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Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.

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.

SUBMITTER: Olabode AS

PROVIDER: S-EPMC9171507 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.

Olabode Abayomi S AS Ng Garway T GT Wade Kaitlyn E KE Salnikov Mikhail M Grant Heather E HE Dick David W DW Poon Art F Y AFY

Proceedings of the National Academy of Sciences of the United States of America 20220502 19

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 adapti ...[more]

PMID: 35500121

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Dataset Information

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.

Publications

Revisiting the recombinant history of HIV-1 group M with dynamic network community detection.

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