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Updated: Apr 17, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
Published on: May 6, 2010
Federated, governed, and interoperable? The emerging architecture of public human genomic data infrastructures: a
Marco Antonio Tangaro1,2, Matteo Chiara1,3, Graziano Pesole1,4
1Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR), Bari, Italy.
Abstract:
Public infrastructures for human genomic data are increasingly incorporating federated approaches alongside centralized and cloud-native models, yet operational federation remains constrained by unsolved challenges at the legal, semantic, and technical layers. We describe the current landscape along three analytical axes, taking a primarily European perspective while drawing on global examples to highlight broader trends. First, we compare architectural models, centralized archives such as the European Genome-phenome Archive (EGA) and the database of Genotypes and Phenotypes (dbGaP), cloud-native platforms for data analysis, and federated networks exemplified by the European Genomic Data Infrastructure (GDI), highlighting their specific trade-offs on scalability, sovereignty, and analytical flexibility. Second, we examine the governance layer, from the tension between the GDPR's consent requirements and large-scale secondary use, through the European Health Data Space (EHDS) and Health Data Access Bodies, to machine-readable authorization via GA4GH Passports and the Data Use Ontology. Third, we assess interoperability and semantic alignment, including the role of GA4GH technical standards, FAIR metadata principles, and emerging schema harmonization efforts such as the German Human Genome-Phenome Archive (GHGA). We argue that the central challenge is no longer building individual platforms, but aligning heterogeneous regulatory interpretations, metadata models, and trust frameworks across jurisdictions. Addressing this alignment gap will determine whether federated genomics delivers on its promise of large-scale, privacy-preserving data reuse.
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