Introduction to Ontology

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Introduction to Ontology

• Barry Smith
• http//ontology.buffalo.edu/smith

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Who am I?

• NCBO National Center for Biomedical Ontology
  (NIH Roadmap Center)

• Stanford Medical Informatics
• University of San Francisco Medical Center
• Berkeley Drosophila Genome Project
• Cambridge University Department of Genetics
• The Mayo Clinic
• University at Buffalo Department of Philosophy

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Who am I?

• NYS Center of Excellence in Bioinformatics and
  Life Sciences Ontology Research Group
• Buffalo Clinical and Translational Science
  Institute (CTSI)

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Who am I?

• Cleveland Clinic Semantic Database
• Gene Ontology
• Ontology for Biomedical Investigations
• Open Biomedical Ontologies Consortium
• Institute for Formal Ontology and Medical
  Information Science
• BIRN Ontology Task Force
• ...

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natural language labels
to make the data cognitively accessible to human
beings
and algorithmically tractable to computers
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compare legends for maps
compare legends for maps
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compare legends for maps
common legends allow (cross-border) integration
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ontologies are legends for data
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legends

• help human beings use and understand complex
  representations of reality
• help human beings create useful complex
  representations of reality
• help computers process complex representations
  of reality
• help glue data together

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annotations using common ontologies can yield
integration of image data
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computationally tractable legends

• help human beings find things in very large
  complex representations of reality

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where in the body ? where in the cell ?
what kind of organism ?
what kind of disease process ?
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• to yield
• distributed accessibility of the data to
  humans
• reasoning with the data
• cumulation for purposes of research
• incrementality and evolvability
• integration with clinical data

Creating broad-coverage semantic annotation
systems for biomedicine
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The Gene Ontology
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The Gene Ontology
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The Idea of Common Controlled Vocabularies
GlyProt
MouseEcotope
sphingolipid transporter activity
DiabetInGene
GluChem
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The Idea of Common Controlled Vocabularies
GlyProt
MouseEcotope
Holliday junction helicase complex
DiabetInGene
GluChem
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Multiple kinds of data in multiple kinds of silos

• Lab / pathology data
• Electronic Health Record data
• Clinical trial data
• Patient histories
• Medical imaging
• Microarray data
• Protein chip data
• Flow cytometry
• Mass spec
• Genotype / SNP data

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How to find your data?

• How to find other peoples data?
• How to reason with data when you find it?
• How to work out what data does not yet exist?

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Multiple kinds of standardization for data

• Terminologies (SNOMED, UMLS)
• CDEs (Clinical research)
• Information Exchange Standards (HL7 RIM)
• LIMS (LOINC)
• MGED standards for microarray data, etc.

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how solve the problem of making such data
queryable and re-usable by others to address NIH
mandates?
part of the solution must involve standardized
terminologies and coding schemes
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most successful, thus far UMLS

• collection of separate terminologies built by
  trained experts
• massively useful for information retrieval and
  information integration
• UMLS Metathesaurus a system of post hoc mappings
  between overlapping source vocabularies

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for UMLS

• local usage respected
• regimentation frowned upon
• cross-framework consistency not important
• no concern to establish consistency with basic
  science
• different grades of formal rigor, different
  degrees of completeness, different update policies

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caBIG approach BRIDG (top-down imposition)
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for science

• where do you find scientifically validated
  information linking gene products and other
  entities represented in biochemical databases to
  semantically meaningful terms pertaining to
  disease, anatomy, development in different model
  organisms?

A new approach
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• caBIG
• BRIDG

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SNOMED

• Ultimately as data become attached to the
  samples (e.g., pathology data, genotypes) these
  will be linked to the patient records.

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where in the body ? where in the cell ?
what kind of organism ?
what kind of disease process ?
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ontologies high quality controlled structured
vocabularies for the annotation (description) of
data
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compare legends for diagrams
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or chemistry diagrams
legends for chemistry diagrams
Prasanna, et al. Chemical Compound Navigator A
Web-Based Chem-BLAST, Chemical Taxonomy-Based
Search Engine for Browsing Compounds PROTEINS
Structure, Function, and Bioinformatics
63907917 (2006)
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Ramirez et al. Linking of Digital Images to
Phylogenetic Data Matrices Using a Morphological
Ontology Syst. Biol. 56(2)283294, 2007
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The Network Effects of Synchronization
GlyProt
MouseEcotope
Holliday junction helicase complex
DiabetInGene
GluChem
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Five bangs for your GO buck

• based in biological science
• incremental approach (evidence-based evolutionary
  pathway)
• cross-species data comparability (human, mouse,
  yeast, fly ...)
• cross-granularity data integration (molecule,
  cell, organ, organism)
• cumulation of scientific knowledge in
  algorithmically tractable form, links people to
  software

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• Model organism databases employ scientific
  curators who use the experimental observations
  reported in the biomedical literature to
  associate GO terms with entries in gene product
  and other molecular biology databases
• (4 mill. p.a. NIH funding)

The methodology of annotations
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How to extend the GO methodology to other domains
of clinical and translational medicine?

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the problem
existing clinical vocabularies are of variable
quality and low mutual consistency current
proliferation of tiny ontologies by different
groups with urgent annotation needs
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the solution

• establish common rules governing best practices
  for creating ontologies in coordinated fashion,
  with an evidence-based pathway to incremental
  improvement

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How to build an ontology

• work with scientists to create an initial
  top-level classification
• find 50 most commonly used terms corresponding
  to types in reality
• arrange these terms into an informal is_a
  hierarchy according to the universality principle
• A is_a B ? every instance of A is an instance of
  B
• fill in missing terms to give a complete
  hierarchy
• (leave it to domain scientists to populate the
  lower levels of the hierarchy)

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First step (2003)

• a shared portal for (so far) 58 ontologies
• (low regimentation)
• http//obo.sourceforge.net ? NCBO BioPortal

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OBO now the principal entry point for creation of
web-accessible biomedical data

• OBO and OBOEdit low-tech to encourage users
• Simple (web-service-based) tools created to
  support the work of biologists in creating
  annotations (data entry)
• OBO ? OWL DL converters make OBO Foundry
  annotated data immediately accessible to Semantic
  Web data integration projects

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Second step (2004)reform efforts initiated,
e.g. linking GO formally to other ontologies and
data sources
GO

Cell type

Osteoblast differentiation Processes whereby an
osteoprogenitor cell or a cranial neural crest
cell acquires the specialized features of an
osteoblast, a bone-forming cell which secretes
extracellular matrix.
New Definition
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Third step (2006)
The OBO Foundryhttp//obofoundry.org/
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Building out from the original GO
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initial OBO Foundry coverage
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• Continuants (aka endurants)
• have continuous existence in time
• preserve their identity through change
• exist in toto whenever they exist at all
• Occurrents (aka processes)
• have temporal parts
• unfold themselves in successive phases
• exist only in their phases

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You are a continuant

• Your life is an occurrent
• You are 3-dimensional
• Your life is 4-dimensional

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Dependent entities

• require independent continuants as their bearers
• There is no run without a runner
• There is no grin without a cat

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Dependent vs. independent continuants

• Independent continuants (organisms, buildings,
  environments)
• Dependent continuants (quality, shape, role,
  propensity, function, status, power, right)

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All occurrents are dependent entities

• They are dependent on those independent
  continuants which are their participants (agents,
  patients, media ...)

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BFO Top-Level Ontology
Continuant
Occurrent (always dependent on one or more
independent continuants)
Independent Continuant
Dependent Continuant
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A representation of top-level types
Continuant
Occurrent
biological process
Independent Continuant
Dependent Continuant
cell component
molecular function
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Top-Level Ontology
Continuant
Occurrent
Independent Continuant
Dependent Continuant
Side-Effect, Stochastic Process, ...
Functioning
Function
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Top-Level Ontology
Continuant
Occurrent
Independent Continuant
Dependent Continuant
Functioning
Side-Effect, Stochastic Process, ...
Function
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Top-Level Ontology
instances (in space and time)
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CRITERIA

• The ontology is open and available to be used by
  all.
• The ontology is in, or can be instantiated in, a
  common formal language.
• The developers of the ontology agree in advance
  to collaborate with developers of other OBO
  Foundry ontology where domains overlap.

CRITERIA
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• UPDATE The developers of each ontology commit to
  its maintenance in light of scientific advance,
  and to soliciting community feedback for its
  improvement.
• ORTHOGONALITY They commit to working with other
  Foundry members to ensure that, for any
  particular domain, there is community convergence
  on a single controlled vocabulary.

CRITERIA
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• communities must work together to ensure
  consistency ? orthogonality ? modular development
  plus additivity of annotations
• if we annotate a database or body of literature
  with one OBO Foundry ontology, we should be able
  to add annotations from a second such ontology
  without conflicts
• ontologies do not need to create tiny theories of
  anatomy or chemistry within themselves

ORTHOGONALITY
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CRITERIA

• IDENTIFIERS The ontology possesses a unique
  identifier space within OBO.
• VERSIONING The ontology provider has procedures
  for identifying distinct successive versions.
• The ontology includes textual definitions for all
  terms.

CRITERIA
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• CLEARLY BOUNDED The ontology has a clearly
  specified and clearly delineated content.
• DOCUMENTATION The ontology is well-documented.
• USERS The ontology has a plurality of
  independent users.

CRITERIA
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• COMMON ARCHITECTURE The ontology uses relations
  which are unambiguously defined following the
  pattern of definitions laid down in the OBO
  Relation Ontology

CRITERIA
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• OBO Foundry is serving as a benchmark for
  improvements in discipline-focused terminology
  resources
• yielding callibration of existing terminologies
  and data resources and alignment of different
  views

Consequences
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Foundry ontologies all work in the same way

• all are built to represent the types existing in
  a pre-existing domain and the relations between
  these types in a way which can support reasoning
• we have data
• we need to make this data available for semantic
  search and algorithmic processing
• we create a consensus-based ontology for
  annotating the data
• and ensure that it can interoperate with Foundry
  ontologies for neighboring domains

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Mature OBO Foundry ontologies (now undergoing
reform)

• Cell Ontology (CL)
• Chemical Entities of Biological Interest (ChEBI)
• Foundational Model of Anatomy (FMA)
• Gene Ontology (GO)
• Phenotypic Quality Ontology (PaTO)
• Relation Ontology (RO)
• Sequence Ontology (SO)

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Ontologies being built to satisfy Foundry
principles ab initio

• Ontology for Clinical Investigations (OCI)
• Common Anatomy Reference Ontology (CARO)
• Ontology for Biomedical Investigations (OBI)
• Protein Ontology (PRO)
• RNA Ontology (RnaO)
• Subcellular Anatomy Ontology (SAO)

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Ontologies in planning phase

• Biobank/Biorepository Ontology (BrO, part of OBI)
• Environment Ontology (EnvO)
• Immunology Ontology (ImmunO)
• Infectious Disease Ontology (IDO)
• Mouse Adult Neurogenesis Ontology (MANGO)

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OBO Foundry provides a method for handling legacy
databases
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Senselab/NeuronDB

• NeuronDB comprehends three types of neuronal
  properties
• voltage gated conductances
• neurotransmitter receptors
• neurotransmitter substances
• Many questions immediately arise what are
  receptors? Proteins? Protein complexes? The
  Foundry framework provides an opportunity to
  evaluate such choices.

http//senselab.med.yale.edu/
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Senselab/NeuronDB

• The GO Molecular Function (MF) ontology already
  has classes such as receptor activity
  (GO_0004872) plus subclasses describing receptor
  activities already referred to in NeuronDB.
• This provides a roadmap for further development.
  Review the 130 receptor classes to see if they
  exist in MF, where not, create subclasses and
  submit to GO for future inclusion. We can then
  e.g. take advantage of GO Annotations to find
  the proteins that correspond to these receptor
  classes in different species.

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OBO Foundry Success Story

• Model organism research seeks results valuable
  for the understanding of human disease.
• This requires the ability to make reliable
  cross-species comparisons, and for this anatomy
  is crucial.
• But different MOD communities have developed
  their anatomy ontologies in uncoordinated
  fashion.

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Multiple axes of classification
Functional cardiovascular system, nervous
system Spatial head, trunk, limb Developmental
endoderm, germ ring, lens placode Structural
tissue, organ, cell Stage developmental staging
series
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• Developmental terms are often lumped together for
  lack of a way to categorize them
• Stages are represented in a variety of ways.
  Terms can be children of superstages, stages can
  be integrated into each term, or stages can be
  assigned to terms from a separate ontology

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Ontologies facilitate grouping of annotations
brain 20 hindbrain 15
rhombomere 10
Query brain without ontology 20 Query brain
with ontology 45
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CARO Common Anatomy Reference Ontology

• for the first time provides guidelines for model
  organism researchers who wish to achieve
  comparability of annotations
• for the first time provides guidelines for those
  new to ontology work
• See Haendel et al., CARO The Common Anatomy
  Reference Ontology, in Burger (ed.), Anatomy
  Ontologies for Bioinformatics Springer, in press.

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CARO-conformant ontologies already in development

• Fish Multi-Species Anatomy Ontology (NSF funding
  received)
• Ixodidae and Argasidae (Tick) Anatomy Ontology
• Mosquito Anatomy Ontology (MAO)
• Spider Anatomy Ontology
• Xenopus Anatomy Ontology (XAO)
• undergoing reform Drosophila and Zebrafish
  Anatomy Ontologies

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OBI / OCI

• Ontology for Biomedical Investigations
• overarching terminology resource for MIBBI
  Foundry
• Ontology for Clinical Investigations
• collaboration with EPOCH ontology for clinical
  trial management
• and with CDISC (FDA mandated vocabulary for
  clinical trial reports)

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next step repertoire of disease ontologiesbuilt
out of OBO Foundry elements
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Scope of Draft Ontology for Multiple Sclerosis