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

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

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

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

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:0005262 = ‘calcium channel activity’ Hit 2: GO:0005245 = ‘voltage-gated calcium channel activity’  Both hits consistent (GO:0005245 is_a GO:0005262) 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)

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

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 http://www.obofoundry.org/

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 http://www.obofoundry.org/

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

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 https://github.com/OBOFoundry/OBOFoundry.github.io/issues 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 http://www.obofoundry.org/

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

OBI Workshop San Diego Jan. OBI Origins MO/ MAGE FuGO FuGE 2nd FuGO Workshop Hinxton July OBI 2004 2005 2006 2007 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

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!

>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

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

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* http://en.wikipedia.org/wiki/Glucose_meter Curation Status* Ready for release Example of usage Diabetic patients use glucometers to determine their glucose levels Alternative Term glucose meter * required

Example logical definition Asserted single inheritance hierarchy. Inferred multiple inheritance

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

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

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

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

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

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

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: http://obi-ontology.org/ 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