1. Randi Vita, M.D.
Better living through ontologies at the Immune Epitope Database
La Jolla Institute for Allergy & Immunology
Division of Vaccine Discovery
La Jolla, California

2. The Immune Epitope Database
Free online resource of experimentally-derived epitope information
IEDB has >99% of all published epitope data
Allergy, Infectious diseases, Autoimmune diseases, Transplant/alloantigens
New data is added every week
18,300 references
>1,000,000 experimental assays
275,000 peptidic epitopes
2,400 nonpeptidic epitopes

3. What is an epitope? APC T cell
The portion of a pathogen, allergen, or autoantigen that the immune system recognizes is the epitope
Antibodies and T cells bind to epitopes to trigger an immune response
Antibodies typically bind to discontinuous residues of proteins
T cells recognize epitopes (typically peptides) presented by MHC molecules
APC
T cell

4. Why do epitopes matter? Vaccine development Allergy immunotherapy
Immunogenicity
Transplantation

5. Collaborations with existing resources and ontologies (OBO)
Ensures consistency and accuracy Provides standardized nomenclature Provides definitions, synonyms, and hierarchical relationships for database terms Makes curation easier Enhances user experience Facilitates interoperability

6. Embedded Finders driven by external resources and ontologies (OBO)
Structure
Source Molecule
Organism source
Epitope
Immunization Process(es)
Host
Immunogen
Disease
Assay
Assay Type
T cells
MHC restriction
MRO

7. UniProt Molecule Finder
Amino acid sequence
Protein source
Organism source
Peptidic
Epitope
Users can see all proteins expressed by an organism as well as processed fragments

8. NCBI Organism Finder Amino acid sequence Peptidic Epitope
Protein source
Organism source
Peptidic
Epitope

9. Organism example in IEDB
NCBI hierarchy

10. Organism example in IEDB
Immunologist friendly hierarchy

11. Disease Ontology Finder
Immunization Process(es)
Host
Immunogen
Disease

12. Model, Reason and Infer
We use logical reasoning to create validation rules that can identify errors within the data, enforce accurate curation, and infer data fields.
For example, Timothy grass allergy is logically defined as has_allergic_trigger (pollen produced_by Phleum pratense)
Timothy grass allergy should not be curated as being caused by a chicken egg. Using the logical definitions to drive validation, such existing data is flagged as an error, the curation interface will not allow new entries with this type of error, and the allergen can be inferred
Curation Fields
Logical Definition
invalid

13. Gained Knowledge
The curator only has to only specify ‘Occurrence of allergy’ and the allergen as benzylpenicillin CHEBI:18208
benzylpenicillin allergy is inferred as the disease
drug allergy, antibacterial drug, beta-lactam antibiotic, etc are gained knowledge

14. OBI Assay Finder
Assay
Assay Type
T cells
MHC restriction
Users can search on all T cell assays, all cytokine assays, or selectively on IL-2 assays.

15. Interoperability
As more resources represent information utilizing formal ontologies (especially OBO foundry)
Interoperability between data sources is facilitated
Queries across resources become possible. Example:
What are shared features of chemicals causing allergic responses

16. Thanks
James Overton
ChEBI, DO, OBI, Gaz – PRO, MGI, IPTM