ArticleBioinformatics advances2024
HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis.
Article in Bioinformatics advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Local implementation of scalable malaria molecular surveillance in a low-resource setting: a North-South collaborative model.Malaria journal · 2026Article
- A scalable HPC framework for bioinformatics in resource-limited settings: design principles, implementation, and sustainability from the UVRI experience.Bioinformatics (Oxford, England) · 2026Article
- Pathways, outputs and impact of NIH-supported bioinformatics and genomics graduate trainees in Africa.Briefings in bioinformatics · 2026Review
- The utility of integrating nanopore sequencing into routine HIV-1 drug resistance surveillance.Microbial genomics · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Summary: Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms. Availability and implementation: HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.