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[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the
Martin Lablans1, Dennis Kadioglu2, Sebastian Mate3
1Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. m.lablans@dkfz.de.
Background:
Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage.
Objectives:
Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies.
Materials And Methods:
Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity.
Results:
The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty.
Conclusions:
Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.
