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Updated: Jul 16, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
Published on: May 9, 2017
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
Wentao Li1, Miran Kim2,3,4, Kai Zhang1
1Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
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 .
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