Plant Ontologies –
Industrial Science meets
DuPont Agriculture and Nutrition
What is the nature of the problem that a Plant
Anatomy Ontology can solve?
What is an Ontology?
How do you make a Plant Anatomy Ontology?
Does it really solve the problem?
Not science in industry, but the industrialization of data
creation, i.e. the „omics revolutions.
The double-edged sword of
Industrial science means lots of cheap data
Sequencing << $0.01/base
$10,000 prokaryotic genomes are reality
$10,000 eukaryotic genomes will be reality in the next five years
And much of this data is available for free after it is
Lots of data means that you can‟t sit down with
your lab notebook and analyze the data by hand.
Databases, software for searching and comparing
Whole new areas of research devoted to finding
meaningful patterns in lots of data. RESEARCH
Information is not knowledge.
But knowledge can be acquired from information.
But only with a lot of effort, see third law of thermodynamics
Central challenge with Industrial science is organizing the
The organization of the information determines what you can
Good design will produce a contrast that will support or refute a
Statistical rigor –
– Is the signal higher than the noise?
– How conclusive will the discoveries be?
How do we compare across experiments?
Not too hard if one person did all the experiments and
kept careful notes.
multiple people, then we need to define what was
done, what the analysis was, and what the sample was.
What was done – e.g. MIAME standard for describing the
technical details of an expression experiment.
Analysis – e.g. ANOVA, SAM, etc.
Sample – ?
Things can be systematically described
Organisms - Linneaus, Species Plantarum,
Linneaus‟ problem is much the same as
the sample description problem
California Laurel or Oregon Myrtlewood?
Kernel or seed?
In addition, a term like kernel assumes all
parts, but this assumption could be wrong
Ontologies to the rescue?
Ontology = the study of being (Philosophy)
The specification of a conceptualization of a domain of interest
Original and continuing computer science interest was Artificial
How can a computer make inferences?
Need to define meanings – can for example.
Structure and relationships in an ontology allow a computer to make
– Mary is the mother of Bill. Is Mary a parent of Bill?
– IsA Mother Parent
Parts of an ontology
Concepts -> objects, real and abstract, processes, functions
Partitions -> rules that can classify concepts
Attributes -> properties of a concept, can have individual and class
Relationships -> is a, part of
Does an ontology make sense?
The value of ontologies is a current debate among
One group advocates that ontologies are necessary for computers
to understand content.
Semantic web -> an extension of the current HTML/XML based web to
something with ontological inference
Others argue that ontologies are not needed and are not practical
Complexity is ok and just use a Google like search to connect concepts.
However, some problems, like organismal classification and the
periodic table are very amenable to an ontological approach.
Formal categories and stable entities
Expert users and catalogers
Forms of ontologies
Ontologies can take several forms (data
Controlled vocabulary (List)
Terms but no relationships
Enforces systematic naming
Hierarchy (tree structure) => Taxonomy
Terms and “is a” relationship
Children are unique and have a single parent
Directed acyclic graph => Gene Ontology
Multiple relationship types
Children with multiple parents
Features of Trees
Because each child node has only one parent
There is an unambiguous path to the root from each leaf
Child nodes can be easily grouped at any level of the structure
Trees can express only one organizing principle
Work well for taxonomy (at least eukaryotic taxonomy)
Organizing principle is classification by similarity
All terms have an “is a” relationship to the next level term
Organisms were classified before evolution was hypothesized, but
the classification matches the evolutionary relationships
Similar example would be the periodic table of the elements
Classification can facilitate discovery of underlying principles
A tree based Anatomy Ontology
Developed by Winston Hide‟s group at SANBI and
Single concept, orthogonal trees
Each tree is independent, but has related
dimensions describing a sample
Set operations, intersection or union, between
trees allows specific queries. RESEARCH
Features of DAGs
A tree is a special case of the DAG class
Children can have multiple parents.
Allows multiple classifications of the same child
E.g. a guard cell is both part of a leaf and is an epidermal cell.
Allows for more than a binary classification of a concept
Ifthis results from poor definition of the concept, then it
is not good.
Multiple parentage fits a “normalized” data model
Likea normalized relational database, a DAG can
minimize duplication of objects (concepts).
– Bay leaf
• Laurel nobilis
• Umbellularia californica (California laurel)
• Laurel nobilis
• Umbellularia californica
Constructing the Pioneer Plant
Decided to produce a DAG
Used DAGeditor (editor developed for GO)
Developed our own web based viewing tool
AmiGO was too complicated to re-use. Other public browsers
did not have the functionality we wanted.
Decided to focus on Corn and Soybeans
Used Kiesselbach‟s 1949 Monograph on Corn structure
and reproduction as the primary source.
Used Iowa State University Ag Extension publications
for the development stages of corn and soybeans
Added information from a botany textbook to cover
missing terms from soybean.
To collaborate or not to
Advantage of just using the Pioneer Ontology was
that it served our needs and was focused on corn
and soybeans, our major crops.
Disadvantage was that it was not synchronized to
We would not be able to easily integrate public tissue
classifications to ours
We would not be able to easily take advantage of
improvements to the public ontology
Presumably the public ontology would be more
“botanically correct” than ours.
Plant Ontology Consortium
Focused on model organisms
Rice and other grasses with the rice terms (corn).
Used a DAG approach
Structure (cells, tissues, sporophyte and gametophyte)
Used DAGeditor and other GO approaches
Most terms have multiple parents
Same software and data structures as GO
Domain = Plant anatomy and development
Plant parts (leaf, root, flower, meristem, etc.)
Life cycle stages (sporophyte, gametophyte)
Developmental stages (V1, flowering, R1, etc.)
Relationships between concepts
“A kind of” (Is a)
– A prop root is a root
“A part of” (part of)
– A root cap is part of a root
In addition, for plant anatomy a “develops from” relation is needed
– For example the relationship between stomatal guard cells and the guard
– Guard cells develop from guard mother cells
Adapting the POC ontology for
Problem is that it has many more terms than
required for our experiments
Some terms describe tissues or cells that are not
practical to collect (e.g. antipodal cells)
Some terms describe parts not found in corn (e.g.
Another problem is that we collect samples that
are convenient subdivisions of structures
Tipand base of an immature ear. Each differs from a
whole immature ear in terms of what it contains.
Basal endosperm – morphologically distinct from starchy
endosperm, but not found in the ontology
Our current solution
Add additional terms to the POC ontology
Use a different id system
easily distinguished from POC terms
will not be overwritten by on-going public curation efforts.
Label experiments with the terms from the ontology.
Create a Custom ontology
Query the whole ontology with the terms used in the labeling and
terms that are used to label an experimental sample
Can be readily rebuilt if new experiments or terms are added.
What can you do with the
Provides a grouping mechanism
Summarize expression for a tissue
Compare expression between tissues
Make complex queries that involve multiple tissues
Provides a systematic label for annotating genes
Where is the gene expressed?
Query annotation of genes based on terms
Provides a description of the complexity of tissue samples
Leaf sample is composed of multiple cell types with different roles
Cell types can be shared between tissues or structures
Comparing by tissue
The ontology provides the groupings, but how to
Significance of differences?
Eachgroup will be much more variable than a set of
samples from a controlled experiment.
But you may be able to eliminate the inevitable false
discoveries that appear when looking at large numbers
of genes. RESEARCH
This is the primary use for TAIR and Gramene
Potentially label most genes with tissues of expression
However, need to differentiate presence with
A gene may be present in many tissues, but highly expressed in
Another gene may be present in the same tissues, but similarly
expressed in all of them.
– Might need to precompute and indicate which tissues the gene is
significantly preferentially expressed in.
– Might be able to use the RMS differences between expression in
each tissue as a measure of consistency.
Genes may appear to differ between tissues for
Example: Gene appears to be preferentially expressed
in stem versus leaf tissue.
If gene is really specific to vascular tissue and stem has more…
Gene is expressed late in development, adjacent leaves and
stems may differ in development.
Ontology can guide further experiments
Compare vascular and non-vascular tissue from both leaf and
Compare multiple leaf and stem samples from different positions
The Plant Ontology classifies experiments and
genes based on anatomical and developmental
Now that we have significant data, can we, like
Darwin, discern the underlying mechanisms for
how anatomical and developmental differences
The Plant Ontology will be successful and used
long term if it facilitates these kinds of
Doreen Ware (Gramene)
Katica Ilic (TAIR)