1 introduction to ontology barry smith
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1
Introduction to Ontology
Barry Smith
http://ontology.buffalo.edu/smith
Who am I?
NCBO: National Center for Biomedical Ontology (NIH Roadmap Center)
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• 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
Who am I?
NYS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group
Buffalo Clinical and Translational Science Institute (CTSI)
3
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
...4
5
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natural language labels
to make the data cognitively accessible to human beings
and algorithmically tractable to computers
7
compare: legends for mapscompare: legends for maps
8
compare: legends for mapscommon legends allow (cross-border) integration
9
ontologies are legends for data
10
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
11
annotations using common ontologies can yield integration of image data
12
computationally tractable legends
help human beings find things in very large complex representations of reality
13
where in the body ? where in the cell ?
what kind of organism ?
what kind of disease process ?
14
to yield: distributed accessibility of the data to humansreasoning with the datacumulation for purposes of researchincrementality and evolvabilityintegration 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
MouseEcotope GlyProt
DiabetInGene
GluChem
sphingolipid transporter
activity
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The Idea of Common Controlled Vocabularies
MouseEcotope GlyProt
DiabetInGene
GluChem
Holliday junction helicase complex
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
23
How to find your data?
How to find other people’s data?
How to reason with data when you find it?
How to work out what data does not yet exist?
24
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
27
most successful, thus far: UMLScollection 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 UMLSlocal 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
caBIG approach: BRIDG (top-down imposition)
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class Comprehensiv e Class-only diagram
Material MaterialRole{abstract}
FundingSponsorHealthCareSite
Inv estigator
Organization OrganizationRole{abstract}
Participant
Person
PersonRole{abstract}
TherapeuticAgent
PerformedActiv ity
PerformedStudy
PlannedActiv ity
PlannedStudy
StudyAgent
StudyInv estigator
StudySite
SubjectAssignment
StudyDocument
Dev iceDrug
Study{abstract}
StudyAuthor
Activ ity
ClinicalResult
Objectiv eResult
Observ ationRelationship
Procedure
SpecimenCollection
PerformedSubjectActiv ityObserv ation
LabTestSpecimen
LabResult
Quantitativ eMeasurement
InterpretedResult
Interpretation
Assessment{abstract}
AssessmentRelationship
Role{abstract}
RoleRoleRelationship Participation{abstract}
ConceptRelationship
PlannedObserv ation
Document{abstract}
Questionnaire
Examination VitalSign
Laboratory
CentralLaboratory
PerformingLaboratory
StudySubject
Interv ention
SubstanceAdministration
MicrobiologyTest
Organism
StudyDataProv ider
RandomizationScheme
EligibilityCriterion
StudyObjectiv e
SubGroup
SubjectVisit
Incident
ProtocolDev iation
Disposition
ClinicalIncident
MedicalHistory
StudyConductActiv ity
Adv erseEv ent
PlannedEv ent
PlannedVisitTempName:NonDrugSubstance
TestTestRelationship
TestInterpretation
InterpretedLaboratoryTest
BRIDGDosingDescriptionDataType
BRIDGCodedConcept
BRIDGContactAddress
BRIDGDescription
BRIDGID BRIDGInterv al
BRIDGStatus BRIDGTelecomAddress
PlannedStudyDrug
StudySubstanceAdministration
ConcomitantSubstanceAdministration
Image
MicrobiologyResult
Entity
Role
Participation
Activity/Act
Complex Datatype
Registry
RegistrySteward
Ev ent
Ev entEv entRelationship
CTOMActivityActivityRelationship
PerformedActiv ityRelationship
PlannedCalendar
PlannedEv entActiv ityRelationship
ScheduledEv ent
ScheduledEv entActiv ityRelationship
PlannedArm
ScheduledArm
ScheduledActiv ityScheduledCalendar
Inv estigativ eResult
StudyDesignEpoch
0..*
1
+is collecte at0..*
+collects 0..1
+source activity*
+are written by
+write
10..*
+is described by1
+describes1
1
1
1..*0..*
*
+target activity1..*
has a
1
1
has a1..*
0..*
1
1 0..*
1..1
1..*
+interprets 1
+is interpreted by 1..*
0..*1
1
0..1
0..1
1
0..* 1
0..11
1
0..*
+areattributed to
+have
+areattributed to
+have
+is fulfi ledby the role
1
+participate as
0..*
+areattributed to
+have
+is a subject-specific description of 1
+is used to create a
0..*
+are performed at
1..* +participate in 1
0..*
1
+are performed by
+participate in
0..*
1
+assign
0..*
+are assigned by1
+is described by
1..1
+is operationalized by
0..1
+is operationalized by
0..1
+is described by
1..1
1
1
+has test performed 0..*
+Is performed on
1
+receives Interventions 1
+are performed on 0..*
+is assigned to
0..*
+is responsible for 1
+is assigned to
0..*
+is the location for participation for
1
+is screened for or enrolled in
1..* +screens or enrolls 1
+must have
1
+is experienced by
1..*+may have a
1
+belongs to
0..*
+uses 0..*
+is used in 1
+target 1..*
+source
1
+are used in
+have
+is a subject-specific description of
1
+is used to create a 0..*
0..*1
0..*1
+may experience
1..
+may happen to
0..*
+uses a
+is used by
+is collected at
0..*
+collects
0..1
+has a study population defined by
+defines a patient population
0..1
0..1
0..*
1
+collects 0..1
+are collected at 0..*
0..1
1
+target
0..*
+source 1
1..*
1
+source 1
+target 0..*
+is a subject-specific description of 1
+is used to create a 0..*
+target0..*
+source
1
+compose
+are composed of
+are contained within
+contain+is used to create a 1
+is a subject-specific description of 0..*
class Comprehensiv e Class-only diagram
Material MaterialRole{abstract}
FundingSponsorHealthCareSite
Inv estigator
Organization OrganizationRole{abstract}
Participant
Person
PersonRole{abstract}
TherapeuticAgent
PerformedActiv ity
PerformedStudy
PlannedActiv ity
PlannedStudy
StudyAgent
StudyInv estigator
StudySite
SubjectAssignment
StudyDocument
Dev iceDrug
Study{abstract}
StudyAuthor
Activ ity
ClinicalResult
Objectiv eResult
Observ ationRelationship
Procedure
SpecimenCollection
PerformedSubjectActiv ityObserv ation
LabTestSpecimen
LabResult
Quantitativ eMeasurement
InterpretedResult
Interpretation
Assessment{abstract}
AssessmentRelationship
Role{abstract}
RoleRoleRelationship Participation{abstract}
ConceptRelationship
PlannedObserv ation
Document{abstract}
Questionnaire
Examination VitalSign
Laboratory
CentralLaboratory
PerformingLaboratory
StudySubject
Interv ention
SubstanceAdministration
MicrobiologyTest
Organism
StudyDataProv ider
RandomizationScheme
EligibilityCriterion
StudyObjectiv e
SubGroup
SubjectVisit
Incident
ProtocolDev iation
Disposition
ClinicalIncident
MedicalHistory
StudyConductActiv ity
Adv erseEv ent
PlannedEv ent
PlannedVisitTempName:NonDrugSubstance
TestTestRelationship
TestInterpretation
InterpretedLaboratoryTest
BRIDGDosingDescriptionDataType
BRIDGCodedConcept
BRIDGContactAddress
BRIDGDescription
BRIDGID BRIDGInterv al
BRIDGStatus BRIDGTelecomAddress
PlannedStudyDrug
StudySubstanceAdministration
ConcomitantSubstanceAdministration
Image
MicrobiologyResult
Entity
Role
Participation
Activity/Act
Complex Datatype
Registry
RegistrySteward
Ev ent
Ev entEv entRelationship
CTOMActivityActivityRelationship
PerformedActiv ityRelationship
PlannedCalendar
PlannedEv entActiv ityRelationship
ScheduledEv ent
ScheduledEv entActiv ityRelationship
PlannedArm
ScheduledArm
ScheduledActiv ityScheduledCalendar
Inv estigativ eResult
StudyDesignEpoch
0..*
1
+is collecte at0..*
+collects 0..1
+source activity*
+are written by
+write
10..*
+is described by1
+describes1
1
1
1..*0..*
*
+target activity1..*
has a
1
1
has a1..*
0..*
1
1 0..*
1..1
1..*
+interprets 1
+is interpreted by 1..*
0..*1
1
0..1
0..1
1
0..* 1
0..11
1
0..*
+areattributed to
+have
+areattributed to
+have
+is fulfi ledby the role
1
+participate as
0..*
+areattributed to
+have
+is a subject-specific description of 1
+is used to create a
0..*
+are performed at
1..* +participate in 1
0..*
1
+are performed by
+participate in
0..*
1
+assign
0..*
+are assigned by1
+is described by
1..1
+is operationalized by
0..1
+is operationalized by
0..1
+is described by
1..1
1
1
+has test performed 0..*
+Is performed on
1
+receives Interventions 1
+are performed on 0..*
+is assigned to
0..*
+is responsible for 1
+is assigned to
0..*
+is the location for participation for
1
+is screened for or enrolled in
1..* +screens or enrolls 1
+must have
1
+is experienced by
1..*+may have a
1
+belongs to
0..*
+uses 0..*
+is used in 1
+target 1..*
+source
1
+are used in
+have
+is a subject-specific description of
1
+is used to create a 0..*
0..*1
0..*1
+may experience
1..
+may happen to
0..*
+uses a
+is used by
+is collected at
0..*
+collects
0..1
+has a study population defined by
+defines a patient population
0..1
0..1
0..*
1
+collects 0..1
+are collected at 0..*
0..1
1
+target
0..*
+source 1
1..*
1
+source 1
+target 0..*
+is a subject-specific description of 1
+is used to create a 0..*
+target0..*
+source
1
+compose
+are composed of
+are contained within
+contain+is used to create a 1
+is a subject-specific description of 0..*
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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
for science
caBIG
BRIDG
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Top-down (master-model-based)
Bottom-up (evidence-based)
prospective standardization
caBIGSNOMEDHL7
OBO Foundry
retrospective mapping
UMLS (multiple authorities)
NLP / data + text-mining
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
or 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 63:907–917 (2006)
legends for chemistry diagrams
Ramirez et al. Linking of Digital Images to Phylogenetic Data Matrices Using a Morphological OntologySyst. Biol. 56(2):283–294, 2007
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The Network Effects of Synchronization
MouseEcotope GlyProt
DiabetInGene
GluChem
Holliday junction helicase complex
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Five bangs for your GO buck1. based in biological science
2. incremental approach (evidence-based evolutionary pathway)
3. cross-species data comparability (human, mouse, yeast, fly ...)
4. cross-granularity data integration (molecule, cell, organ, organism)
5. 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 problemexisting 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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a shared portal for (so far) 58 ontologies (low regimentation)
http://obo.sourceforge.net NCBO BioPortal
First step (2003)
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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
id: CL:0000062name: osteoblastdef: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." is_a: CL:0000055relationship: develops_from CL:0000008relationship: develops_from CL:0000375
GO
Cell type
New Definition
+
=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.
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The OBO FoundryThe OBO Foundryhttp://obofoundry.org/http://obofoundry.org/
Third step (2006)Third step (2006)
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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/chebiPaula 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
analysisfugo.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)
relationsobo.sf.net/
relationshipBarry 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 OCCURRENT
INDEPENDENT DEPENDENT
ORGAN ANDORGANISM
Organism(NCBI
Taxonomy)
Anatomical Entity(FMA, CARO)
OrganFunction
(FMP, CPRO) Phenotypic
Quality(PaTO)
Biological Process
(GO)CELL AND CELLULAR
COMPONENT
Cell(CL)
Cellular Compone
nt(FMA, GO)
Cellular Function
(GO)
MOLECULEMolecule
(ChEBI, SO,RnaO, PrO)
Molecular Function(GO)
Molecular Process
(GO)
Building out from the original GO
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CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN ANDORGANISM
Organism(NCBI
Taxonomy)
Anatomical Entity
(FMA, CARO)
OrganFunction
(FMP, CPRO) Phenotypic
Quality(PaTO)
Organism-Level Process
(GO)
CELL AND CELLULAR
COMPONENT
Cell(CL)
Cellular Compone
nt(FMA, GO)
Cellular Function
(GO)
Cellular Process
(GO)
MOLECULEMolecule
(ChEBI, SO,RnaO, PrO)
Molecular Function(GO)
Molecular Process
(GO)
initial OBO Foundry coverage
GRANULARITY
RELATION TO TIME
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Continuants (aka endurants)have continuous existence in timepreserve their identity through changeexist in toto whenever they exist at all
Occurrents (aka processes)have temporal partsunfold themselves in successive phasesexist 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
56
Dependent entities
require independent continuants as their bearers
There is no run without a runner
There is no grin without a cat
57
Dependent vs. independent continuants
Independent continuants (organisms, buildings, environments)
Dependent continuants (quality, shape, role, propensity, function, status, power, right)
58
All occurrents are dependent entities
They are dependent on those independent continuants which are their participants (agents, patients, media ...)
59
BFO Top-Level Ontology
ContinuantOccurrent
(always dependent on one or more
independent continuants)
IndependentContinuant
DependentContinuant
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= A representation of top-level types
Continuant Occurrent
IndependentContinuant
DependentContinuant
cell component
biological process
molecular function
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Top-Level Ontology
Continuant Occurrent
IndependentContinuant
DependentContinuant
Functioning
Side-Effect, Stochastic Process, ...
Function
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Top-Level Ontology
Continuant Occurrent
IndependentContinuant
DependentContinuant
Functioning Side-Effect, Stochastic Process, ...
Function
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Top-Level Ontology
Continuant Occurrent
IndependentContinuant
DependentContinuant
Quality Function Spatial Region
Functioning Side-Effect, Stochastic Process, ...
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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CRITERIA 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.
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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
67
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
68
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
69
COMMON ARCHITECTURE: The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology
CRITERIA
70
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
71
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
72
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)
73
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)
74
Ontologies in planning phaseBiobank/Biorepository Ontology (BrO, part of OBI)Environment Ontology (EnvO) Immunology Ontology (ImmunO)Infectious Disease Ontology (IDO)Mouse Adult Neurogenesis Ontology (MANGO)
OBO Foundry provides a method for handling legacy databases
75
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.
76
* http://senselab.med.yale.edu/
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.
77
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.
78
Multiple axes of classification
Functional: cardiovascular system, nervous systemSpatial: head, trunk, limbDevelopmental: endoderm, germ ring, lens placodeStructural: 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
Ontologies facilitate grouping of annotations
brain 20 hindbrain 15 rhombomere 10
Query brain without ontology 20Query 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 OntologyXenopus Anatomy Ontology (XAO)
undergoing reform: Drosophila and Zebrafish Anatomy Ontologies
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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INDEPENDENT
CONTINUANTS
organism
system
organ
organ part
tissue
cell
acellular anatomical structure
biological molecule
genome
DEPENDENT CONTINUANTS
physiology
(functions)
pathologyacute stage
progressive stage
resolution stage
next step: repertoire of disease ontologiesbuilt out of OBO Foundry elements
86
Scope of Draft Ontology for Multiple Sclerosis