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COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496350/ https://www.ncbi.nlm.nih.gov/pubmed/37697426 http://dx.doi.org/10.1186/s13059-023-03039-z |
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author | Li, Wentao Kim, Miran Zhang, Kai Chen, Han Jiang, Xiaoqian Harmanci, Arif |
author_facet | Li, Wentao Kim, Miran Zhang, Kai Chen, Han Jiang, Xiaoqian Harmanci, Arif |
author_sort | Li, Wentao |
collection | PubMed |
description | Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03039-z. |
format | Online Article Text |
id | pubmed-10496350 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104963502023-09-13 COLLAGENE enables privacy-aware federated and collaborative genomic data analysis Li, Wentao Kim, Miran Zhang, Kai Chen, Han Jiang, Xiaoqian Harmanci, Arif Genome Biol Software Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03039-z. BioMed Central 2023-09-11 /pmc/articles/PMC10496350/ /pubmed/37697426 http://dx.doi.org/10.1186/s13059-023-03039-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Li, Wentao Kim, Miran Zhang, Kai Chen, Han Jiang, Xiaoqian Harmanci, Arif COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title | COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title_full | COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title_fullStr | COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title_full_unstemmed | COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title_short | COLLAGENE enables privacy-aware federated and collaborative genomic data analysis |
title_sort | collagene enables privacy-aware federated and collaborative genomic data analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496350/ https://www.ncbi.nlm.nih.gov/pubmed/37697426 http://dx.doi.org/10.1186/s13059-023-03039-z |
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