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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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Detalles Bibliográficos
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
Descripción
Sumario: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.