Immunology Ontology
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 (Barry Smith Slides)
- 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
- Examples
- The OBO (Open Biological and Biomedical Ontologies) Foundry
- The Ontology for Biomedical Investigations (OBI)
9:15am Overview of ontologies with content relevant to immunology (Lindsay Cowell, Slides)
- Use of ontology portals and search engines
- The Gene Ontology (GO) Poster
- Relation to MeSH and similar resources
- How are ontologies used?
- 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
- 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). Participants will suggest possible target areas, expanding this list in light of their own areas of interest and expertise.
- Adaptive immunity
- Immune tolerance
- Inflammation
- Adaptive immunity
- Innate immunity
- Mucosal immunity
- Transplantation immunology /MHC
- Tumor microenvironment / Cancer
- Virology
(These are all areas for which a suitable single ontology does not already exist. Some of the items are taken from the DAIT list here.)
1:10 Participants will be divided into corresponding breakout groups. The task of each group will be to create a workplan for building 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 any other source
Two samples:
- allergy and allergic disease
- soluble proteins (including cytokines and chemokines) relevant to the work of HIPC]
- 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
Each group should select a reporter, who will present the results of the group's work, and a recorder, who will take notes of the discussions and have control of the powerpoint slides documenting the results of these discussions.
2:20 Presentation of results: Each group is required to produce at least 4 slides summarizing the results of their work.
- 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: List of the principal ontologies selected to be used as sources / starting points for development of the needed ontology
- Slide 4: List of the main terms in the ontology (if possible with some rudimentary BFO organization)
2.50 Summation
Background Resources (will be reviewed in class)
Immunological Ontologies
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.
