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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...

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Autores principales: Li, Wentao, Kim, Miran, Zhang, Kai, Chen, Han, Jiang, Xiaoqian, Harmanci, Arif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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.
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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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