Abstract
BackgroudThe Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to catalogue genetic mutations responsible for cancer using genome analysis techniques. One of the aims of this project is to create a comprehensive and open repository of cancer related molecular analysis, to be exploited by bioinformaticians towards advancing cancer knowledge. However, devising bioinformatics applications to analyse such large dataset is still challenging, as it often requires downloading large archives and parsing the relevant text files. Therefore, it is making it difficult to enable virtual data integration in order to collect the critical co-variates necessary for analysis.MethodsWe address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed.ResultsWe compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX.ConclusionWith TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.
Highlights
The Cancer Genome Atlas [1] (TCGA) is an effort led by the National Cancer Institute to characterize and sequence more than 30 cancer types from 9000 patients at the molecular level
We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints
We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing
Summary
The Cancer Genome Atlas [1] (TCGA) is an effort led by the National Cancer Institute to characterize and sequence more than 30 cancer types from 9000 patients at the molecular level. Big data research initiatives such as the International Cancer Genomics Consortia [3], the 1000genomes [4] and the One Million Genomes project [5], the $10 Million Genome Prize [6], and the remarkable drop in the cost of genome sequencing [7] will soon mean that the current bioinformatics paradigm in which researchers download all the data, extract the interesting pieces and remove the rest, will no longer be feasible [8,9]. A scalable and robust solution is a critical requirement, whereby researchers can obtain a subset of big data they are interested in by executing a query using a particular service
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