This article is part of the supplement: Semantic Web Applications and Tools for Life Sciences (SWAT4LS) 2010
A semantic web framework to integrate cancer omics data with biological knowledge
1 Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, 300 George Street, Suite 501, New Haven, CT, 06511, USA
2 Department of Pathology, Yale University, 310 Cedar Street LH 108, PO Box 208023, New Haven, CT, 06520, USA
3 Department of Computer Science, Yale University, PO Box 208285, New Haven, CT, 06520-8285, USA
4 Center for Medical Informatics, Yale University, 300 George Street, Suite 501, New Haven, CT, 06511, USA
5 Department of Genetics, Yale University, 333 Cedar Street, New Haven, CT, 06520, USA
Citation and License
BMC Bioinformatics 2012, 13(Suppl 1):S10 doi:10.1186/1471-2105-13-S1-S10Published: 25 January 2012
The RDF triple provides a simple linguistic means of describing limitless types of information. Triples can be flexibly combined into a unified data source we call a semantic model. Semantic models open new possibilities for the integration of variegated biological data. We use Semantic Web technology to explicate high throughput clinical data in the context of fundamental biological knowledge. We have extended Corvus, a data warehouse which provides a uniform interface to various forms of Omics data, by providing a SPARQL endpoint. With the querying and reasoning tools made possible by the Semantic Web, we were able to explore quantitative semantic models retrieved from Corvus in the light of systematic biological knowledge.
For this paper, we merged semantic models containing genomic, transcriptomic and epigenomic data from melanoma samples with two semantic models of functional data - one containing Gene Ontology (GO) data, the other, regulatory networks constructed from transcription factor binding information. These two semantic models were created in an ad hoc manner but support a common interface for integration with the quantitative semantic models. Such combined semantic models allow us to pose significant translational medicine questions. Here, we study the interplay between a cell's molecular state and its response to anti-cancer therapy by exploring the resistance of cancer cells to Decitabine, a demethylating agent.
We were able to generate a testable hypothesis to explain how Decitabine fights cancer - namely, that it targets apoptosis-related gene promoters predominantly in Decitabine-sensitive cell lines, thus conveying its cytotoxic effect by activating the apoptosis pathway. Our research provides a framework whereby similar hypotheses can be developed easily.