Plant Phenotype Pilot Project AIM: To use ontologies in express and analyze plant phenotypes from...
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Transcript of Plant Phenotype Pilot Project AIM: To use ontologies in express and analyze plant phenotypes from...
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