Representing the Immune Epitope Database in OWL

Jason A. Greenbaum1, Randi Vita1, Laura Zarebski1, Hussein Emami2, Alessandro Sette1, Alan Ruttenberg3, and Bjoern

Peters1

1La Jolla Institute for Allergy and Immunology2Science Applications International Corporation

3Science Commons

Overview• Background

– Immune epitopes – Epitope mapping experiments– The Immune Epitope Database (IEDB)

• IEDB development cycle– Ontology development– Database design– Content curation

• Database export into OWL

Mouse

Virus

cell

CD8+ T cell epitopes in viral infection

MHC-I

cell

MHC-I

TCytokine

Release

Cytotoxicity

TT

Proliferation

Mouse

TCR CD8

CD8+ T cell epitopes in viral infection

Virus

epitope role: the role of a material entity that is realized when it binds to an adaptive immune receptor.

Context is key – What immune receptor? What host? What happened to the host previously (infections? vaccinations? diseases?)…

adaptive immune response: a GO:immune response resulting from epitope binding by adaptive immune receptor

Entities in a epitope mapping experiment

T

APC

T

• Data items

– spot count

– spot forming cells per million

• Processes

– Administering substance in vivo

– Take sample from organism

– Perform ELISPOT assay

– Transform data

• Material entities

– Cell

– Organism

– Peptide

• Roles and Functions

– Immunogen

– Antigen

– Antigen presenting cell

– Effector cell42 SFC/10^6

Goal: To catalog and make accessible immune epitope characterizing experiments

IEDBwww.immuneepitope.org

Literature curationEpitope discovery contract submission

The Immune Epitope Database (IEDB)

10 full time curators

Content >6,500 journal articles >50,000 epitopes >300,000 experiments

Completed:• 98% infectious disease• 95% allergy

Next: autoimmunity (25%)

Example curated experiment: typically 100 – 300 fields

Example curated experiment: typically 100 – 300 fields

Example curated experiment: typically 100 – 300 fields

Summary I

• Immune epitopes are the molecular entities recognized by adaptive immune receptors

• The IEDB catalogs experiments defining immune epitopes

Large amounts of complex data, which poses challenges for data consistency

Overview• Background

– Immune epitopes – The Immune Epitope Database (IEDB)

• IEDB development cycle– Ontology development– Database design– Content curation

• Database export into OWL

Development cycleOntology development

• identify entities and relations

Database design• table structure• lookup table values• validation rules

Content curation• add new content• recurate invalid content

Ontology development (ONTIE)

• Re-use terms from OBO foundry candidate ontologies• Native ONTIE terms for entities specific for epitopes Goal is to find a good home for them

Imports from: Gene OntologyCell Ontology

ChEBI,NCBI Taxonomy

OBIProtein Ontology

Information Artifact Ontology

Partial high-level ‘is a’ hierarchy

Available: http://ontology.iedb.org/

Database design / implementation

Ontology terms | Database tablesHistory:• initial design (to get started) • iterative updates (to fix things)• redesign from scratch for 2.0 because we (still) can

Tables aligned with ontology Improved understanding between software engineers and domain experts ‘ontologic normalization’

Content migration and re-curation

IEDB 1.0

Rule based validation first pass: 693,133

inconsistencies

1. conditional field-to-field mapping2. script based re-curation (SQL)

IEDB 2.0

3. manual recuration (web interface)

Summary II

• Application specific ontology (ONTIE) developed based on OBO foundry principles, and relying heavily on OBI

• Database re-designed and structure aligned with the ontology

• Data migrated and consistency enforced by rule based validation engine

Overview• Background

– Immune epitopes – The Immune Epitope Database (IEDB)

• IEDB development cycle– Ontology development– Database design– Content curation

• Database export into OWL

Subset of IEDB 2.0

Database export into OWL

Advantages of OWL export

• Allows to directly use ontology and OWL reasoner to perform consistency checks

• Provides expressive query language within the IEDB

• Enables query across integrated biomedical databases.

Future Work

• Provide IEDB in triple store / access through SPARQL queries

• Complete ontology development and OWL export for all data in the IEDB

• Overcome technical challenges (Pellet takes 1 minute to classify 100 assays; 300,000 in IEDB…)

• Overcome ontological challenges (cells, peptides, negative data, …)

IEDB Team - www.iedb.org

SAIC• Scott Stewart• Tom Carolan• Hussein Emami

San Diego Supercomputer Center

• Phil Bourne• Julia Ponomarenko• Zhanyang Zhu

Technical University of Denmark

• Ole Lund• Morten Nielsen

University of Copenhagen• Søren Buus

La Jolla Institute for Allergy & Immunology

THANKS! OBI Consortium - http://obi-ontology.orgAlan Ruttenberg – Science Commons