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Volume 6, Issue 7

HIV-DRIVES: HIV drug resistance identification, variant evaluation, and surveillance pipeline Open Access

Abstract

The global prevalence of resistance to antiviral drugs combined with antiretroviral therapy (cART) emphasizes the need for continuous monitoring to better understand the dynamics of drug-resistant mutations to guide treatment optimization and patient management as well as check the spread of resistant viral strains. We have recently integrated next-generation sequencing (NGS) into routine HIV drug resistance (HIVDR) monitoring, with key challenges in the bioinformatic analysis and interpretation of the complex data generated, while ensuring data security and privacy for patient information. To address these challenges, here we present HIV-DRIVES (HIV Drug Resistance Identification, Variant Evaluation, and Surveillance), an NGS-HIVDR bioinformatics pipeline that has been developed and validated using Illumina short reads, FASTA, and Sanger .seq files.

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  • Accepted:
  • Published Online:
Keyword(s): bioinformatics , HIV drug resistance , monitoring , mutations and next-generation sequencing
Funding
This study was supported by the:
  • Eastern Africa Network of Bioinformatics Training (EANBiT) project
    • Principle Award Recipient: StephenKanyerezi
  • African Doctoral Dissertation Research Fellowship (ADDRF)
    • Principle Award Recipient: StephenKanyerezi
  • EDCTP (Award TMA2020CDF-3159)
    • Principle Award Recipient: GeraldMboowa
  • Foundation for the National Institutes of Health (Award U2RTW010672)
    • Principle Award Recipient: GeraldMboowa
  • Foundation for the National Institutes of Health (Award U2RTW012116)
    • Principle Award Recipient: GeraldMboowa
  • CAGE-TB Project
    • Principle Award Recipient: IvanSserwadda
  • PHA4GE (Award INV-038071)
    • Principle Award Recipient: IvanSserwadda
© 2024 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. The Microbiology Society waived the open access fees for this article.
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/content/journal/acmi/10.1099/acmi.0.000815.v3
2024-07-17
2025-07-20
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References

  1. Pennings PS. HIV drug resistance: problems and perspectives. Infect Dis Rep 2013; 5:e5 [View Article] [PubMed]
     [Google Scholar]
  2. Rhee S-Y, Jordan MR, Raizes E, Chua A, Parkin N et al. HIV-1 drug resistance mutations: potential applications for point-of-care genotypic resistance testing. PLoS One 2015; 10:e0145772 [View Article] [PubMed]
     [Google Scholar]
  3. Rhee S-Y, Gonzales MJ, Kantor R, Betts BJ, Ravela J et al. Human immunodeficiency virus reverse transcriptase and protease sequence database. Nucleic Acids Res 2003; 31:298–303 [View Article] [PubMed]
     [Google Scholar]
  4. Shafer RW. Rationale and uses of a public HIV drug-resistance database. J Infect Dis 2006; 194:S51–S58 [View Article] [PubMed]
     [Google Scholar]
  5. Woods CK, Brumme CJ, Liu TF, Chui CKS, Chu AL et al. Automating HIV drug resistance genotyping with RECall, a freely accessible sequence analysis tool. J Clin Microbiol 2012; 50:1936–1942 [View Article] [PubMed]
     [Google Scholar]
  6. Marinier E, Enns E, Tran C, Fogel M, Peters C et al. quasitools: a collection of tools for viral quasispecies analysis. Bioinformatics 2019733238 [View Article]
     [Google Scholar]
  7. Krueger F. FelixKrueger/trimGalore: a wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data; 2023 https://github.com/FelixKrueger/TrimGalore accessed 27 September 2023
  8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
     [Google Scholar]
  9. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V et al. Twelve years of SAMtools and BCFtools. Gigascience 2021; 10:giab008 [View Article] [PubMed]
     [Google Scholar]
  10. Ho J, Ng G, Renaud M, Poon A. sierra-local: A lightweight standalone application for drug resistance prediction. J Open Source Softw 2019; 4:1186 [View Article]
     [Google Scholar]
  11. Sserwadda I, Mboowa G. rMAP: the rapid microbial analysis pipeline for ESKAPE bacterial group whole-genome sequence data. Microb Genom 2021; 7:000583 [View Article] [PubMed]
     [Google Scholar]
  12. Namaganda MM, Sendagire H, Kateete DP, Kigozi E, Luutu Nsubuga M et al. Next-generation sequencing (NGS) reveals low-abundance HIV-1 drug resistance mutations among patients experiencing virological failure at the time of therapy switching in Uganda. F1000Res 2022; 11:901 [View Article]
     [Google Scholar]
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HIV-DRIVES: HIV drug resistance identification, variant evaluation, and surveillance pipeline
Access Microbiology 6, 000815.v3 (2024); https://doi.org/10.1099/acmi.0.000815.v3
/content/journal/acmi/10.1099/acmi.0.000815.v3
/content/journal/acmi/10.1099/acmi.0.000815.v3
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