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An ontology approach to comparative phenomics in plants.

Oellrich A, Walls RL, Cannon EK, Cannon SB, Cooper L, Gardiner J, Gkoutos GV, Harper L, He M, Hoehndorf R, Jaiswal P, Kalberer SR, Lloyd JP, Meinke D, Menda N, Moore L, Nelson RT, Pujar A, Lawrence CJ, Huala E - Plant Methods (2015)

Bottom Line: 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 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.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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA UK.

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.

No MeSH data available.


Related in: MedlinePlus

The method applied to annotate mutant phenotypes from textual descriptions. Textual descriptions from the literature or databases (A), based on observations of mutant plants, are first broken down into atomized statements corresponding to phenes (B) that are then represented with EQ statements (C).
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Fig1: The method applied to annotate mutant phenotypes from textual descriptions. Textual descriptions from the literature or databases (A), based on observations of mutant plants, are first broken down into atomized statements corresponding to phenes (B) that are then represented with EQ statements (C).

Mentions: We first decomposed each free text phenotype description into a set of simple atomized statements corresponding to each component, or “phene”, of the phenotype. We then translated each of these components into an EQ (Entity-Quality) statement (Figure 1). As with EQ statements previously developed for mammalian species, we distinguish between structural phenotypes, such as “short plant”, and process phenotypes, such as “late flowering” [40]. In a structural phenotype, the Entity is an affected part of the plant, represented with a term from the Plant Ontology (PO) [27] or Gene Ontology (GO) cellular component branch [29]. In a process phenotype, the Entity is an altered process represented with a term from the GO biological process branch. In both cases, the manner in which the entity is affected was described using Quality terms from the Phenotype and Trait Ontology (PATO) [16]. For example, the atomized statement “short leaves” can be expressed as: Entity = vascular leaf from the Plant Ontology (PO:0009025)a and Quality = decreased length from the Phenotype and Trait Ontology (PATO:0000574).Figure 1


An ontology approach to comparative phenomics in plants.

Oellrich A, Walls RL, Cannon EK, Cannon SB, Cooper L, Gardiner J, Gkoutos GV, Harper L, He M, Hoehndorf R, Jaiswal P, Kalberer SR, Lloyd JP, Meinke D, Menda N, Moore L, Nelson RT, Pujar A, Lawrence CJ, Huala E - Plant Methods (2015)

The method applied to annotate mutant phenotypes from textual descriptions. Textual descriptions from the literature or databases (A), based on observations of mutant plants, are first broken down into atomized statements corresponding to phenes (B) that are then represented with EQ statements (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359497&req=5

Fig1: The method applied to annotate mutant phenotypes from textual descriptions. Textual descriptions from the literature or databases (A), based on observations of mutant plants, are first broken down into atomized statements corresponding to phenes (B) that are then represented with EQ statements (C).
Mentions: We first decomposed each free text phenotype description into a set of simple atomized statements corresponding to each component, or “phene”, of the phenotype. We then translated each of these components into an EQ (Entity-Quality) statement (Figure 1). As with EQ statements previously developed for mammalian species, we distinguish between structural phenotypes, such as “short plant”, and process phenotypes, such as “late flowering” [40]. In a structural phenotype, the Entity is an affected part of the plant, represented with a term from the Plant Ontology (PO) [27] or Gene Ontology (GO) cellular component branch [29]. In a process phenotype, the Entity is an altered process represented with a term from the GO biological process branch. In both cases, the manner in which the entity is affected was described using Quality terms from the Phenotype and Trait Ontology (PATO) [16]. For example, the atomized statement “short leaves” can be expressed as: Entity = vascular leaf from the Plant Ontology (PO:0009025)a and Quality = decreased length from the Phenotype and Trait Ontology (PATO:0000574).Figure 1

Bottom Line: 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 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.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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA UK.

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.

No MeSH data available.


Related in: MedlinePlus