Paper Info
Title
TopFed: TCGA Tailored Federated Query Processing and Linking to LOD.
Abstract
Backgroud: 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. Conclusion: 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.
Year
DOI
Venue
2014
10.1186/2041-1480-5-47
J. Biomedical Semantics
Keywords
Field
DocType
Federated queries,SPARQL,TCGA,RDF
Genome,Data integration,Data science,Data mining,Multidisciplinary approach,Information retrieval,Computer science,Upload,SPARQL,Parsing,RDF
Journal
Volume
Issue
ISSN
5
47
2041-1480
Citations 
PageRank 
References 
10
0.53
18
Authors
7
Name
Order
Citations
PageRank
Muhammad Saleem119421.78
Shanmukha S. Padmanabhuni2241.34
Axel-Cyrille Ngonga Ngomo31775139.40
Aftab Iqbal4616.11
Jonas S Almeida573142.25
Stefan Decker65799643.68
Helena F. Deus721013.23