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Phenotype And Trait Ontology (PATO) and plant phenotypes

Phenotype And Trait Ontology (PATO) and plant phenotypes. George Gkoutos. Phenotype And Trait Ontology (PATO). The meaningful cross species and across domain translation of phenotype is essential  phenotype-driven gene function discovery and comparative pathobiology

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Phenotype And Trait Ontology (PATO) and plant phenotypes

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  1. Phenotype And Trait Ontology (PATO) and plant phenotypes George Gkoutos

  2. Phenotype And Trait Ontology (PATO) The meaningful cross species and across domain translation of phenotype is essential  phenotype-driven gene function discovery and comparative pathobiology Goal - “A platform for facilitating mutual understanding and interoperability of phenotype information across • species, • domains of knowledge, and amongst people and machines” …..

  3. PATO today PATO is now being used as a community standard for phenotype description • many consortia (e.g. Phenoscape, The Virtual Human Physiology project (VPH), IMPC, BIRN, NIF) • most of the major model organism databases, (e.g. example Flybase, Dictybase, Wormbase, Zfin, Mouse genome database (MGD)) • international projects

  4. PATO’s Semantic Framework • Conceptual Layer • Semantic Components Layer • Unification Layer • Integration Layer

  5. EQ PATO Conceptual Layer EQ Model link Entities (E) from GO, CheBI, FMA etc. to Qualities (Q) from PATO  EQ statements

  6. EQ Semantic Components Layer • Behavior • Physiology • Cell Phenotype • Pathology • UBERON • Measurements • (Units Ontology)

  7. Unification Layer Provision of PATO based equivalence definitions

  8. Integration Layer

  9. Cross Species Data Integration

  10. Cross species integration framework • A PATO-based cross species phenotype network based on experimental data from 5 model organisms yeast, fly, worm, fish and mouse and human disease phenotypes (OMIM, OrphaNet) • integration of anatomy and phenotype ontologies • more than 1,000,000 classes and 2,500,000 axioms • PhenomeNET forms a network with more than 300.000 complex phenotype nodes representing complex phenotypes, diseases, drug indications and adverse reactions • Semantic similarity measures  pairwise comparison of disease and animal phenotypes

  11. Area Under Curve (AUC) = 0.9 • quantitative evaluation based on predicting orthology, pathway, disease • enhance the network e.g. • semantics e.g Behavior and pathology related phenotypes etc. • methods e.g. text mining, machine learning etc. • resources

  12. Candidate disease gene prioritization Human (HPO) Mouse (MP) E1: Aorta(FMA) Q: overlap with (PATO) E2: Membranous part of the interventricular septum (FMA) E1: Aorta(MA) Q: overlap with (PATO) E2: Membranous interventricularseptum (MA) • Predict all known human and mouse disease genes • Adam19 and Fgf15 mouse genes • using zebrafishphenotypes - mammalian homologues of Cx36.7 and Nkx2.5 are involved in TOF E: Pulmonary valve (FMA) Q: constricted (PATO) E: Pulmonary valve (MA) Q: constricted (PATO) E: Interventricular septum (FMA) Q: closure incomplete (PATO) E1: ventricular septum (MA) Q: closure incomplete (PATO) E: Wall of right ventricle(FMA) Q: hypertrophic (PATO) E: heart ventricle wall(MA) Q: hypertrophic (PATO)

  13. Data Integration Across Domains of Knowledge

  14. Gene function determination • bridge the gap between the availability of phenotype  infer the functions(impaired given a phenotype observation) • example: delayed cartilage development  EQ = GO:0051216 ! cartilage development PATO:0000502! delayed • phenotype data from 5 different species more than 40000 novel gene functions • evaluation: • manually for biological correctness • predict genetic interactions based on gene functions (semantic similarity between genes)

  15. improvement for every species, and significantly improvement for fruitfly, worm and mouse • GO-based gene function annotations  analysis gene expression data • large volume of data from high-throughput mutagenesis screens

  16. Canwe do the same for plants ?

  17. PPPP pilot project • Dataset • manual EQ annotations for various species • Framework for plant phenotype analyses • build a Plant PhenomeNet • Analysis • analyze the dataset and quantify the extent of phenotype similarity among certain groups of alleles/genes • prediction of gene identity for QTL or genetically defined locus • etc

  18. Plant PhenotypingCenters • Participating centres • National Plant PhenomicsCentre • European Plant PhenomicsCentre • JulichPlant Phenotyping Centre • RothamstedResearch • INRA • Goal: PATO-based plant phenotyping analysis framework • Link to PPPP and NSF proposal

  19. Solanaceae Phenotypic Atlas (SPA) PATO-based methodology to systematically study associations between genotype, phenotype and environment in Solanum • Nightshade Phenotype Ontology (NPO) • Compare phenotypic similarity across flowering plants • Generate a Solanum geospatial map • Link environmental features to the Solanum geospatial map

  20. Questions?

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