ArticleBioinformatics (Oxford, England)2026
A scalable HPC framework for bioinformatics in resource-limited settings: design principles, implementation, and sustainability from the UVRI experience.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
motivationBuilding and sustaining High-Performance Computing (HPC) infrastructure for bioinformatics research in resource-limited settings presents significant technical, financial and operational challenges. Institutions in low-and middle-income regions often face constraints such as limited technical expertise, unstable infrastructure and restricted funding which can hinder the deployment of large-scale computational platforms necessary for modern genomics and bioinformatics analyses.
resultsWe present a scalable and modular HPC framework developed at the Uganda Virus Research Institute (UVRI) to support large-scale genomics and other omics data analyses in resource-limited settings. The framework integrates open-source HPC management tools, infrastructure automation, and reproducible configuration management to enable reliable deployment and maintenance. Optimized storage and networking configurations combined with a phased capacity-building strategy support high-throughput genomic workflows while strengthening local technical expertise. From our implementation experience, we derive ten practical design and operational rules that provide a transferable methodology for establishing and sustaining in-house HPC infrastructure. These rules emphasize strategic investment in human capacity, structured planning, leveraging collaborations, adoption of open-source technologies and service management practices to improve operational resilience and long-term sustainability. AVAILABILITY: The design principles, automation strategies and implementation guidelines described in this work are applicable to institutions seeking to establish sustainable HPC resources for bioinformatics research in resource-constrained environments.
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