The OBO Foundry

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The OBO Foundry
Ontology A Vision for the Future and Its
Realization

• Barry Smith
• University at Buffalo
• http//ontology.buffalo.edu/smith

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• how do we know what data we have ?
• how do I know what data you have ?
• how do we know what data we dont have ?
• how do we make different sorts of data
  combinable, as we need to do in large domains
  such as neurodevelopment, immunology, cancer ...?

we are accumulating huge amounts of sequence
data, image data, pharma data, ...
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• genomic medicine, molecular medicine,
  translational medicine, personalized medicine ...
  need
• methods for data integration to enable reasoning
  across data at multiple granularities

to identify biomedically relevant relations on
the side of the entities themselves
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where in the body ?
what kind of disease process ?
we need semantic annotation of data
we need ontologies
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• Semantic Web, Moby, wikis, etc.
• let a million flowers (and weeds) bloom
• to create integration rely on (automatically
  generated?) post hoc mappings

how create broad-coverage semantic annotation
systems for biomedicine?
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most successful, thus far UMLS

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

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UMLS-based mappings fall shortof creating
interoperability

• because local usage is respected
• regimentation frowned upon, no concern for
  cross-framework consistency
• UMLS terminologies have different grades of
  formal rigor, different degrees of completeness,
  different update policies

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with UMLS-based annotations

• we can know what data we have (via term
  searches), but it is noisy
• we can map between data at single granularities
  (via synonyms), but synonymy information is
  noisy
• how do we know what data we dont have ?
• how do we reason with data (as at the molecular
  level), when no common logical backbone ?

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for science

• to develop high quality annotation resources in a
  collaborative, community effort?
• create an evolutionary path towards improvement
  of terminologies, of the sort we find elsewhere
  in science
• find ways to reward early adopters of the results

what is to be done?
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for science

• science works out from a consensus core, and
  strives to isolate and resolve inconsistencies as
  it extends at the fringes
• we need to create a consensus core
• start with what for human beings are trivialities
  (low hanging fruit) and work out from there

for science, consistency is a sine qua non
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Anatomical Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
is_a
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
part_of
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
FMA
Foundational Model of Anatomy
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for science

• include ontologies corresponding to the basic
  biomedical sciences in the core

clinical medicine relies on anatomy and molecular
biology to provide integration across medical
specialisms
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for science

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

but we need more
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what makes GO so wildly successful ?
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• science basis of the GO trained experts curating
  peer-reviewed literature
• different model organism databases employ
  scientific curators who use the experimental
  observations reported in the biomedical
  literature to associate GO terms with gene
  products in a coordinated way

The methodology of annotations
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• cellular locations
• molecular functions
• biological processes
• used to annotate the entities represented in the
  major biochemical databases
• thereby creating integration across these
  databases and making them available to semantic
  search

A set of standardized textual descriptions of
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what cellular component?
what molecular function?
what biological process?
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This process

• leads to improvements and extensions of the
  ontology
• which in turn leads to better annotations
• ? a virtuous cycle of improvement in the quality
  and reach of both future annotations and the
  ontology itself
• RESULT a slowly growing computer-interpretable
  map of biological reality within which major
  databases are automatically integrated in
  semantically searchable form

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Five bangs for your GO buck

• science base
• cross-species database integration
• cross-granularity database integration
• through links to the things which are of
  biomedical relevance
• ? semantic searchability links people to software

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but now
need to improve the quality of GO to support
more rigorous logic-based reasoning across the
data annotated in its terms need to extend the GO
by engaging ever broader community support for
the addition of new terms and for the correction
of errors
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but also
need to extend the methodology to other domains,
including clinical domains ? need for disease
ontology immunology ontology symptom
(phenotype) ontology clinical trial ontology ...
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the problem
existing clinical vocabularies are of variable
quality and low mutual consistency need for
prospective standards to ensure mutual
consistency and high quality of clinical
counterparts of GO need to ensure consistency of
the new clinical ontologies with the basic
biomedical sciences if we do not start now, the
problem will only get worse
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the solution

• establish common rules governing best practices
  for creating ontologies and for using these in
  annotations
• apply these rules to create a complete suite of
  orthogonal interoperable biomedical reference
  ontologies
• this solution is already being implemented

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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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Second step (2004)reform efforts initiated, e.g.
linking GO to other OBO ontologies to ensure
orthogonality
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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• a family of interoperable gold standard
  biomedical reference ontologies to serve the
  annotation of inter alia
• scientific literature
• model organism databases
• clinical trial data

The OBO Foundry
The OBO Foundry http//obofoundry.org/
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A prospective standard

• designed to guarantee interoperability of
  ontologies from the very start (contrast to post
  hoc mapping)
• established March 2006
• 12 initial candidate OBO ontologies focused
  primarily on basic science domains
• several being constructed ab initio
• by influential consortia who have the authority
  to impose their use on large parts of the
  relevant communities.

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• GO Gene Ontology
• ChEBI Chemical Ontology
• CL Cell Ontology
• FMA Foundational Model of Anatomy
• PaTO Phenotype Quality Ontology
• SO Sequence Ontology
• CARO Common Anatomy Reference Ontology
• CTO Clinical Trial Ontology
• FuGO Functional Genomics Investigation Ontology
• PrO Protein Ontology
• RnaO RNA Ontology
• RO Relation Ontology

new
The OBO Foundry http//obofoundry.org/
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Annotations plus ontologies yield an ever-growing
computer-interpretable map of biological
reality.
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Building out from the original GO
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• Disease Ontology (DO)
• Biomedical Image Ontology (BIO)
• Upper Biomedical Ontology (OBO UBO)
• Environment Ontology (EnvO)
• Systems Biology Ontology (SBO)

Under consideration
The OBO Foundry http//obofoundry.org/
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• OBO Foundry a subset of OBO ontologies, whose
  developers have agreed in advance to accept a
  common set of principles reflecting best practice
  in ontology development designed to ensure
• tight connection to the biomedical basic
  sciences
• compatibility
• interoperability, common relations
• formal robustness
• support for logic-based reasoning

The OBO Foundry http//obofoundry.org/
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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
The OBO Foundry http//obofoundry.org/
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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
The OBO Foundry http//obofoundry.org/
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for science

• if we annotate a database or body of literature
  with one high-quality biomedical ontology, we
  should be able to add annotations from a second
  such ontology without conflicts

orthogonality of ontologies implies additivity of
annotations
The OBO Foundry http//obofoundry.org/
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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 to
  ensure BACKWARDS COMPATIBITY with annotation
  resources already in common use
• The ontology includes TEXTUAL DEFINITIONS and
  where possible equivalent formal definitions of
  its 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
The OBO Foundry http//obofoundry.org/
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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.
• Smith et al., Genome Biology 2005, 6R46

CRITERIA
The OBO Foundry http//obofoundry.org/
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OBO Relation Ontology
The OBO Foundry http//obofoundry.org/
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• Further criteria will be added over time in light
  of lessons learned in order to bring about a
  gradual improvement in the quality of Foundry
  ontologies
• ALL FOUNDRY ONTOLOGIES WILL BE SUBJECT TO
  CONSTANT UPDATE IN LIGHT OF SCIENTIFIC ADVANCE

IT WILL GET HARDER
The OBO Foundry http//obofoundry.org/
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• But not everyone needs to join
• The Foundry is not seeking to serve as a check on
  flexibility or creativity
• ALL FOUNDRY ONTOLOGIES WILL ENCOURAGE COMMUNITY
  CRITICISM, CORRECTION AND EXTENSION WITH NEW
  TERMS

IT WILL GET HARDER
The OBO Foundry http//obofoundry.org/
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• to introduce some of the features of SCIENTIFIC
  PEER REVIEW into biomedical ontology development
• CREDIT for high quality ontology development work
• KUDOS for early adopters of high quality
  ontologies / terminologies e.g. in reporting
  clinical trial results

GOALS
The OBO Foundry http//obofoundry.org/
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• to providing a FRAMEWORK OF RULES to counteract
  the current policy of ad hoc creation of new
  annotation schemas by each clinical research
  group by
• REUSABILITY if data-schemas are formulated using
  a single well-integrated framework ontology
  system in widespread use, then this data will be
  to this degree itself become more widely
  accessible and usable

GOALS
The OBO Foundry http//obofoundry.org/
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• to serve as BENCHMARK FOR IMPROVEMENTS in
  discipline-focused terminology resources
• once a system of interoperable reference
  ontologies is there, it will make sense to
  calibrate existing terminologies in its terms in
  order to achieve more robust alignment and
  greater domain coverage
• exploit the avenue of EVIDENCE-BASED MEDICINE
  (NIH CLINICAL RESEARCH NETWORKS) to foster their
  use by clinicians

GOALS
The OBO Foundry http//obofoundry.org/
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• June 2006 establishment of MICheck
• reflects growing need for prescriptive
  checklists specifying the key information to
  include when reporting experimental results
  (concerning methods, data, analyses and results).

the vision is spreading
The OBO Foundry http//obofoundry.org/
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• MICheck a common resource for minimum
  information checklists analogous to OBO / NCBO
  BioPortal
• MICheck Foundry will create a suite of
  self-consistent, clearly bounded, orthogonal,
  integrable checklist modules
• Taylor CF, et al. Nature Biotech, in press

MICheck Foundry
The OBO Foundry http//obofoundry.org/
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• Transcriptomics (MIAME Working Group)
• Proteomics (Proteomics Standards Initiative)
• Metabolomics (Metabolomics Standards Initiative)
• Genomics and Metagenomics (Genomic Standards
  Consortium)
• In Situ Hybridization and Immunohistochemistry
  (MISFISHIE Working Group)
• Phylogenetics (Phylogenetics Community)
• RNA Interference (RNAi Community)
• Toxicogenomics (Toxicogenomics WG)
• Environmental Genomics (Environmental Genomics
  WG)
• Nutrigenomics (Nutrigenomics WG)
• Flow Cytometry (Flow Cytometry Community)

MICheck/Foundry communities
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• how to replicate the successes of the GO in
  clinical medicine?
• choose two or three representative disease
  domains
• work out reasoning challenges for those domains
• work with specialists to create ontologies
  interoperable with OBO Foundry basic science
  ontologies to address these reasoning challenges
• work with leaders of professional associations
  and of clinical trial initiatives to foster the
  collection of clinical data annotated in their
  terms

Fourth Step (the future)
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OBO Foundry coverage (canonical ontologies)
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Draft Ontology for Acute Respiratory Distress
Syndrome
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Draft Ontology for Muscular Sclerosis
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Draft Ontology for Muscular Sclerosis
to apprehend what is unknown requires a complete
demarcation of the relevant space of alternatives


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with thanks (inter alia) to
with thanks to