Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings

Batsirai M Mabvakure; Raymond Rott; Leslie Dobrowsky; Peter Van Heusden; Lynn Morris; Cathrine Scheepers; Penny L Moore

doi:10.1177/1177932219882347

Advancing HIV Vaccine Research With Low-Cost High-Performance Computing Infrastructure: An Alternative Approach for Resource-Limited Settings

Bioinform Biol Insights. 2019 Nov 7:13:1177932219882347. doi: 10.1177/1177932219882347. eCollection 2019.

Authors

Batsirai M Mabvakure^{1

2

3}, Raymond Rott⁴, Leslie Dobrowsky⁴, Peter Van Heusden⁵, Lynn Morris^{1

2

6}, Cathrine Scheepers^{1

2}, Penny L Moore^{1

2

6}

Affiliations

¹ Center for HIV and STIs, National Institute for Communicable Diseases, National Health Laboratory Service (NHLS), Johannesburg, South Africa.
² Antibody Immunity Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
³ Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁴ Bridge-the-Gap, Johannesburg, South Africa.
⁵ South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa.
⁶ Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa.

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.

Keywords: High-performance computing; bioinformatics; cluster; data analysis; large data; low-cost systems; next-generation sequencing.