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Bioinformatics. 2017 Nov 15;33(22):3627-3634. doi: 10.1093/bioinformatics/btx478.

BiobankUniverse: automatic matchmaking between datasets for biobank data discovery and integration.

Author information

1
Department of Genetics, Genomics Coordination Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
2
Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
3
Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland.
4
Biobanking and BioMolecular Resources Research Infrastructure (BBMRI-ERIC), Graz, Austria.

Abstract

Motivation:

Biobanks are indispensable for large-scale genetic/epidemiological studies, yet it remains difficult for researchers to determine which biobanks contain data matching their research questions.

Results:

To overcome this, we developed a new matching algorithm that identifies pairs of related data elements between biobanks and research variables with high precision and recall. It integrates lexical comparison, Unified Medical Language System ontology tagging and semantic query expansion. The result is BiobankUniverse, a fast matchmaking service for biobanks and researchers. Biobankers upload their data elements and researchers their desired study variables, BiobankUniverse automatically shortlists matching attributes between them. Users can quickly explore matching potential and search for biobanks/data elements matching their research. They can also curate matches and define personalized data-universes.

Availability and implementation:

BiobankUniverse is available at http://biobankuniverse.com or can be downloaded as part of the open source MOLGENIS suite at http://github.com/molgenis/molgenis.

Contact:

m.a.swertz@rug.nl.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29036577
PMCID:
PMC5870622
DOI:
10.1093/bioinformatics/btx478
[Indexed for MEDLINE]
Free PMC Article

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