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Cited 2 time in webofscience Cited 1 time in scopus
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cMapper: gene-centric connectivity mapper for EBI-RDF platform

Authors
Shoaib, MuhammadAnsari, Adnan AhmadAhn, Sung-Min
Issue Date
15-Jan-2017
Publisher
OXFORD UNIV PRESS
Citation
BIOINFORMATICS, v.33, no.2, pp.266 - 271
Journal Title
BIOINFORMATICS
Volume
33
Number
2
Start Page
266
End Page
271
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/6495
DOI
10.1093/bioinformatics/btw612
ISSN
1367-4803
Abstract
Motivation: In this era of biological big data, data integration has become a common task and a challenge for biologists. The Resource Description Framework (RDF) was developed to enable interoperability of heterogeneous datasets. The EBI-RDF platform enables an efficient data integration of six independent biological databases using RDF technologies and shared ontologies. However, to take advantage of this platform, biologists need to be familiar with RDF technologies and SPARQL query language. To overcome this practical limitation of the EBI-RDF platform, we developed cMapper, a web-based tool that enables biologists to search the EBI-RDF databases in a gene-centric manner without a thorough knowledge of RDF and SPARQL. Results: cMapper allows biologists to search data entities in the EBI-RDF platform that are connected to genes or small molecules of interest in multiple biological contexts. The input to cMapper consists of a set of genes or small molecules, and the output are data entities in six independent EBI-RDF databases connected with the given genes or small molecules in the user's query. cMapper provides output to users in the form of a graph in which nodes represent data entities and the edges represent connections between data entities and inputted set of genes or small molecules. Furthermore, users can apply filters based on database, taxonomy, organ and pathways in order to focus on a core connectivity graph of their interest. Data entities from multiple databases are differentiated based on background colors. cMapper also enables users to investigate shared connections between genes or small molecules of interest. Users can view the output graph on a web browser or download it in either GraphML or JSON formats.
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