Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.
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....
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012626 |
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| _version_ | 1846091593137782784 |
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| author | Xavier Escriba-Montagut Yannick Marcon Augusto Anguita-Ruiz Demetris Avraam Jose Urquiza Andrei S Morgan Rebecca C Wilson Paul Burton Juan R Gonzalez |
| author_facet | Xavier Escriba-Montagut Yannick Marcon Augusto Anguita-Ruiz Demetris Avraam Jose Urquiza Andrei S Morgan Rebecca C Wilson Paul Burton Juan R Gonzalez |
| author_sort | Xavier Escriba-Montagut |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-979ab60608ab41c3bb2162a654184074 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-979ab60608ab41c3bb2162a6541840742025-01-10T05:31:27ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-12-012012e101262610.1371/journal.pcbi.1012626Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.Xavier Escriba-MontagutYannick MarconAugusto Anguita-RuizDemetris AvraamJose UrquizaAndrei S MorganRebecca C WilsonPaul BurtonJuan R GonzalezThe 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.https://doi.org/10.1371/journal.pcbi.1012626 |
| spellingShingle | Xavier Escriba-Montagut Yannick Marcon Augusto Anguita-Ruiz Demetris Avraam Jose Urquiza Andrei S Morgan Rebecca C Wilson Paul Burton Juan R Gonzalez Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. PLoS Computational Biology |
| title | Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. |
| title_full | Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. |
| title_fullStr | Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. |
| title_full_unstemmed | Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. |
| title_short | Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform. |
| title_sort | federated privacy protected meta and mega omics data analysis in multi center studies with a fully open source analytic platform |
| url | https://doi.org/10.1371/journal.pcbi.1012626 |
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