Plant Phenotype Pilot Project

AIM: To use ontologies in express and analyze plant phenotypes from multiple species

The Issue:Traditional free text phenotype descriptions are inadequate for large-scale computerized comparative analyses

http://www.phenotypercn.org/

4 Working Groups:VertebratesArthropods

PlantsInformatics

Many fields of biology represented:Systematics

Evolutionary biologyGenetics/developmental biology

EcologyPaleontology

…

Phenotype Ontology Research Coordination Network

Unifying ideas:Shared Ontologies

Shared tools and methodsBest practices

Community outreach

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?

Collection of phenotype data- Who is involved?

Soybean

Tomato

Medicago

Corn

Rice

Arabidopsis

SpeciesGenes included in

project set SourceGlycine max 233 SoyBaseSolanum lycopersicum 74 SGNMedicago truncatula 443 LISZea mays 324 MaizeGDBOryza sativa 138 PO/Gramene/OryzabaseArabidopsis thaliana 2400 Lloyd and Meinke 2012

Phenotypic measurement

Experimental treatment

Genotype measured

Data collection method

Statistical method

Reference genotype

Control treatment

Mutant yfg1-1 has narrow leaves and flowers early in short days

Growth conditions

Phenotype Data:

Phenotype Summary:

Leaves are 1 cm wide

Image

Data interpretation – preferably done by experimenter

Pilot Project - limited scope: • Mutant phenotypes (not natural variants) • Emphasis on visual and morphological (no gene expression patterns)• Summary data (not phenotype measurements)

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_a type of leaf– Leaf abscission zone part_of leaf– Leaf develops_from leaf primordium

• Makes computational approaches possible– Searches– Categorization– Network analysis, semantic similarity

Phenotypes of cloned

genes

Existing phenotype datasets: Existing reference ontologies

Consistent and thorough set of ontology annotations

Semantic similarity computational analysis

Phenotypes of mutant loci, QTL Plant

Ontology

Gene Ontology

ChEBI

Ontology statements

PATO

Outline of Pilot Project

Plant EO

From an ontological perspective, a phenotype is a combination of an entity and a quality that inheres in that entity

inheres in Qualityfusedlobedincreased massincreased rate

Entityjuvenile vascular leafpetalseedtranspiration

Phenotype nameadherent leafnotched petalhigh yieldincreased water loss

Phenotypes may also consist of two entities and a relationship between them:

Relationship*fused withbasal to

Entity 1juvenile vascular leafgynoecium

Entity 2stemperianth

Phenotypes and Ontologies:

*in PATO, the relationship is called a “relational quality”

Examples of mutant phenotypes shared across species:

Dwarf plants

Rolled leaves

ExamplesDescription of

Mutant Phenotype

Atomized Phenotype statements

Entity Quality(PATO)

Dwarf with profuse slender tillers, small panicles

dwarf PO: shoot system decreased height

profuse tillers PO: whole plant has extra parts of type (basal axillary shoot system)

slender tillers PO: basal axillary shoot system slender

small panicles PO: inflorescence decreased size

Delayed flowering; Reduction in total chlorophyll

GO: flowering delayed

ChEBI: chlorophyll decreased concentration

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?

Future Possibilities with cROP• Expansion to use Protein Ontology

Plant Ontology

Gene Ontology

PRO

Ontology statements

PATO

Plant EO

ChEBI

Acknowledgements

Funding: NSF - Phenotype Ontology Research Coordination Network (RCN)

Oregon State University:Laurel CooperPankaj JaiswalLaura Moore

University of Arizona:Ramona Walls (PO / iPlant)

USDA-ARS-CICGRU:

Steven Cannon, Scott Kalberer

Carolyn Lawrence, Lisa Harper

Rex Nelson, David Grant

George Gkoutos (University of Aberystwyth)Anika Oellrich (EBI)

Oklahoma State University:David Meinke

Boyce Thompson Institute:Lukas Mueller (SGN)Naama Menda (SGN)

Michigan State University:Johnny Lloyd

U. Of NottinghamSean May

Ontology