the national center for biomedical ontology stanford – berkeley mayo – victoria – buffalo ucsf...
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The National Center for Biomedical
Ontology
Stanford – Berkeley Mayo – Victoria – Buffalo
UCSF – Oregon – Cambridge
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
eye
what kinds of things exist?
what are the relationships between these things?
ommatidium
sense organ
eye disc
is_a
part_of
developsfrom
The Foundational Model of The Foundational Model of AnatomyAnatomy
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
Open Biomedical Ontologies
(OBO)
Open Biomedical Data (OBD)
BioPortal
Capture and index experimental results
Revise biomedicalunderstanding
Relate experimental data to results from other sources
National Center for Biomedical Ontology
Stanford: Tools for ontology alignment, indexing, and management (Cores 1, 4–7: Mark Musen)
Lawrence–Berkeley Labs: Tools to use ontologies for data annotation (Cores 2, 5–7: 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
OntologyCore 3: Driving
Biological ProjectsMonte Westerfield
Animal models
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
Animal disease models
Humans Animal models
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease)
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease model)
Animal disease models
Humans Animal models
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease)
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease model)
Animal disease models
Humans Animal models
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease)
Mutant Gene
Mutant or missing Protein
Mutant Phenotype
(disease model)
Animal disease models
SHH-/+ SHH-/-
shh-/+ shh-/-
Phenotype (clinical sign) = entity + attribute
Phenotype (clinical sign) = entity + attribute
P1 = eye + hypoteloric
Phenotype (clinical sign) = entity + attribute
P1 = eye + hypoteloric
P2 = midface + hypoplastic
Phenotype (clinical sign) = entity + attribute
P1 = eye + hypoteloric
P2 = midface + hypoplastic
P3 = kidney + hypertrophied
Phenotype (clinical sign) = entity + attribute
P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied
PATO: hypoteloric
hypoplastic
hypertrophied
ZFIN: eye
midface
kidney
+
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)
Phenotype (clinical sign) = entity + attribute
P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied
Syndrome = P1 + P2 + P3 (disease)
= holoprosencephaly
Human holo-prosencephaly
Zebrafishshh
Zebrafishoep
Human holo-prosencephaly
Zebrafishshh
Zebrafishoep
ZFINmutantgenes
ZFINmutantgenes
OMIMgenes
OMIMgenes
ZFINmutantgenes
FlyBasemutantgenes
OMIM gene
ZFIN gene
FlyBase gene
FlyBase mut pub
ZFIN mut pub
mouse rat SNOMED
OMIM disease
LAMB1 lamb1 LanB1 5 15 39 -
FECH fech Ferro-
chelatase
2 5 2 29 Protoporphyria, Erythropoietic
GLI2 gli2a ci 388 41 22 -
SLC4A1 slc4a1 CG8177 7 7 19 Renal Tubular Acidosis, RTADR
MYO7A myo7a ck 84 5 9 3 16 Deafness; DFNB2; DFNA11
ALAS2 alas2 Alas 1 7 14 Anemia, Sideroblastic, X-Linked
KCNH2 kcnh2 sei 27 3 12 -
MYH6 myh6 Mhc 166 3 1 12 Cardiomyopathy, Familial Hypertrophic; CMH
TP53 tp53 p53 64 3 3 19 11 Breast Cancer
ATP2A1 atp2a1 Ca-P60A 32 6 1 11 Brody Myopathy
EYA1 eya1 eya 251 5 4 6 Branchiootorenal Dysplasia
SOX10 sox10 Sox100B 1 17 4 4 Waardenburg-Shah Syndrome
Open Biomedical Ontologies
(OBO)
Open Biomedical Data (OBD)
BioPortal
Capture and index experimental results
Revise biomedicalunderstanding
Relate experimental data to results from other sources
National Center for Biomedical Ontology
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The National Center for Biomedical
OntologyCore 2: Bioinformatics
Suzanna Lewis
cBio Bioinformatics Goals
1. Apply ontologies Software toolkit for annotation
2. Manage data Databases and interfaces to store and
view annotations
3. Investigate and compare Linking human diseases to genetic
models
4. Maintain Ongoing reconciliation of ontologies
with annotations
cBio Bioinformatics Goals
1. Apply ontologies Software toolkit for annotation
2. Manage data Databases and interfaces to store and
view annotations
3. Investigate and compare Linking human diseases to genetic
models
4. Maintain Ongoing reconciliation of ontologies
with annotations
Phenotype as an observation
context
environment
genetic
The class of thing observed
publicationfigures
evidence
assaysequence ID
ontology
Phenotype from published evidence
Ontologies enable users to describe
assays
Phenotype as an observation
context
environment
genetic
The class of thing observed
publicationfigures
evidence
assaysequence ID
ontology
Ontologies enable users to describe
environments
Phenotype as an observation
context
environment
genetic
The class of thing observed
publicationfigures
evidence
assaysequence ID
ontology
Ontologies enable users to describe
genotypes
Open Biomedical Ontologies
(OBO)
Open Biomedical Data (OBD)
BioPortal
Capture and index experimental results
Revise biomedicalunderstanding
Relate experimental data to results from other sources
National Center for Biomedical Ontology
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The National Center for Biomedical
OntologyCore 1: Computer Science
Mark Musen
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
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?
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
Open Biomedical Ontologies
(OBO)
Open Biomedical Data (OBD)
BioPortal
Capture and index experimental results
Revise biomedicalunderstanding
Relate experimental data to results from other sources
National Center for Biomedical Ontology
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 sitesQuickTime™ and a
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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 programQuickTime™ and aTIFF (Uncompressed) decompressor
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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 24–25, 2006
Training in Biomedical Ontology Schloss Dagstuhl, May 21–24, 2006
Training in Biomedical Ontology Baltimore, November 6–8, 2006 (in association with FOIS and AMIA conferences)
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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PI Mark Musen
Co-PI Suzanna Lewis
Other NCBC Centers
NIH Program Officer and
Science Officers
Biomedical Science Community
Scientific AdvisoryCommittee
BiomedicalComputingCommunity
Center DirectorDaniel Rubin
Associate DirectorSima Misra
Business ManagerRosalind Ravasio
Administrative Asst.Donna Mahood
Executive CommitteeMusen, Lewis, Rubin, Misra
cBIO CouncilMusen, Lewis, Rubin, Misra, Smith, Storey,
Chute, Ashburner, Westerfield, Sim
Core 1 LeadMark Musen
Core 6 LeadBarry Smith
Core 5 LeadMark Musen
Core 4 LeadDaniel Rubin
Core 3 LeadExec Committee
Core 2 LeadSuzanna Lewis
cBiO Organization Chart
Ontologies are essential to make sense of biomedical data
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