Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform

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• dc.contributor.author Escribà Montagut, Xavier
• dc.contributor.author Marcon, Yannick
• dc.contributor.author Anguita Ruiz, Augusto
• dc.contributor.author Avraam, Demetris
• dc.contributor.author Urquiza, José M.
• dc.contributor.author Morgan, Andrei S.
• dc.contributor.author Wilson, Rebecca C.
• dc.contributor.author Burton, Paul
• dc.contributor.author González, Juan Ramón
• dc.date.accessioned 2025-02-12T07:21:08Z
• dc.date.available 2025-02-12T07:21:08Z
• dc.date.issued 2024
• dc.description.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.
• dc.description.sponsorship 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].
• dc.format.mimetype application/pdf
• dc.identifier.citation Escriba-Montagut X, Marcon Y, Anguita-Ruiz A, Avraam D, Urquiza J, Morgan AS, et al. Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. PLoS Comput Biol. 2024 Dec 9;20(12):e1012626. DOI: 10.1371/journal.pcbi.1012626
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1012626
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/69587
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLoS Comput Biol. 2024 Dec 9;20(12):e1012626
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/825775
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-122855OB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/CEX2023-0001290-S
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/824989
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/874583
• dc.rights © 2024 Escriba-Montagut et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Genome-wide association studies
• dc.subject.keyword Metaanalysis
• dc.subject.keyword Genome analysis
• dc.subject.keyword Genomics
• dc.subject.keyword Algorithms
• dc.subject.keyword Data management
• dc.subject.keyword Single nucleotide polymorphisms
• dc.subject.keyword Statistical data
• dc.title Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion