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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

This figures illustrates the usage of Plant PhenomeNET for the maize genemac1. After searching for the gene (A), search results are returned (B) and assigned and inferred phenes are shown (C), as well as semantically similar phenotypes from other genes (D). See text for more details.
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Fig4: This figures illustrates the usage of Plant PhenomeNET for the maize genemac1. After searching for the gene (A), search results are returned (B) and assigned and inferred phenes are shown (C), as well as semantically similar phenotypes from other genes (D). See text for more details.

Mentions: To illustrate the usage of Plant PhenomeNET, we provide an example search for tasselseed1 (ts1) maize gene. The tassel of maize normally bears only male flowers, but in the ts1 mutant, female flowers also develop in the spikelets born on the tassel. By entering “ts1” into the search box and submitting the form, we obtain a list of genes that all match the string “ts1” (for search query and results see: panel A and B of Figure 4). For the navigation from the search list, there are two options provided (see last two columns in panel B of Figure 4): one can either show the phenotype or explore phenotypically similar mutants.Figure 4


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)

This figures illustrates the usage of Plant PhenomeNET for the maize genemac1. After searching for the gene (A), search results are returned (B) and assigned and inferred phenes are shown (C), as well as semantically similar phenotypes from other genes (D). See text for more details.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: This figures illustrates the usage of Plant PhenomeNET for the maize genemac1. After searching for the gene (A), search results are returned (B) and assigned and inferred phenes are shown (C), as well as semantically similar phenotypes from other genes (D). See text for more details.
Mentions: To illustrate the usage of Plant PhenomeNET, we provide an example search for tasselseed1 (ts1) maize gene. The tassel of maize normally bears only male flowers, but in the ts1 mutant, female flowers also develop in the spikelets born on the tassel. By entering “ts1” into the search box and submitting the form, we obtain a list of genes that all match the string “ts1” (for search query and results see: panel A and B of Figure 4). For the navigation from the search list, there are two options provided (see last two columns in panel B of Figure 4): one can either show the phenotype or explore phenotypically similar mutants.Figure 4

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