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COLLAGENE enables privacy-aware federated and collaborative genomic data analysis.

ABSTRACT: 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 .

SUBMITTER: Li W

PROVIDER: S-EPMC10496350 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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COLLAGENE enables privacy-aware federated and collaborative genomic data analysis.

Li Wentao W Kim Miran M Zhang Kai K Chen Han H Jiang Xiaoqian X Harmanci Arif A

Genome biology 20230911 1

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 too ...[more]

PMID: 37697426

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