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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?