Efficient data management infrastructure for the integration of imaging and omics data in life science research

Kuhn Cuellar, L.; Friedrich, A.; Gabernet, G.; de la Garza, L.; Fillinger, S.; Seyboldt, A.; zur Oven-Krockhaus, S.; Wanke, F.; Richter, S.; Thaiss, W. M.; Horger, M.; Malek, N.; Harter, K.; Bitzer, M.; Nahnsen, S.

doi:10.1101/2019.12.28.889295

bioRxiv · 10.1101/2019.12.28.889295

Efficient data management infrastructure for the integration of imaging and omics data in life science research

Kuhn Cuellar, L.·Friedrich, A.·Gabernet, G.·de la Garza, L.·Fillinger, S.·Seyboldt, A.·zur Oven-Krockhaus, S.·Wanke, F.·Richter, S.·Thaiss, W. M.·Horger, M.·Malek, N.·Harter, K.·Bitzer, M.·Nahnsen, S.

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Abstract

As technical developments in omics and biomedical imaging drive the increase in quality, modality, and throughput of data generation in life sciences, the need for information systems capable of integrative, long-term storage and management of these heterogeneous digital assets is also increasing. Here, we propose an approach based on principles of Service Oriented Architecture design, to allow the integrated management and analysis of multi-omics and biomedical imaging data. The proposed architecture introduces an interoperable image management system, the OMERO server, into the backend of qPortal, a FAIR-compliant web-based platform for omics data management. The implementation of an integrated metadata model, the development of software components to enable the communication with the OMERO server, and an extension to the data management operations of established software, allows for FAIR management of heterogeneous omics and biomedical imaging data within an integrated system, which facilitates metadata queries from web-based scientific applications. The applicability of the proposed architecture is demonstrated in two prototypical use cases, a plant biology study using confocal scanning microscopy, and a clinical study on hepatocellular carcinoma, with data from a variety of medical imaging and omics modalities. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, as the joint analysis of omics and imaging data becomes not only desirable but necessary to derive novel insights into biological processes. In particular for powerful machine learning applications where the availability of large datasets with high quality phenotypic annotations is a requirement.

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Kuhn Cuellar, L., Friedrich, A., Gabernet, G., de la Garza, L., Fillinger, S., Seyboldt, A., zur Oven-Krockhaus, S., Wanke, F., Richter, S., Thaiss, W. M., Horger, M., Malek, N., Harter, K., Bitzer, M., Nahnsen, S.. 2019-12-30. Efficient data management infrastructure for the integration of imaging and omics data in life science research. https://doi.org/10.1101/2019.12.28.889295

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