Plant Phenotype Pilot Project
The Issue:
Traditional free text phenotype descriptions are
inadequate for large-scale computerized
comparative analyses
AIM: To use ontologies in express and analyze plant
phenotypes from multiple species
Phenotype Ontology Research Coordination Network
http://www.phenotypercn.org/
4 Working Groups:
Vertebrates
Arthropods
Plants
Informatics
Many fields of biology represented:
Systematics
Evolutionary biology
Genetics/developmental biology
Ecology
Paleontology
…
Unifying ideas:
Shared Ontologies
Shared tools and methods
Best 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?
Species
Glycine max
Solanum lycopersicum
Medicago truncatula
Zea mays
Oryza sativa
Arabidopsis thaliana
Genes included in
project set
233
74
443
324
138
2400
Source
SoyBase
SGN
LIS
MaizeGDB
PO/Gramene/Oryzabase
Lloyd and Meinke 2012
Pilot Project - limited scope:
• Mutant phenotypes (not natural variants)
• Emphasis on visual and morphological (no gene expression patterns)
• Summary data (not phenotype measurements)
Phenotype Data:
Phenotypic measurement
Genotype measured
Leaves are 1 cm wide
Growth conditions
Control treatment
Image
Reference genotype
Experimental treatment
Data collection method
Statistical method
Data interpretation –
preferably done by
experimenter
Phenotype Summary:
Mutant yfg1-1 has narrow leaves and flowers early in short days
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
Outline of Pilot Project
Existing phenotype datasets:
Phenotypes
of mutant
loci, QTL
Existing reference ontologies
Phenotypes
of cloned
genes
Plant
Ontology
Gene
Ontology
PATO
ChEBI
Plant
EO
Consistent and thorough set
of ontology annotations
Ontology
statements
Semantic similarity
computational analysis
Phenotypes and Ontologies:
From an ontological perspective, a phenotype is a combination
of an entity and a quality that inheres in that entity
Phenotype name
adherent leaf
notched petal
high yield
increased water loss
inheres in
Entity
juvenile vascular leaf
petal
seed
transpiration
Quality
fused
lobed
increased mass
increased rate
Phenotypes may also consist of two entities and a relationship
between them:
Entity 1
juvenile vascular leaf
gynoecium
Relationship*
fused with
basal to
*in PATO, the relationship is called a “relational quality”
Entity 2
stem
perianth
Examples of mutant phenotypes shared across species:
Dwarf plants
Rolled leaves
Examples
Description of
Mutant Phenotype
Atomized
Phenotype
statements
dwarf
Dwarf with profuse
slender tillers, small
panicles
PO: shoot system
profuse tillers PO: whole plant
PO: basal axillary
shoot system
small panicles PO: inflorescence
slender tillers
Delayed flowering;
Reduction in total
chlorophyll
Quality
(PATO)
Entity
GO: flowering
decreased height
has extra parts of type
(basal axillary shoot
system)
slender
decreased size
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
ChEBI
Gene
Ontology
Ontology
statements
Plant
EO
PATO
PRO
Acknowledgements
USDA-ARS-CICGRU:
Oklahoma State University:
David Meinke
Steven Cannon, Scott Kalberer
Michigan State University:
Johnny Lloyd
Carolyn Lawrence, Lisa Harper
U. Of Nottingham
Sean May
Rex Nelson, David Grant
Boyce Thompson Institute:
Lukas Mueller (SGN)
Naama Menda (SGN)
University of Arizona:
Ramona Walls (PO / iPlant)
Oregon State University:
Laurel Cooper
Pankaj Jaiswal
Laura Moore
George Gkoutos (University of Aberystwyth)
Anika Oellrich (EBI)
Funding: NSF - Phenotype Ontology
Research Coordination Network (RCN)
Ontolog
y