期刊
出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2108815119
关键词
HIV-1; recombination; evolution; stochastic block model; dynamic network
资金
- Government of Canada through Genome Canada
- Ontario Genomics Institute [OGI-131]
- Natural Sciences and Engineering Research Council of Canada [05516-2018 RGPIN]
- Canadian Institutes of Health Research [PJT-155990, PJT-156178, FRN-130609, BOP-149562]
This study developed a new clustering method to analyze HIV-1 genomes, revealing that most of the genomes exhibit recombination. It also proposed an informative framework for HIV-1 classification.
Theprevailing 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 x 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 HIV1 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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