype descriptions recorded in mutant models of Arabidopsis thaliana , maize, barrel medic, rice, soybean, and tomato [28]. Out of 5,186 phenotype statements contained in the dataset that involve a plant anatomical entity, 315 directly match one of the classes in the FLOPO, while the others have superclasses in the FLOPO.…”

“…These approaches have the advantage of explicitly being able to utilize knowledge from anatomy or physiology ontologies [6, 46], and have successfully been applied to integrate a large number of phenotype ontologies [28, 48]. However, a difference in axiom patterns to other phenotype ontologies may increase the effort required in integrating these ontologies with FLOPO.…”

A pan-plant protein complex map reveals deep conservation and novel assemblies

“…Related to Figure 7. Interacting partners share mutational phenotypes (here, from the plant PhenomeNET ontology (Oellrich et al, 2015) ) at a signi icantly higher rate than random protein pairs ( p < 10 -16 , chi-squared test).…”