How Ontologies Create Research Communities

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How Ontologies Create Research Communities

• Barry Smith
• University at Buffalo
• 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)
• Duke/Dallas/Houston CTSA Ontology Consortium

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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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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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where in the body ? where in the cell ?
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where in the body ? where in the cell ?
what kind of organism ?
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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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we need semantic annotation of data
we need ontologies
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natural language labels designed for use
in annotations
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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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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computationally tractable legends

• help integrate complex representations of
  reality
• help human beings find things in complex
  representations of reality
• help computers reason with complex
  representations of reality

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The Gene Ontology
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what cellular component?
what molecular function?
what biological process?
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The Idea of Common Controlled Vocabularies
GlyProt
MouseEcotope
sphingolipid transporter activity
DiabetInGene
GluChem
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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

• 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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what cellular component?
what molecular function?
what biological process?
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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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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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Ontology Scope URL Custodians
Cell Ontology (CL) cell types from prokaryotes to mammals obo.sourceforge.net/cgi- bin/detail.cgi?cell Jonathan Bard, Michael Ashburner, Oliver Hofman
Chemical Entities of Bio- logical Interest (ChEBI) molecular entities ebi.ac.uk/chebi Paula Dematos, Rafael Alcantara
Common Anatomy Refer- ence Ontology (CARO) anatomical structures in human and model organisms (under development) Melissa Haendel, Terry Hayamizu, Cornelius Rosse, David Sutherland,
Foundational Model of Anatomy (FMA) structure of the human body fma.biostr.washington. edu JLV Mejino Jr., Cornelius Rosse
Functional Genomics Investigation Ontology (FuGO) design, protocol, data instrumentation, and analysis fugo.sf.net FuGO Working Group
Gene Ontology (GO) cellular components, molecular functions, biological processes www.geneontology.org Gene Ontology Consortium
Phenotypic Quality Ontology (PaTO) qualities of anatomical structures obo.sourceforge.net/cgi -bin/ detail.cgi? attribute_and_value Michael Ashburner, Suzanna Lewis, Georgios Gkoutos
Protein Ontology (PrO) protein types and modifications (under development) Protein Ontology Consortium
Relation Ontology (RO) relations obo.sf.net/relationship Barry Smith, Chris Mungall
RNA Ontology (RnaO) three-dimensional RNA structures (under development) RNA Ontology Consortium
Sequence Ontology (SO) properties and features of nucleic sequences song.sf.net Karen Eilbeck
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RELATION TO TIME GRANULARITY CONTINUANT CONTINUANT CONTINUANT CONTINUANT OCCURRENT
RELATION TO TIME GRANULARITY INDEPENDENT INDEPENDENT DEPENDENT DEPENDENT
ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality(PaTO) Biological Process (GO)
CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) Phenotypic Quality(PaTO) Biological Process (GO)
MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Function (GO) Molecular Process (GO)
Building out from the original GO
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CONTINUANT CONTINUANT CONTINUANT CONTINUANT OCCURRENT
INDEPENDENT INDEPENDENT DEPENDENT DEPENDENT
ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality(PaTO) Organism-Level Process (GO)
CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) Phenotypic Quality(PaTO) Cellular Process (GO)
MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Function (GO) Molecular Process (GO)
initial OBO Foundry coverage
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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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for science

• 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 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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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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• 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).

Minimal Information Checklists
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• MIBBI a common resource for minimum information
  checklists analogous to OBO / NCBO BioPortal
• MIBBI Foundry will create a suite of
  self-consistent, clearly bounded, orthogonal,
  integrable checklist modules
• Taylor CF, et al. Nature Biotech, in press

The vision is spreading
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• Transcriptomics (MIAME Working Group / MGED)
• 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)

MIBBI Foundry communities
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OBI / OCI

• Ontology for Biological 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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INDEPENDENT CONTINUANTS
organism
system
organ
organ part
tissue
cell
acellular anatomical structure
biological molecule
genome
DEPENDENT CONTINUANTS DEPENDENT CONTINUANTS DEPENDENT CONTINUANTS DEPENDENT CONTINUANTS
physiology (functions) pathology pathology pathology
physiology (functions) acute stage progressive stage resolution stage
next step repertoire of disease ontologiesbuilt
out of OBO Foundry elements
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Draft Ontology for Multiple Sclerosis
to apprehend what is unknown requires a complete
demarcation of the relevant space of alternatives


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CTSA Ontology Consortium

• Duke Clinical Research Institute (DCRI)
• Dallas University of Texas Southwestern Medical
  Center Clinical and Translational Science
  Initiative Division of Biomedical Informatics
• University of Texas Health Science Center at
  Houston Center for Clinical and Translational
  Sciences

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Multiple kinds of standardization for data

• Terminologies (SNOMED, UMLS)
• CDEs (Clinical research)
• Ontologies (Biology, Disease Models)
• Information Exchange Standards (HL7 RIM)
• LIMS (LOINC)
• Duke DCRI project to deal with 3 of these

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Houston CTSA Biomedical Informatics

• Specific aim 1 To design and implement the
  biological data interface ... based on existing
  biological ontologies, specifically those
  included in the NIH Roadmap funded Open
  Biomedical Ontologies (OBO) project, and to
  leverage previous informatics research in
  ontology management.

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Houston CTSA proposal

• providing a coherent and integrated framework
  for CTSI investigators to integrate disparate
  sources of data, improve the communication among
  researchers, and establish better contact between
  researchers and the community. Of critical
  importance, by combining isolated data clusters
  the biomedical informatics component will empower
  investigators to redefine human disease and the
  response to diagnostic and therapeutic strategies
  through the use of combined clinical and
  molecular profiling.

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PAR-07-425 Data Ontologies for Biomedical
Research (R01)

• Adoption of ontologies also depends on the
  ontology being in a format that is broadly
  supported, fully machine interpretable and not
  subject to intellectual property restrictions.
  ... Another determinate of ontology acceptance is
  the degree to which the ontology conforms to best
  practices governing ontology design and
  construction. Criteria have been developed, and
  are undergoing empirical validation, by the
  Vocabulary and Common Data Element Work Group of
  caBIG. Other criteria have been specified by the
  OBO Foundry (http//obofoundry.org).

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Top-down (master-model-based) Bottom-up (evidence-based)
prospective standardization caBIG SNOMED HL7 OBO Foundry
retrospective mapping UMLS (multiple authorities) NLP / data text-mining

• 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.