1. Ontology for Biomedical Investigation
3/16/2016, ACS, San Diego
Bjoern Peters
La Jolla Institute for Allergy and Immunology

2. My hats
High throughput wetlab-studies mandated to submit data to repositories
Bioinformatics core; data management and analysis (at institute + for multi-project grants)
Immune Epitope Database
Manual literature curation
Receiving data submissions from
Development of machine learning tools
Ontology development (OBI)
Principles for ontology development (OBO foundry)
 Complete lack of focus and inability to say no

3. Acronym similarity and beyond: OBI and OBO
The Open Biomedical Ontologies (OBO) project has two groups of member ontologies
OBO foundry
OBO library
The Ontology of Biomedical Investigations (OBI) is a member ontology of the OBO foundry project
 Start with the OBO foundry

4. OBO foundry history: Gene Ontology
Model organism databases wanted to use the same vocabulary to annotate gene products  Gene Ontology Consortium
16,000 citations (!)

5. OBO foundry history: Gene Ontology
Killer app 1 (design goal): Predicting function of a newly discovered gene
Compare sequence of gene with genes of known function
GO ensures same vocabulary is used across model organisms
Hierarchy of terms enables dealing with different level of details: Hit 1: GO: = ‘calcium channel activity’ Hit 2: GO: = ‘voltage-gated calcium channel activity’  Both hits consistent (GO: is_a GO: )
Killer app 2 (bonus): Gene set enrichment analysis for microarrays
Measure gene expression in presence or absence of a chemical
Determine which genes are expressed differently ( typically >100)
Determine if there is an enrichment of GO annotations in the differentially expressed genes (e.g. DNA repair  chemical causes damage)

6. OBO foundry history: Beyond GO
The practical success of GO made ontologies a thing in biomedical science
Knowledge representation community became engaged  more formal ontology development beyond controlled vocabulary + acyclic graph
Sibling ontologies emerged that wanted to ‘plug in’ to GO + expand beyond genomics
Transcriptomics
Proteomics
Metabolomics
Proliferation of incompatible ontologies with overlapping scope
Rather than mimicking the attributes that made GO successful, this resembled the status quo ante of incompatible model organism DBs

7. Principles include (among others):
Mission: Develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate
Approach: Participants voluntarily adhere (and contribute) to ontology development principles that facilitate the foundry mission
Principles include (among others):
licensing that facilitates re-use
common machine readable syntax
standardized naming conventions
non-overlapping content and re-use
Ontologies that want to participate in the Foundry are peer-reviewed for principle compliance

8. Eight OBO Foundry ontologies Over hundred of ontologies in OBO Library
OBO member ontologies
Eight OBO Foundry ontologies
+ Disease Ontology
= 9 (2016)
Over hundred of ontologies in OBO Library

9. Gene Ontology http://www.obofoundry.org/
Mailing list, contact developers
For issues and new term request

10. OBO foundry principles
Since 2014: Review of Review process
 Ongoing process to clarify the wording of the principles and criteria to be used to evaluate ontologies for compliance
Notify us of problems via issue tracker
Common sense principles
open – licensing that enables re-use
format – OWL / OBO
uris - identifiers
versioning – deal with it
documented – tell others how to use it
locus of authority – contact person
users – have more than 1
Metadata principles
textual definitions
naming conventions
Foundry specific principles
delineated content
relations
collaboration
maintenance

11. Original Focus of OBO foundry: ‘Biological Reality’
Gene ontology (GO, the start of it all)
Molecular function
Biological process
Cellular component
Relationship Ontology (RO, evolved with gene ontology)
Ontologies describing complementary entities to GO (Anatomy (XFO, ZFA), Chemical entities (ChEBI), Proteins (PRO), Phenotypes (PATO))
Focus on representing assertions like: “chemical X inhibits protein Y”
Problem: Lack of context/evidence/provenance/metadata to go along with these assertions
We would like to know: Who said that? Based on what? Crystal structure of chemical binding to active site? Protein expression level in animal administered with chemical?
 OBI goal: represent the experiment / investigation

12. OBI Workshop San Diego Jan.
OBI Origins
MO/ MAGE
FuGO
FuGE
2nd FuGO
Workshop
Hinxton
July
OBI
MAGE
Jamboree
Hinxton
Dec
1st FuGO
Workshop
Philadelphia
Feb.
Cancer Genomics
Polypmorphisms
Genome Sequences
Crop Sciences
OBI Workshop San Diego Jan.
MGED 8
Bergen
Sept.
SOFG
Philadelphia
Oct
MAGE
Jamboree
Stanford
March
Transcriptomics (MGED)
Proteomics (PSI)
PSI
Siena
April
Toxicogenomics
Environmental Genomics
Nutrigenomics
(MGED RSBI)
Metabolomics
Flow Cytometry
Cellular Assays Immport IEDB Neuroinformatics

13. OBI – a user driven project
Established 2007 (along with OBO foundry)
First stable release in 2009
Current members typically have one or more applications that drive OBI development. Funding comes from these projects
9 year running effort, 1-2 phone calls per week, 1-2 face-to-face meetings per year
 Open project with constant addition of new communities, please consider joining!

14. >30 projects involved with OBI
Adverse Event Reporting Ontology (AERO)
eNanoMapper
NCBO Resource Index
An Ontology for Drug Discovery Investigations (DDI)
EuPathDB
Neuroscience Information Framework (NIF)
Antibody Registry
Evidence Ontology (ECO)
NIAID GSC/BRC project
Beta Cell Genomics Ontology (BCGO)
Experimental Factor Ontology (EFO)
Ontology for Biological and Clinical Statistics (OBCS)
BioSharing
Functional Genomics Data (FGED) Society
Ontology of Core Data Mining Entities (OntoDM)
Brucellosis Ontology (IDOBRU)
Immune Epitope Database and Analysis Resource (IEDB)
Statistics Ontology (STATO)
Cardiac Electrophysiology (CEP) Ontology
Influenza Ontology
The Encyclopedia of DNA Elements (ENCODE)
Cell Line Ontology
Informed Consent Ontology (ICO)
The Investigation / Study / Assay (ISA) tab-delimited (TAB)
Chemical Methods Ontology (CHMO)
miRNA and Aging Ontology (MIAGO)
Vaccination Informed Consent Ontology (VICO)
Cognitive Paradigm Ontology (CogPO)
NanoParticle Ontology
Vaccine Ontology
eagle-i
National Center for Biomedical Ontology (NCBO) Annotator

15. Partial high level class overview of OBI
BFO
IAO
OBO
OBI
is a
entity
continuant
occurrent
information
content entity
planned
process
data item
investigation
specimen collection
material component
separation
plan specification
material combination
assay
document
material processing
material entity
biological
process (GO)
material maintenance
processed material
specimen
gross anatomical part (CARO)
organism (NCBI taxonomy)
molecular entity (ChEBI)
organization
device
processed specimen
study design
specifically
dependent
independent
generically
function
role
human subject enrollment
Upper level ontology (ignore)
OBI imports foundry ontologies

16. Example metadata for OBI term
OWL annotation property 
Example
Preferred Term*
glucometer
Definition*
A measurement device with the function to measure and record the level/amount of glucose in a blood sample
Term Editor*
PERSON:Frank Gibson PERSON:Helen Parkinson
Definition Source*
Curation Status*
Ready for release
Example of usage
Diabetic patients use glucometers to determine their glucose levels
Alternative Term
glucose meter
* required

17. Example logical definition
Asserted single inheritance hierarchy. Inferred multiple inheritance

18. Modeling an experiment in OBI
Measure Function
is realized by
Process: Collecting specimen from organism
inheres in
Process: Analyte assay
Glucometer
Measurement Datum
1.2 mg/ml
has specified input
has specified output
has specified input
has specified input
has specified input
has specified output
inheres in
located in
is realized by
Specimen Role
is realized by
is realized by
Analyte Role
Glucose molecules
Blood Specimen
Evaluant Role
inheres in
part of
inheres in

19. Applications of OBI
Adding semantic expressivity to data stored in the IEDB
Designing smart, standardized submission forms for EuPathDB
Harmonize the annotation across different functional genomics resources using ISA-TAB

23. What is the OBI killer app?
For us (=data resource providers):
Standardize metadata capture at data generation
Formulate curation rules + consistency checks
Drive query interfaces
Inference of missing data
For the broader community
Aggregation of experimental evidence, summarize and resolve differences
Query across resources

24. Pain Points: Using community standards in a production system
They don’t change
(fast enough to keep up with knowledge)
They change
(and make existing data invalid)
Scope limitations
We are now formalizing our workflow to address this

25. Internal source of truth: Nomenclature standards + homemade extensions
Community standards
Internal source of truth: Nomenclature standards + homemade extensions
In House Human subject enrollment system (HLA typing)
Immune Epitope database (IEDB)
curation
external DB
MHC binding prediction tools
In House MHC Binding Assay Database

26. What should we be doing now? How should we be doing it?
Ontology developers:
Adhere to and contribute to OBO foundry principles
Consider submitting ontology for foundry membership
Do not duplicate efforts and limit benefits; rather contribute to existing efforts to make them better
Data generators
Use OBO foundry ontologies for data capture
Contribute to ontologies rather than complaining that they are incomplete!!!
Include personnel costs in your grants for this
Resist the temptation to build your own ontology island
Where are things going in the future?
Depends. Data scientists could be drivers of knowledge discovery, or forever deal with mapping identifiers

27. Acknowledgements OBO Foundry: http://www.obofoundry.org/
Colin Batchelor (E), Royal Society of Chemistry, UK
Mathias Brochhausen (O)
Melanie Courtot (O, T), Simon Fraser University, Vancouver, Canada
Melissa Haendel (O), Oregon Health & Science University, Portland, OR (OBO coordinator)
Janna Hastings (E, O)
Suzi Lewis (OBO coordinator)
Chris Mungall, Lawrence Berkeley Laboratory, Berkeley, USA (OBO coordinator) (T)
Darren Natale (E)
James A. Overton (T), Knocean.com, Toronto, Canada
Bjoern Peters (E)
Philippe Rocca-Serra (E, O)
Alan Ruttenberg (O, T), University at Buffalo, Buffalo, USA (OBO coordinator)
Richard Scheuermann (OBO coordinator) (O), J. Craig Venter Institute, La Jolla, CA, USA
Lynn Schriml (E,O) (OBO coordinator)
Barry Smith (OBO coordinator) (O)
Chris Stoeckert (O), University of Pennsylvania, Philadelphia, PA, USA
Ramona Walls (E, O), The iPlant Collaborative, University of Arizona, Tucson, AZ, USA
Jie Zheng (T), University of Pennsylvania, Philadelphia, USA
OBI consortium:
OBI: Anita Bandrowski1, Ryan Brinkman2, Mathias Brochhausen3, Matthew H. Brush4, Bill Bug†, Marcus C. Chibucos5, Kevin Clancy6, Mélanie Courtot7, Dirk Derom8, Michel Dumontier9, Liju Fan10, Jennifer Fostel11, Gilberto Fragoso12, Frank Gibson13, Alejandra Gonzalez-Beltran14, Melissa A. Haendel4, Yongqun He15, Mervi Heiskanen16, Tina Hernandez-Boussard9, Mark Jensen17, Yu Lin15, Allyson L. Lister14, Phillip Lord18, James Malone19, Elisabetta Manduchi20, Monnie McGee21, Norman Morrison22, James A. Overton23, Helen Parkinson19, Bjoern Peters23*, Philippe Rocca-Serra14, Alan Ruttenberg17, Susanna-Assunta Sansone14, Richard H. Scheuermann24, Daniel Schober25, Barry Smith17, Larisa N. Soldatova26, Christian J. Stoeckert Jr.20, Chris F Taylor19, Carlo Torniai4, Jessica A. Turner27, Randi Vita23, Patricia L. Whetzel1, Jie Zheng20
1University of California, San Diego, La Jolla, California, USA
2British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
3University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
4Oregon Health & Science University, Portland, Oregon, USA
†Deceased
5University of Maryland School of Medicine, Baltimore, Maryland, USA
6Thermo Fisher Scientific, Carlsbad, California, USA 
7Simon Fraser University, Burnaby, British Columbia, Canada
8The Vrije Universiteit Brussel, Ixelles, Brussels, Belgium
9Stanford University, Stanford, California, USA
10Ontology Workshop, LLC, Columbia, Maryland, USA
11National Toxicology Program, NIEHS, National Institutes of Health, Research Triangle Park, North Carolina, USA
12Center for Biomedical Informatics and Information Technology, National Institutes of Health, Rockville, Maryland, USA
13Royal Society of Chemistry, Cambridge, Cambridgeshire, United Kingdom
14University of Oxford, Oxford, Oxfordshire, United Kingdom
15University of Michigan Medical School, Ann Arbor, Michigan, USA
16National Cancer Institute, Rockville, Maryland, USA
17University at Buffalo, Buffalo, New York, USA
18Newcastle University, Newcastle-upon-Tyne, Tyne and Wear, United Kingdom
19European Molecular Biology Laboratory- European Bioinformatics Institute, Hinxton, Cambridgeshire, United Kingdom
20University of Pennsylvania, Philadelphia, Pennsylvania, USA
21Southern Methodist University, Dallas, Texas, USA
22The University of Manchester, Manchester, Greater Manchester, United Kingdom
23La Jolla Institute for Allergy and Immunology, La Jolla, California, USA
24J. Craig Venter Institute, La Jolla, California, USA
25Leibniz Institute of Plant Biochemistry, Halle, Saxony-Anhalt, Germany
26Brunel University London, Uxbridge, Middlesex, United Kingdom
 27Georgia State University, Atlanta, Georgia, USA
 Need to add industry disclosure