Plant Ontologies Industrial Science meets Renaissance Concepts

1
Plant Ontologies Industrial Science meets
Renaissance Concepts

• Dave Selinger
• Computational Biologist
• Pioneer Hi-Bred,
• DuPont Agriculture and Nutrition

2
Outline

• 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?

3
Industrial 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

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The double-edged sword of Industrial Science

• Industrial science means lots of cheap data
• Sequencing ltlt 0.01/base
• 10,000 prokaryotic genomes are reality
• 10,000 eukaryotic genomes will be reality in the
  next five years
• Expression lt0.50/gene
• And much of this data is available for free after
  it is produced!
• Lots of data means that you cant 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.

5
Organizing 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?

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Context

• 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 ?

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Renaissance concepts (historically 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

8
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 -gt objects, real and abstract,
  processes, functions
• Partitions -gt rules that can classify concepts
• Attributes -gt properties of a concept, can have
  individual and class attributes
• Relationships -gt is a, part of

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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 -gt 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

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Forms of ontologies

• Ontologies can take several forms (data
  structures)
• Controlled vocabulary (List)
• Terms but no relationships
• Enforces systematic naming
• Hierarchy (tree structure) gt Taxonomy
• Terms and is a relationship
• Children are unique and have a single parent
• Directed acyclic graph gt Gene Ontology
• Multiple relationship types
• Children with multiple parents

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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

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A tree based Anatomy Ontology

• Developed by Winston Hides 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.

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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
• 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).

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Sample DAG

• Root
• Cooking
• Spices
• Bay leaf
• Laurel nobilis
• Umbellularia californica (California laurel)
• Trees
• Lauraceae
• Laurel
• Laurel nobilis
• Umbellularia
• Umbellularia californica

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Constructing the Pioneer Plant Ontology

• 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 Kiesselbachs 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.

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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.

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Plant 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

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Plant 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

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Adapting the POC ontology for Pioneers 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

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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 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.

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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

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Comparing 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 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.

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Complexity

• 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).

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Conclusions

• 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.

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Acknowledgements

• Pioneer
• Henry Mirsky
• Lane Arthur
• Bob Merrill
• POC
• Doreen Ware (Gramene)
• Katica Ilic (TAIR)