ArticleProceedings of the National Academy of Sciences of the United States of America2022
Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Pervasive HIV recombination limits the utility of circulating recombinant form nomenclature.PLoS pathogens · 2026Article
- Atlas of HIV cis-regulatory elements reveals extensive transcriptional variation across clades, isolates, and within individuals.bioRxiv : the preprint server for biology · 2026Article
- Review
- Computer aided design of inhibitor molecules against Vpr protein from different HIV-1 subtypes.In silico pharmacology · 2025Article
- Article
- Characterization of HIV-1 CRF02_AG/A3/G unique recombinant forms identified among children in Larkana, Pakistan.Frontiers in cellular and infection microbiology · 2023Article
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7 authors.
Funding
Abstract
The prevailing abundance of full-length HIV type 1 (HIV-1) genome sequences provides an opportunity to revisit the standard model of HIV-1 group M (HIV-1/M) diversity that clusters genomes into largely nonrecombinant subtypes, which is not consistent with recent evidence of deep recombinant histories for simian immunodeficiency virus (SIV) and other HIV-1 groups. Here we develop an unsupervised nonparametric clustering approach, which does not rely on predefined nonrecombinant genomes, by adapting a community detection method developed for dynamic social network analysis. We show that this method (dynamic stochastic block model [DSBM]) attains a significantly lower mean error rate in detecting recombinant breakpoints in simulated data (quasibinomial generalized linear model (GLM), P<8×10−8), compared to other reference-free recombination detection programs (genetic algorithm for recombination detection [GARD], recombination detection program 4 [RDP4], and RDP5). When this method was applied to a representative sample of n = 525 actual HIV-1 genomes, we determined k = 29 as the optimal number of DSBM clusters and used change-point detection to estimate that at least 95% of these genomes are recombinant. Further, we identified both known and undocumented recombination hotspots in the HIV-1 genome and evidence of intersubtype recombination in HIV-1 subtype reference genomes. We propose that clusters generated by DSBM can provide an informative framework for HIV-1 classification.
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