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J Extracell Vesicles. 2015 Aug 28;4:27497. doi: 10.3402/jev.v4.27497. eCollection 2015.

Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies.

Author information

1
Bioinformatics Research Laboratory, Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA.
2
Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
3
Division of Molecular Psychiatry, Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, Yale University School of Medicine, New Haven, CT, USA.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
5
Pacific Northwest Diabetes Research Institute, Seattle, WA, USA.
6
Division of Neurosurgery, UC San Diego School of Medicine, UC San Diego Health System, La Jolla, CA, USA.
7
Department of Emergency Medicine, Yale Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA.
8
Department of Reproductive Medicine, University of California, San Diego, La Jolla, CA, USA.
9
Gladstone Institutes, San Francisco, CA, USA.
10
Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine, Rochester, MN, USA.
11
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA.
12
Department of Computer Science, Yale University, New Haven, CT, USA.
13
Bioinformatics Research Laboratory, Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA; amilosav@bcm.edu.

Abstract

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks. As exRNA profiling gains wide adoption in the research community, we anticipate that the strategies discussed here will increasingly be required to enable data reuse and to facilitate integrative analysis of exRNA data.

KEYWORDS:

DMRR; ERC Consortium; exRNA; exRNA Atlas; exRNA Portal

PMID:
26320941
PMCID:
PMC4553261

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