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PIONEER HI-BRED INTERNATIONAL, INC.
Plant Ontologies – Industrial Plant Ontologies – Industrial Science meets Renaissance Science meets Renaissance
ConceptsConcepts
Dave Selinger
Computational Biologist
Pioneer Hi-Bred,
DuPont Agriculture and Nutrition
RESEARCH
OutlineOutline
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?
RESEARCH
Industrial ScienceIndustrial Science
Not science in industry, but the industrialization of data creation, i.e. the ‘omics revolutions.
High-throughput data Sequencing Expression
Medium-throughput data Proteomics Metabolomics
Low-throughput data Gene/protein function Phenotype
RESEARCH
The double-edged sword of The double-edged sword of Industrial ScienceIndustrial Science
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
Expression <$0.50/gene And much of this data is available for free after it is
produced!
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
Organizing informationOrganizing information
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 information. The organization of the information determines what you can
discover. Experimental design
Good design will produce a contrast that will support or refute a hypothesis.
Statistical rigor – – Is the signal higher than the noise?
– How conclusive will the discoveries be?
RESEARCH
ContextContext
How do we compare across experiments? Not too hard if one person did all the experiments and
kept careful notes. If 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 – ?
RESEARCH
Renaissance concepts (historically Renaissance concepts (historically Enlightenment)Enlightenment)
Things can be systematically described and classified Organisms - Linneaus, Species Plantarum,
1758
Linneaus’ problem is much the same as the sample description problem Variable specificity
California Laurel or Oregon Myrtlewood? Kernel or seed?
In addition, a term like kernel assumes all parts, but this assumption could be wrong
RESEARCH
Ontologies to the rescue?Ontologies to the rescue? Ontology = the study of being (Philosophy)
The specification of a conceptualization of a domain of interest (Computer Science)
Original and continuing computer science interest was Artificial Intelligence.
How can a computer make inferences? Need to define meanings – can for example. Structure and relationships in an ontology allow a computer to make
inferences.– 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
attributes Relationships -> is a, part of
RESEARCH
Does an ontology make sense?Does an ontology make sense?
The value of ontologies is a current debate among information scientists. 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
RESEARCH
Forms of ontologiesForms of ontologies
Ontologies can take several forms (data structures) 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
RESEARCH
Features of TreesFeatures 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
RESEARCH
A tree based Anatomy OntologyA tree based Anatomy Ontology
Developed by Winston Hide’s group at SANBI and Electric Genetics
Single concept, orthogonal trees Cells Tissues Organs Disease state
Each tree is independent, but has related dimensions describing a sample
Set operations, intersection or union, between trees allows specific queries.
RESEARCH
Features of DAGsFeatures 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
If this results from poor definition of the concept, then it is not good.
Multiple parentage fits a “normalized” data model Like a normalized relational database, a DAG can
minimize duplication of objects (concepts).
RESEARCH
Sample DAGSample DAG
Root Cooking
Spices
– Bay leaf• Laurel nobilis
• Umbellularia californica (California laurel)
Trees Lauraceae
– Laurel• Laurel nobilis
– Umbellularia• Umbellularia californica
RESEARCH
Constructing the Pioneer Plant Constructing the Pioneer Plant OntologyOntology
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.
RESEARCH
To collaborate or not to collaborate?To collaborate or not to collaborate?
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 the public 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.
RESEARCH
Plant Ontology ConsortiumPlant Ontology Consortium
Focused on model organisms Arabidopsis Rice and other grasses with the rice terms (corn).
Used a DAG approach Multiple concepts
Structure (cells, tissues, sporophyte and gametophyte) Development
Used DAGeditor and other GO approaches Most terms have multiple parents Same software and data structures as GO
RESEARCH
Plant OntologyPlant Ontology
Domain = Plant anatomy and development Concepts
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 mother cell
– Guard cells develop from guard mother cells
RESEARCH
Adapting the POC ontology for Adapting the POC ontology for Pioneer’s needsPioneer’s needs
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.
nectary)
Another problem is that we collect samples that are convenient subdivisions of structures Tip and 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
RESEARCH
Our current solutionOur 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 keep only
terms that are used to label an experimental sample Parent terms of used terms.
Can be readily rebuilt if new experiments or terms are added.
RESEARCH
What can you do with the ontology?What can you do with the ontology?
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
RESEARCH
Comparing by tissueComparing by tissue
The ontology provides the groupings, but how to summarize Mean? Median? Maximum value?
Significance of differences? Each group 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.
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Annotating genesAnnotating genes
This is the primary use for TAIR and Gramene Potentially label most genes with tissues of expression However, need to differentiate presence with
preferential expression. A gene may be present in many tissues, but highly expressed in
a few 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.
RESEARCH
ComplexityComplexity
Genes may appear to differ between tissues for trivial reasons 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
stem. Compare multiple leaf and stem samples from different positions
(developmental stages).
RESEARCH
ConclusionsConclusions
The Plant Ontology classifies experiments and genes based on anatomical and developmental concepts.
Now that we have significant data, can we, like Darwin, discern the underlying mechanisms for how anatomical and developmental differences occur.
The Plant Ontology will be successful and used long term if it facilitates these kinds of investigations.
RESEARCH
AcknowledgementsAcknowledgements
Pioneer Henry Mirsky Lane Arthur Bob Merrill
POC Doreen Ware (Gramene) Katica Ilic (TAIR)
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