An Ontology-driven Semantic Mash-up of Gene and Biological Pathway Information: Application to the Domain of Nicotine Dependence

TitleAn Ontology-driven Semantic Mash-up of Gene and Biological Pathway Information: Application to the Domain of Nicotine Dependence
Publication TypeJournal Article
Year of Publication2008
AuthorsKaren Skinner, Joni Rutter, Amit Sheth, Olivier Bodenreider, Satya S. Sahoo
JournalJournal of Biomedical Informatics
Pagination752-765
KeywordsEntrez Knowledge Model (EKoM), Gene-Pathway data integration, information integration, Multi-ontology schema integration, Nicotine dependence, Semantic Bioinformatics, Semantic mashup
Abstract

Objectives: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results: Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion: Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces.

Full Text

Satya S. Sahoo, Olivier Bodenreider, Joni L. Rutter, Karen J. Skinner, and Amit P. Sheth, 'An Ontology-Driven Semantic Mash-up of Gene and Biological Pathway Information: Application to the Domain of Nicotine Dependencem,'special issue: Semantic Biomedical Mashups Journal of Biomedical Informatics, 41 (5), October 2008, pp.752-765.PMID: 18395495
pages: pp752-765
publisher: Journal of Biomedical Informatics
year: 2008
related resource url: http://www.ncbi.nlm.nih.gov/pubmed/18395495
hasURL: http://knoesis.wright.edu/library/download/EKoM_BioPAX_JBI.pdf

Related Files: