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Plant Phenotype Pilot Project

Plant Phenotype Pilot Project. The Issue: Traditional free text phenotype descriptions are inadequate for large-scale computerized comparative analyses. AIM: To use ontologies in express and analyze plant phenotypes from multiple species. Phenotype Ontology Research Coordination Network.

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Plant Phenotype Pilot Project

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  1. Plant Phenotype Pilot Project The Issue: Traditional free text phenotype descriptions are inadequate for large-scale computerized comparative analyses AIM: To use ontologies in express and analyze plant phenotypes from multiple species

  2. Phenotype Ontology Research Coordination Network http://www.phenotypercn.org/ Unifying ideas: Shared Ontologies Shared tools and methods Best practices Community outreach Many fields of biology represented: Systematics Evolutionary biology Genetics/developmental biology Ecology Paleontology … 4 Working Groups: Vertebrates Arthropods Plants Informatics

  3. Challenges of managing phenotype data • Extremely diverse data type (can range from expression profile to behavior) • Can be associated to individuals, populations or species • Different levels (summary, measurement data) • Can be comparative (mutant vs. wild type) or absolute (days to flowering of a cultivar) • Data integration - needs extensive connections to other types of data (seed stocks, genes, experimental methods, publications) • Database schema and interface design Data representation - how to represent the data in a consistent way across experiments, research communities and species Data accessibility – how do we get data out of literature and into the database?

  4. Collection of phenotype data- Who is involved? Soybean Tomato Medicago Corn Rice Arabidopsis

  5. Pilot Project - limited scope: • Mutant phenotypes (not natural variants) • Emphasis on visual and morphological (no gene expression patterns) • Summary data (not phenotype measurements) Phenotypic measurement Phenotype Data: Genotype measured Leaves are 1 cm wide Growth conditions Reference genotype Experimental treatment Data collection method Control treatment Statistical method Image Data interpretation – preferably done by experimenter Phenotype Summary: Mutant yfg1-1 has narrow leaves and flowers early in short days

  6. Why use ontologies? • Supplement, not replacement, for free text • Provides standardized vocabulary • Dwarf, short stature, small plant, reduced height are different ways of expressing the same idea • Provides relationships among terms • Vascular leaf is_atype of leaf • Leaf abscission zone part_of leaf • Leaf develops_from leaf primordium • Makes computational approaches possible • Searches • Categorization • Network analysis, semantic similarity

  7. Outline of Pilot Project Existing phenotype datasets: Existing reference ontologies Phenotypes of mutant loci, QTL Gene Ontology Phenotypes of cloned genes Plant Ontology PATO ChEBI Plant EO Ontology statements Consistent and thorough set of ontology annotations Semantic similarity computational analysis

  8. Phenotypes and Ontologies: From an ontological perspective, a phenotype is a combination of an entity and a quality that inheres in that entity Phenotypes may also consist of two entities and a relationship between them: *in PATO, the relationship is called a “relational quality” inheres in Phenotype name adherent leaf notched petal high yield increased water loss Quality fused lobed increased mass increased rate Entity juvenile vascular leaf petal seed transpiration Entity 1 juvenile vascular leaf gynoecium Relationship* fused with basal to Entity 2 stem perianth

  9. Examples of mutant phenotypes shared across species: Dwarf plants Rolled leaves

  10. Examples

  11. Next steps: • Data analysis • Clustering of genes into pathways • Degree of correlation between sequence and phenotype • Computational prediction of gene candidates for uncloned mutant genes and QTL • Apply lessons learned • Is the data set big enough? • Are the ontologies complete enough? • Is our annotation consistency good enough? • Better analysis methods?

  12. Future Possibilities with cROP • Expansion to use Protein Ontology Plant Ontology ChEBI Gene Ontology Ontology statements Plant EO PRO PATO

  13. Acknowledgements USDA-ARS-CICGRU: Steven Cannon, Scott Kalberer Carolyn Lawrence, Lisa Harper Rex Nelson, David Grant Oklahoma State University: David Meinke Michigan State University: Johnny Lloyd U. Of Nottingham Sean May Boyce Thompson Institute: Lukas Mueller (SGN) NaamaMenda (SGN) University of Arizona: Ramona Walls (PO / iPlant) Oregon State University: Laurel Cooper Pankaj Jaiswal Laura Moore George Gkoutos (University of Aberystwyth) Anika Oellrich (EBI) Funding: NSF - Phenotype Ontology Research Coordination Network (RCN)

  14. Ontology

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