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Editor's Notes

1. The OGO system ... Presentation URL Creative commons attribution non commercial share alike
2. Orthologs are homolog sequences (they share a common ancestor) that diverged by an speciation event
3. Orthologs can be used to generate hypotheses. For example, if frog alpha and chicken alpha are ortholog genes, and it is known that frog alpha is involved in a certain trait (e.g. a disease), then it is likely that chicken alpha is also involved in or related to such trait, in chicken Therefore, the information about orthologs is very important in biomedical research, since they show new research paths for human diseases with a genetic cause
4. Unfortunately, information about orthologs and diseases is scattered and it is difficult to combine
5. The OGO system provides a resource for accesing the ortholog/diseases combined information in a precise way. The OGO system is an OWL KB, in which the OGO ontology provides the schema and the information regarding orthologs and diseases is stored in instances, with relationships between them The OGO ontology is also used as a guide for the user to build queries The system is accessed with keywords or SPARQL The pipeline is executed periodically (Mappings, information checking)
6. OGO ontology (KB schema and querying)
7. Imported ontologies (GO, ECO, RO) reuse existing semantics for querying, as we will see when I describe the queries OBOF: Wealth of quality reusable semantics of the biodomain GO: Member ECO, RO: Candidates
8. Not detailed Classes as values (OBO format) Future DL
9. Pipeline
10. JENA allows to store OWL in a MySQL database, and to access it with SPARQL
11. The OGO system has two interfaces: Keyword based interface (by disease/by orthologs): not very expressive but fast Semantic interface (next)
12. The semantic interface is more expressive than the keyword based interface. However, as SPARQL is difficult to use by biologists, the semantic interface provides a graphical interface for creating queries, that, later, are translated into SPARQL It should be noted that this does not allow to use the whole expressivity of SPARQL, but a considerable part of it (see grammar)
13. In order to define the query, we can select concepts from the OGO ontology, and add any requirements, also using the OGO ontology We can exploit the imported ontologies for querying: GO, ECO, NCBI The defined query is translated into SPARQL and executed against the KB
14. Whole process First we select the variables that we are interested in from the OGO ontology. In this case, Gene and Genetic disease (i.e, we want to retrieve Genes and Genetic diseases) The imported ontologies can be exploited (GO, ECO, NCBI) for querying
15. Then we add requirements, also using the OGO ontology (And Imported ontologies ). We can use the selected variables or new ones. We can delete/edit requirements
16. We edit a requirement by using the OGO ontology (to add new variables and values) or by using the already defined variables NCBI (imported, like GO, and ECO) for providing values for the requirement
17. We add the finished requirement to the the query
18. We can add as many requirements as we want
19. Finally, the query is translated into SPARQL and executed against the KB
20. Results
21. The expressivity of the query is limited by the grammar
22. YOGY already does this, however, redundant results by resource, instead of gene centric, i.e.same gene in different resources OGO ontology is used to check the consistency of the info Less expressivity in SPARQL: no OPTIONAL