TopFed: TCGA tailored federated query processing and linking to LOD.
BACKGROUND
The 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.


METHODS
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. 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.


RESULTS
We 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.


CONCLUSIONS
With 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.
Universität Leipzig, IFI/AKSW, PO 100920, D-04009 Leipzig, Germany.
Muhammad
M
Saleem
Insight Centre for Data Analytics, National University of Ireland (NUIG), Galway, Ireland.
Shanmukha S
SS
Padmanabhuni
Universität Leipzig, IFI/AKSW, PO 100920, D-04009 Leipzig, Germany.
Axel-Cyrille Ngonga
AC
Ngomo
Insight Centre for Data Analytics, National University of Ireland (NUIG), Galway, Ireland.
Aftab
A
Iqbal
Division Informatics, Department of Pathology, University of Alabama, Birmingham, USA.
Jonas S
JS
Almeida
Insight Centre for Data Analytics, National University of Ireland (NUIG), Galway, Ireland.
Stefan
S
Decker
Foundation Medicine, Inc, Cambridge, MA 02141 USA.
Helena F
HF
Deus
Federated queries; RDF; SPARQL; TCGA