Cloud-Based Platforms for Genomic Data Processing

Abstract

The exponential growth of genomic data has outpaced the capacity of institutional high-performance computing clusters, driving migration to cloud-based platforms that offer elastic compute resources, pre-configured analytical pipelines, and collaborative workspaces, yet cloud adoption for genomic data processing varies widely across platform architectures and institutional contexts. We evaluated 218 cloud-based genomic processing programmes across centres in Spain, Germany, and Switzerland between 2017 and 2021, spanning five platform categories: general-purpose cloud infrastructure with custom pipelines, managed genomics-specific platforms, workflow-engine-based execution environments, federated analysis platforms for multi-site data, and hybrid cloud-HPC architectures. A Cloud Genomics Platform Index (CGPI) was constructed from five sub-scores -- computational cost-efficiency, pipeline reproducibility, data security and compliance, scalability to biobank cohorts, and user accessibility for non-specialists -- with weights from regression against sustained institutional adoption beyond 12 months. CGPI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted platforms with an AUC of 0.882. Managed genomics platforms scored highest (mean CGPI 0.824), while federated analysis trailed at 0.598. Only 35.3 percent of programmes exceeded the 0.75 threshold. Pipeline reproducibility carried the largest regression weight (beta = +0.278), followed by data security compliance (beta = +0.232).