Abstract
The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.
Copyright: © 2024 Escriba-Montagut et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
MeSH terms
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Computational Biology* / methods
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Computer Security
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Data Analysis
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Genome-Wide Association Study / methods
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Genomics / methods
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Humans
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Multicenter Studies as Topic
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Privacy
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Software*
Grants and funding
This research has received funding from the Spanish Ministry of Education, Innovation and Universities, the National Agency for Research and the Fund for Regional Development (PID2021-122855OB-I00). We also acknowledge support from the grant CEX2023-0001290-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program and the Consolidated Group on HEALTH ANALYTICS (2021 SGR 01563). This project has also been partially funded from the “Complementary Plan for Biotechnology Applied to Health,” coordinated by the Institut de Bioenginyeria de Catalunya (IBEC) within the framework of the Recovery, Transformation, and Resilience Plan (C17.I1) - Funded by the European Union - NextGenerationEU. We also thank CINECA project (EC H2020 grant 825775) for making synthetic GWAS data available through EGA repository. JU is supported by Catalan program PERIS (Ref.: SLT017/20/000119), granted by Departament de Salut de la Generalitat de Catalunya (Spain). We also thank EUCAN-Connect project (A federated FAIR platform enabling large-scale analysis of high-value cohort data connecting Europe and Canada in personalized health) funded by the European Commission H2020 Flagship Collaboration with Canada (No 824989), the ATHLETE project (Advancing Tools for Human Early Lifecourse Exposome Research and Translation) funded by the European Commission Horizon 2020 research and innovation programme (No 874583) and UKRI Innovation Fellowship with Health Data Research UK [MR/S003959/1].