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



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)



cBio Driving Biological Projects

Trial Bank: UCSF, Ida Sim

Flybase: Cambridge, Michael Ashburner

ZFIN: Oregon, Monte Westerfield

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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 Phenotype

(disease)

Mutant Gene


Mutant Phenotype

(disease model)

Animal disease models

SHH-/+ SHH-/-

shh-/+ shh-/-

Phenotype (clinical sign) = entity + attribute


P1 = eye + hypoteloric



P2 = midface + hypoplastic



P2 = midface + hypoplastic

P3 = kidney + hypertrophied


P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied

PATO: hypoteloric

hypoplastic

hypertrophied

ZFIN: eye

midface

kidney

+


Anatomy ontology

Cell & tissue ontology

Developmental ontology

Gene ontology

biological process

molecular function

cellular component

+ PATO(phenotype and trait ontology)


P1 = eye + hypoteloricP2 = midface + hypoplastic P3 = kidney + hypertrophied

Syndrome = P1 + P2 + P3 (disease)

= holoprosencephaly

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


(OBO)


BioPortal








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

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


context

environment

genetic


publicationfigures

evidence

assaysequence ID

ontology


environments


context

environment

genetic


publicationfigures

evidence

assaysequence ID

ontology


genotypes


(OBO)


BioPortal








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



Core 1 Components


(OBO)


BioPortal





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


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



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



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



The National Center for Biomedical Ontology Stanford – Berkeley Mayo – Victoria – Buffalo UCSF...

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