Transcript 2009-06-NCBO-Peters - National Center for Biomedical Ontology

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 Immunology
2Science 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
CD8+ T cell epitopes in viral infection
Mouse
Virus
MHC-I
cell
CD8+ T cell epitopes in viral infection
adaptive immune response: a
GO:immune response resulting
from epitope binding by
adaptive immune receptor
Mouse
Proliferation
T
T
T
Virus
TCR
MHC-I
Cytokine
Release
CD8
Cytotoxicity
cell
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?)…
Entities in a epitope mapping experiment
• Processes
– Administering substance in vivo
– Take sample from organism
T
T
– Perform ELISPOT assay
– Transform data
• Material entities
APC
• Data items
– Cell
– spot count
– Organism
– spot forming cells
per million
– Peptide
• Roles and Functions
– Immunogen
– Antigen
42 SFC/10^6
– Antigen presenting cell
– Effector cell
The Immune Epitope Database (IEDB)
Goal: To catalog and make accessible
immune epitope characterizing experiments
Epitope discovery
Literature curation contract submission
IEDB
www.immuneepitope.org
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 cycle
Ontology development
• identify entities and
relations
Content curation
• add new content
• recurate invalid content
Database design
• table structure
• lookup table values
• validation rules
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 Ontology
Cell Ontology
ChEBI,
NCBI Taxonomy
OBI
Protein Ontology
Information Artifact Ontology
Partial high-level ‘is a’ hierarchy
Available: http://ontology.iedb.org/
Database design / implementation
History:
• initial design (to get started)
• iterative updates (to fix things)
• redesign from scratch for 2.0
because we (still) can
Ontology terms | Database tables
Tables aligned with ontology
Improved understanding between
software engineers and domain
experts
 ‘ontologic normalization’
Content migration and re-curation
IEDB 1.0
1. conditional field-to-field mapping
2. script based re-curation (SQL)
Rule based validation
first pass: 693,133
inconsistencies
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
Database export into OWL
Subset of
IEDB 2.0
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, …)
THANKS!
OBI Consortium - http://obi-ontology.org
Alan Ruttenberg – Science Commons
IEDB Team - www.iedb.org
La Jolla Institute for Allergy & Immunology
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