ArticleAIDS research and human retroviruses2017
Identifying Transmission Clusters with Cluster Picker and HIV-TRACE.
Article in AIDS research and human retroviruses, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
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- Molecular network characteristics and transmissible drug resistance of HIV-1 CRF55_01B in Yunnan Province.BMC infectious diseases · 2026Article
- Characterization of the HIV-1 molecular network in a middle-aged population aged 50 years and older in a City in Southern Sichuan, China.Scientific reports · 2025Article
- Geospatial and phylogenetic clustering of acute and recent HIV infections in Lilongwe, Malawi.PLOS global public health · 2025Article
- Exploring Dynamic Changes in HIV-1 Molecular Transmission Networks and Key Influencing Factors: Cross-Sectional Study.JMIR public health and surveillance · 2024Article
- HIV-1 molecular transmission network and drug resistance in Chongqing, China, among men who have sex with men (2018-2021).Virology journal · 2023Article
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- Molecular epidemiology of HIV-1 among adult female sex workers at the Guatemala-Mexico border.Global public health · 2023Article
- Epidemiological characteristics of HIV transmission in southeastern China from 2015 to 2020 based on HIV molecular network.Frontiers in public health · 2023Article
- Characterization of HIV-1 Transmission Clusters Inferred from the Brazilian Nationwide Genotyping Service Database.Viruses · 2022Article
- Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.PLoS computational biology · 2022Article
- Antiretroviral Drug Resistance in HIV Sequences From People Who Inject Drugs and Men Who Have Sex With Men Across 21 Cities in India.Open forum infectious diseases · 2022Article
- A deep learning approach to real-time HIV outbreak detection using genetic data.PLoS computational biology · 2022Article
- Transmission networks of hepatitis C virus among HIV/HCV-coinfected patients in Guangdong, China.Virology journal · 2022Article
- Sorting by Race/Ethnicity Across HIV Genetic Transmission Networks in Three Major Metropolitan Areas in the United States.AIDS research and human retroviruses · 2021Article
- Spatiotemporal Phylodynamics of Hepatitis C Among People Who Inject Drugs in India.Hepatology (Baltimore, Md.) · 2021Article
- Human Immunodeficiency Virus (HIV) Drug Resistance, Phylogenetic Analysis, and Superinfection Among Men Who Have Sex with Men and Transgender Women in Sub-Saharan Africa: HIV Prevention Trials Network (HPTN) 075 Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021Article
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10 authors.
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Abstract
We compared the behavior of two approaches (Cluster Picker and HIV-TRACE) at varying genetic distances to identify transmission clusters. We used three HIV gp41 sequence datasets originating from the Rakai Community Cohort Study: (1) next-generation sequence (NGS) data from nine linked couples; (2) NGS data from longitudinal sampling of 14 individuals; and (3) Sanger consensus sequences from a cross-sectional dataset (n = 1,022) containing 91 epidemiologically linked heterosexual couples. We calculated the optimal genetic distance threshold to separate linked versus unlinked NGS datasets using a receiver operating curve analysis. We evaluated the number, size, and composition of clusters detected by Cluster Picker and HIV-TRACE at six genetic distance thresholds (1%-5.3%) on all three datasets. We further tested the effect of using all NGS, versus only a single variant for each patient/time point, for datasets (1) and (2). The optimal gp41 genetic distance threshold to distinguish linked and unlinked couples and individuals was 5.3% and 4%, respectively. HIV-TRACE tended to detect larger and fewer clusters, whereas Cluster Picker detected more clusters containing only two sequences. For NGS datasets (1) and (2), HIV-TRACE and Cluster Picker detected all linked pairs at 3% and 4% genetic distances, respectively. However, at 5.3% genetic distance, 20% of couples in dataset (3) did not cluster using either program, and for >1/3 of couples cluster assignment were discordant. We suggest caution in choosing thresholds for clustering analyses in a generalized epidemic.
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