Abstract
BackgroundAs technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.ResultsWe propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.ConclusionsWe describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.
Highlights
As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing
The large volume of data produced by various omics disciplines, biomedical imaging techniques (e.g. X-ray Computerized Tomography (CT) and Positron-Emission Tomography with two different tracers (PET)), and the unprecedented increase in spatial resolution achieved by conventional confocal and super-resolution light microscopy [1] and modern electron microscopes [2], present a challenge for long-term storage and management of these high-dimensional digital assets, especially with regard to the requirements imposed by the FAIR data management principles [3]
An integrative model was established, which defines detailed metadata boundaries between the project and omics domains, as managed by the Open Biological Information System (openBIS) server, and the imaging domain stored in the Open Microscopy Environment Remote Objects (OMERO) server
Summary
As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. It is of particular importance to employ rich metadata models that allow researchers to relate data from different disciplines, and design experiments using an integrative approach to handle both, multilayer omics, as well as biomedical imaging data. The increasing data volume and experimental design complexity call for data management systems allowing for the integration of multi-omics and imaging data. The OMERO server is capable of managing imaging data a large variety of microscopy and medical imaging modalities (e.g. fluorescence and electron microscopy, histological imaging and medical CT), since it is able to handle multi-channel, 2D or 3D imaging data, with time series support. Most of the available platforms focus on a limited set of omics disciplines, leading to metadata models that are not well-suited to describe complex experimental designs with multiple omics and imaging modalities
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