Phenotype And Trait Ontology (PATO) and plant phenotypes
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Phenotype And Trait Ontology (PATO) and plant phenotypes
George Gkoutos
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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” …..
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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
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PATO’s Semantic Framework
• Conceptual Layer
• Semantic Components Layer
• Unification Layer
• Integration Layer
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PATO Conceptual Layer
PATO
Process
Object
EQ
EQ Modellink Entities (E) from GO, CheBI, FMA etc. to Qualities (Q) from PATO
EQ statements
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PATO
NBO
MPATH
GO
CHEBIUBERON
CL
UO
Semantic Components Layer
EQ
• Behavior• Physiology• Cell Phenotype• Pathology• UBERON• Measurements(Units Ontology)
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Unification Layer
PATO
EQ
CPO
YPO
TO
HPO
FBCV
MP
WBPhenotype
Provision of PATO based equivalence definitions
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PATO
EQ
CPO
YPO
TO
HPO
FBCV
MP
WBPhenotype
Integration Layer
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Cross Species Data Integration
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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
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• 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
Area Under Curve (AUC) = 0.9
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Candidate disease gene prioritization
• Predict all known human and mouse disease genes• Adam19 and Fgf15 mouse genes • using zebrafish phenotypes - mammalian homologues of
Cx36.7 and Nkx2.5 are involved in TOF
E: Pulmonary valve (MA)Q: constricted (PATO)
E: heart ventricle wall(MA)Q: hypertrophic (PATO)
E1: Aorta(MA)Q: overlap with (PATO)E2: Membranous interventricular septum (MA)
E1: ventricular septum (MA)Q: closure incomplete (PATO)
Mouse (MP)E1: Aorta(FMA)Q: overlap with (PATO)E2: Membranous part of the interventricular septum (FMA)
E: Interventricular septum (FMA)Q: closure incomplete (PATO)
E: Pulmonary valve (FMA)Q: constricted (PATO)
E: Wall of right ventricle (FMA)Q: hypertrophic (PATO)
Human (HPO)
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Data Integration Across Domains of Knowledge
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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)
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• 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
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Can we do the same for plants ?
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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
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Plant Phenotyping Centers
• Participating centres– National Plant Phenomics Centre– European Plant Phenomics Centre– Julich Plant Phenotyping Centre– Rothamsted Research– INRA
• Goal: PATO-based plant phenotyping analysis framework– Link to PPPP and NSF proposal
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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
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Questions?