Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings. Issue 13 (November 2019)

• Record Type: Journal Article
• Authors: Mabvakure, Batsirai M; Rott, Raymond; Dobrowsky, Leslie; Van Heusden, Peter; Morris, Lynn; Scheepers, Cathrine; Moore, Penny L
• Is Part Of: Bioinformatics and biology insights. Volume 2019:Issue 13(2019)
• Publication Date: 2019-11
• Subjects: High-performance computing -- bioinformatics -- data analysis -- large data -- low-cost systems -- next-generation sequencing -- cluster; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; 570.285
• DOI: 10.1177/1177932219882347
• Languages: English
• ISSNs: 1177-9322

Abstract

Next-generation sequencing (NGS) technologies have revolutionized biological research by generating genomic data that were once unaffordable by traditional first-generation sequencing technologies. These sequencing methodologies provide an opportunity for in-depth analyses of host and pathogen genomes as they are able to sequence millions of templates at a time. However, these large datasets can only be efficiently explored using bioinformatics analyses requiring huge data storage and computational resources adapted for high-performance processing. High-performance computing allows for efficient handling of large data and tasks that may require multi-threading and prolonged computational times, which is not feasible with ordinary computers. However, high-performance computing resources are costly and therefore not always readily available in low-income settings. We describe the establishment of an affordable high-performance computing bioinformatics cluster consisting of 3 nodes, constructed using ordinary desktop computers and open-source software including Linux Fedora, SLURM Workload Manager, and the Conda package manager. For the analysis of large antibody sequence datasets and for complex viral phylodynamic analyses, the cluster out-performed desktop computers. This has demonstrated that it is possible to construct high-performance computing capacity capable of analyzing large NGS data from relatively low-cost hardware and entirely free (open-source) software, even in resource-limited settings. Such a cluster design has broad utility beyond bioinformatics to other studies that require high-performance computing.