Abstract
AbstractOur work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancel cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Lobic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.
Highlights
The Yale Specialized Program in Research Excellence (SPORE) in skin cancer is a large translational cancer project, which aims at rapidly moving biological insights from the “bench to bedside”
We examined data derived from seven melanoma cell lines (WW165, YUMAC, YUGEN8, YUSAC2, YUSIT1, YULAC and YURIF)
We found an effective compromise to be the use of the Simple Knowledge Organization System (SKOS) [21]
Summary
The Yale Specialized Program in Research Excellence (SPORE) in skin cancer is a large translational cancer project, which aims at rapidly moving biological insights from the “bench to bedside”. As part of the effort, the SPORE collects skin cancer samples from mostly malignant melanoma patients and performs a multitude of Omics studies, probing the melanoma genome, epigenome, transcriptome and proteome. The idea is to integrate this data with clinical outcome information to derive prognostic and predictive biomarkers, i.e. genomic markers that predict patient survival and drug therapy effectiveness, respectively. These markers are either derived statistically in an unbiased fashion [33], or by prior knowledge and candidate (gene) selection [17]. We use SPARQL to query these graphs to better understand the molecular basis of drug resistance and sensitivity
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