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

6. Semantic Components LayerEQ
Behavior
Physiology
Cell Phenotype
Pathology
UBERON
Measurements
(Units Ontology)
PATO
NBO
MPATH GO
CHEBI
UBERON CL UO

7. Unification Layer Provision of PATO based equivalence definitions EQ
CPO
YPO TO
HPO
FBCV MP
WBPhenotype
PATO
NBO
MPATH GO
CHEBI
UBERON CL UO
Provision of PATO based equivalence definitions

8. Integration Layer EQ CPO YPO TO HPO FBCV MP WBPhenotype PATO NBO MPATHGO
CHEBI
UBERON CL UO

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 complex phenotype nodes representing complex phenotypes, diseases, drug indications and adverse reactions
Semantic similarity measures  pairwise comparison of disease and animal phenotypes

11. semantics e.g Behavior and pathology related phenotypes etc.
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
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)
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

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: ! cartilage development PATO: ! delayed
phenotype data from 5 different species  more than 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. Can we do the same for plants ?

17. PPPP pilot project Dataset Framework for plant phenotype analyses
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 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

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?