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A semantic web framework to integrate cancer omics data with biological knowledge

BACKGROUND: 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...

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Autores principales: Holford, Matthew E, McCusker, Jamie P, Cheung, Kei-Hoi, Krauthammer, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471346/
https://www.ncbi.nlm.nih.gov/pubmed/22373303
http://dx.doi.org/10.1186/1471-2105-13-S1-S10
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author Holford, Matthew E
McCusker, Jamie P
Cheung, Kei-Hoi
Krauthammer, Michael
author_facet Holford, Matthew E
McCusker, Jamie P
Cheung, Kei-Hoi
Krauthammer, Michael
author_sort Holford, Matthew E
collection PubMed
description BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
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spelling pubmed-34713462012-10-18 A semantic web framework to integrate cancer omics data with biological knowledge Holford, Matthew E McCusker, Jamie P Cheung, Kei-Hoi Krauthammer, Michael BMC Bioinformatics Research BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. BioMed Central 2012-01-25 /pmc/articles/PMC3471346/ /pubmed/22373303 http://dx.doi.org/10.1186/1471-2105-13-S1-S10 Text en Copyright © 2012 Holford et al. https://creativecommons.org/licenses/by/2.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Holford, Matthew E
McCusker, Jamie P
Cheung, Kei-Hoi
Krauthammer, Michael
A semantic web framework to integrate cancer omics data with biological knowledge
title A semantic web framework to integrate cancer omics data with biological knowledge
title_full A semantic web framework to integrate cancer omics data with biological knowledge
title_fullStr A semantic web framework to integrate cancer omics data with biological knowledge
title_full_unstemmed A semantic web framework to integrate cancer omics data with biological knowledge
title_short A semantic web framework to integrate cancer omics data with biological knowledge
title_sort semantic web framework to integrate cancer omics data with biological knowledge
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3471346/
https://www.ncbi.nlm.nih.gov/pubmed/22373303
http://dx.doi.org/10.1186/1471-2105-13-S1-S10
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