The National Center for Biomedical Ontology

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The National Center for Biomedical Ontology

• Stanford Berkeley Mayo Victoria Buffalo
  UCSF Oregon Cambridge

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Ontologies are essential to make sense of
biomedical data
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A biological ontology is

• A machine interpretable representation of some
  aspect of biological reality

• what kinds of things exist?

sense organ
eye disc
develops from
is_a

• what are the relationships between these things?

eye
part_of
ommatidium
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The Foundational Model of Anatomy
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Knowledge workers seem trapped in a
pre-industrial age

• Most ontologies are
• Of relatively small scale
• Built by small groups working arduously in
  isolation
• Success rests heavily on the particular talents
  of individual artisans, rather than on SOPs and
  best practices
• There are few technologies available to make this
  process faster, better, cheaper

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A Portion of the OBO Library
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National Center for Biomedical Ontology
Capture and index experimental results
Open Biomedical Ontologies (OBO)
Open Biomedical Data (OBD)
BioPortal
Revise biomedicalunderstanding
Relate experimental data to results from other
sources
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• Stanford Tools for ontology alignment,
  indexing, and management (Cores 1, 47 Mark
  Musen)
• LawrenceBerkeley Labs Tools to use ontologies
  for data annotation (Cores 2, 57 Suzanna Lewis)
• Mayo Clinic Tools for access to large
  controlled terminologies (Core 1 Chris Chute)
• Victoria Tools for ontology and data
  visualization (Cores 1 and 2 Margaret-Anne
  Story)
• University at Buffalo Dissemination of best
  practices for ontology engineering (Core 6 Barry
  Smith)

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cBio Driving Biological Projects

• Trial Bank UCSF, Ida Sim
• Flybase Cambridge, Michael Ashburner
• ZFIN Oregon, Monte Westerfield

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The National Center for Biomedical Ontology

• Core 3 Driving Biological Projects
• Monte Westerfield

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Animal disease models
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype
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Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
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Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
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Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease)
Animal disease models
Humans
Animal models
Mutant Gene Mutant or missing
ProteinMutant Phenotype (disease model)
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SHH-/
SHH-/-
shh-/
shh-/-
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Phenotype (clinical sign) entity
attribute
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Phenotype (clinical sign) entity
attribute P1 eye hypoteloric
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Phenotype (clinical sign) entity
attribute P1 eye hypoteloric P2
midface hypoplastic
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Phenotype (clinical sign) entity
attribute P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
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Phenotype (clinical sign) entity
attribute P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
PATO hypoteloric hypoplastic
hypertrophied
ZFIN eye midface kidney

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Phenotype (clinical sign) entity
attribute
Anatomy ontology Cell tissue ontology
Developmental ontology Gene ontology
biological process molecular function
cellular component
PATO (phenotype and trait ontology)
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Phenotype (clinical sign) entity
attribute P1 eye hypoteloric P2
midface hypoplastic P3 kidney
hypertrophied
Syndrome P1 P2 P3 (disease)
holoprosencephaly
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Human holo- prosencephaly
Zebrafish shh
Zebrafish oep
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Human holo- prosencephaly
Zebrafish shh
Zebrafish oep
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ZFIN mutant genes
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OMIM genes
ZFIN mutant genes
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OMIM genes
ZFIN mutant genes
FlyBase mutant genes
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National Center for Biomedical Ontology
Capture and index experimental results
Open Biomedical Ontologies (OBO)
Open Biomedical Data (OBD)
BioPortal
Revise biomedicalunderstanding
Relate experimental data to results from other
sources
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The National Center for Biomedical Ontology

• Core 2 Bioinformatics
• Suzanna Lewis

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cBio Bioinformatics Goals

• Apply ontologies
• Software toolkit for annotation
• Manage data
• Databases and interfaces to store and view
  annotations
• Investigate and compare
• Linking human diseases to genetic models
• Maintain
• Ongoing reconciliation of ontologies with
  annotations

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cBio Bioinformatics Goals

• Apply ontologies
• Software toolkit for annotation
• Manage data
• Databases and interfaces to store and view
  annotations
• Investigate and compare
• Linking human diseases to genetic models
• Maintain
• Ongoing reconciliation of ontologies with
  annotations

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Elicitation of Requirements for Annotation Tools

• Applications pull from pioneer users in Core 3
• ZFIN
• FlyBase
• Trial Bank
• Study these groups currently annotate data
• Determine how our Core 2 tools can integrate with
  existing data flows and databases
• Evaluate the commonalities and differences among
  approaches

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Development of Data-Annotation Tool

• Develop plug-in architecture
• Default user interface for generic
  data-annotation tasks
• Custom-tailored interfaces for particular
  biomedical domains
• Enable interoperability with existing
  ontology-management platforms
• Integrate ontology-annotation tool with BioPortal
• Access ontologies for data annotation from OBO
• Store data annotations in OBD

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Phenotype as an observation
context
The class of thing observed
evidence
publication
environment
figures
assay
genetic
sequence ID
ontology
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Phenotype from published evidence
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Ontologies enable users to describe assays
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Phenotype as an observation
context
The class of thing observed
evidence
publication
environment
figures
assay
genetic
sequence ID
ontology
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Ontologies enable users to describe environments
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Phenotype as an observation
context
The class of thing observed
evidence
publication
environment
figures
assay
genetic
sequence ID
ontology
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Ontologies enable users to describe genotypes
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Phenotypes as collections

• Coincidence
• Same organism, same time
• Relative
• Reduced, enhanced
• Same focus of observation
• All left hands
• Differing levels of scale
• Molecular, cellular, organismal
• Recognizable patterns
• Set of observations that describe a disease

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National Center for Biomedical Ontology
Capture and index experimental results
Open Biomedical Ontologies (OBO)
Open Biomedical Data (OBD)
BioPortal
Revise biomedicalunderstanding
Relate experimental data to results from other
sources
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The National Center for Biomedical Ontology

• Core 1 Computer Science
• Mark Musen

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E-science needs technologies

• To help build and extend ontologies
• To locate ontologies and to relate them to one
  another
• To visualize relationships and to aid
  understanding
• To facilitate evaluation and annotation of
  ontologies

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We need to relate ontologies to one another

• We keep reinventing the wheel
• We dont even know whats out there!
• We need to make comparisons between ontologies
  automatically
• We need to keep track of ontology history and to
  compare versions

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We need to compute both similarities and
differences

• Similarities
• Merging ontologies
• Mapping ontologies
• Differences
• Versioning

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Ontology engineering requires management of
complexity

• How can we
• keep track of hundreds of relationships?
• understand the implications of changes to a large
  ontology?
• know where ontologies are underspecified? And
  where they are over constrained?

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E-science needs technologies

• To help build and extend ontologies
• To locate ontologies and to relate them to one
  another
• To visualize relationships and to aid
  understanding
• To facilitate evaluation and annotation of
  ontologies

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Core 1 Components
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Core 1 Contributors

• Stanford Tools for ontology management,
  alignment, versioning, metadata management,
  automated critiquing, and peer review
• Mayo LexGrid technology for access to large
  controlled terminologies, ontology indexing,
  Soundex, search
• Victoria Technology for ontology visualization

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National Center for Biomedical Ontology
Capture and index experimental results
Open Biomedical Ontologies (OBO)
Open Biomedical Data (OBD)
BioPortal
Revise biomedicalunderstanding
Relate experimental data to results from other
sources
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Core 4 Infrastructure

• Builds on existing IT infrastructure at Stanford
  and at our collaborating institutions
• Adds
• Online resources and technical support for the
  user community
• Collaboration tools to link all participating
  sites

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Core 5 Education and Training

• Builds on existing, strong informatics training
  programs at Stanford, Berkeley, UCSF,
  Mayo/Minnesota, and Buffalo
• New postdoctoral positions at Stanford, Berkeley,
  and Buffalo
• New visiting scholars program

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Core 6 Dissemination

• Active relationships with relevant professional
  societies and agencies (e.g., HL7, IEEE, WHO,
  NIH)
• Internet-based resources for discussing,
  critiquing, and annotating ontologies in OBO
• Cooperation with other NCBCs to offer a library
  of open-source software tools
• Training workshops to aid biomedical scientists
  in ontology development

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Upcoming cBio Dissemination Workshops

• Image Ontology Workshop Stanford CA, March
  2425, 2006
• Training in Biomedical Ontology Schloss
  Dagstuhl, May 2124, 2006
• Training in Biomedical Ontology Baltimore,
  November 68, 2006 (in association with FOIS and
  AMIA conferences)

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Core 7 Administration

• Project management shared between Stanford and
  Berkeley
• Executive committee (PI, co-PI, Center director,
  and Center associate director) provides
  day-to-day management and oversight
• Council (All site PIs, including PIs of DBPs)
  provides guidance and coordination of work plans
• Each Core has a designated lead selected from
  the Council

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cBiO Organization Chart
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Ontologies are essential to make sense of
biomedical data