Immunology Ontology

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What:

Summer School for Quantitative Systems Immunology: Lecture and practical session on Immunology Ontology

When: Tuesday June 11

Where: Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston University, Boston, MA on June 10-14, 2013.

Who: Lindsay Cowell and Barry Smith

Schedule

Morning Lecture

8:30am Introduction to biomedical ontology building

Identification of participant areas of interest in preparation for the afternoon practical session
Overview of biomedical ontology
What is an ontology for?
How ontologies can support data-driven research
Glories and miseries of the Semantic Web
How to build an ontology
How to select appropriate starting points
Mistakes to avoid
Reinventing the wheel
Confusing words with things
Example
The OBO (Open Biological and Biomedical Ontologies) Foundry
The Ontology for Biomedical Investigations (OBI)

9:15am Overview of ontologies with content relevant to immunology

Use of ontology portals and search engines
The Cell Ontology (CL) Bioportal
The Protein Ontology (PRO) Bioportal
The Gene Ontology (GO) Poster
Relation to MeSH and similar resources
How are ontologies used?
in defining data standards (example: ImmPort)
to support data analysis (example: GO enrichment of microarray data)
to support text mining and NLP, document retrieval
example: GOPubMed
The Infectious Disease Ontology (IDO)
Staph Aureus Ontology and Other IDO Extension Ontologies
The Vaccine Ontology (VO) Slides

9:45am Formats and tools

Brief remarks on formats: XML, RDF, OWL and OBO
GO File Format Guide
Brief remarks on tools
Protégé Ontology Editor
Ontofox Slides
SPARQL


Afternoon Practical Session: Building Small Ontologies in the Immunological Domain

1:00pm Establish target areas of interest (drawing on the work initiated in the 8:30am session above)

First draft list of possible target areas (they should be areas for which a suitable single ontology does not already exist)

innate immunity
allergy and allergic disease
autoimmune disease
asthma
immune tolerance
transplantation
medical countermeasures against radiological and nuclear threats
tissue engineering
wounds and injuries

(Some of the items on this list are taken from the DAIT list here.

1:10pm Participants will vote, in order to select ca. 10 topics, for each of which we have six or so people who have some relevant domain knowledge

1:20 Participants will be divided into corresponding groups. The task of each group will be to create a workplan for creating an ontology (or ontologies) for their selected area. The workplan will consist minimally of

i. a list of main terms in their selected area -- to create this list participants can use the terminological resources they use in their own work or on any other source
ii. a list of the main ontologies they would harvest as starting points; in identifying these ontologies participants should experiment with two or more of the ontology portals listed below

2:20 Presentation of results: Each group is required to produce at least 4 slides summarizing the results of their work in 3 minutes.

Slide 1: Title and author list
Slide 2: Quick comparison of the experience of using the 4 ontology

browsers listed above; optional report on experience using MeSH

Slide 3: The list of the principal ontologies selected to be used as

sources / starting points for development of the needed ontology

Slide 4: The list of the main terms in the ontology

2.50 Summation

Background Resources (will be reviewed in class)

Immunological Ontologies

Overview

Examples

[HIPC example http://ncor.buffalo.edu/2013/Immunology/HIPC-Example/]

[Allergy example http://ncor.buffalo.edu/2013/Immunology/allergy-example.docx]

Portals

1. The Open Biological and Biomedical Ontologies

2. Bioportal

3. Ontobee

4. EBI Ontology Lookup Service

5. MeSH


Literature

Diehl AD, Augustine AD, Blake JA, Cowell LG, et al. Hematopoietic cell types: prototype for a revised cell ontology. J Biomed Inform. 2011; 44(1).

Meehan TF, Masci AM, Abdulla A, Cowell LG, et al. Logical development of the cell ontology. BMC Bioinformatics. 2011; 12.

Aravind Subramanian, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, PNAS, 102 (43), 15545–15550.