ArticleScientific reports2020
Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Men who have sex with men newly diagnosed with HIV-1 in Portugal (2023-2024): a comparative analysis of transmission clusters between migrants and non-migrants.Scientific reports · 2026Article
- T-shaped alignments integrating HIV-1 near full-length genome and partial pol sequences can improve phylogenetic inference of transmission clusters.PLoS computational biology · 2025Article
- Newly Diagnosed Individuals in Molecular HIV-1 Clusters in Rhode Island Over 3 Decades.The Journal of infectious diseases · 2025Article
- Integrating Partner Services and Molecular Epidemiology Data to Enhance HIV Transmission Disruption in Rhode Island.Open forum infectious diseases · 2025Article
- Dynamics of clustering rates in the Rhode Island HIV-1 epidemic.AIDS (London, England) · 2025Article
- Phylodynamics analysis of HIV epidemic history in Belarus in 1987-2022.Frontiers in epidemiology · 2025Article
- Molecular epidemiology of HIV-1 in Hungary: an evolving contact zone of colliding virus subtypes.Frontiers in microbiology · 2025Article
- User experience with HIV molecular epidemiology in research, surveillance, and cluster detection and response: a needs assessment.Current medical research and opinion · 2024Article
- Prospective Evaluation of Routine Statewide Integration of Molecular Epidemiology and Contact Tracing to Disrupt Human Immunodeficiency Virus Transmission.Open forum infectious diseases · 2024Article
- AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters.Frontiers in bioinformatics · 2024Article
- High HIV diversity, recombination, and superinfection revealed in a large outbreak among persons who inject drugs in Kentucky and Ohio, USA.Virus evolution · 2024Article
- Article
- Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.PLoS computational biology · 2022Article
- Article
- Exploring the Evolutionary History and Phylodynamics of Human Immunodeficiency Virus Type 1 Outbreak From Unnao, India Using Phylogenetic Approach.Frontiers in microbiology · 2022Article
- Statewide Longitudinal Trends in Transmitted HIV-1 Drug Resistance in Rhode Island, USA.Open forum infectious diseases · 2022Article
- HIV Drug Resistance and Transmission Networks Among a Justice-Involved Population at the Time of Community Reentry in Washington, D.C.AIDS research and human retroviruses · 2021Article
- Longitudinal typing of molecular HIV clusters in a statewide epidemic.AIDS (London, England) · 2021Article
- Article
- Molecular Epidemiological Analysis of the Origin and Transmission Dynamics of the HIV-1 CRF01_AE Sub-Epidemic in Bulgaria.Viruses · 2021Article
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17 authors.
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Abstract
Public health interventions guided by clustering of HIV-1 molecular sequences may be impacted by choices of analytical approaches. We identified commonly-used clustering analytical approaches, applied them to 1886 HIV-1 Rhode Island sequences from 2004-2018, and compared concordance in identifying molecular HIV-1 clusters within and between approaches. We used strict (topological support ≥ 0.95; distance 0.015 substitutions/site) and relaxed (topological support 0.80-0.95; distance 0.030-0.045 substitutions/site) thresholds to reflect different epidemiological scenarios. We found that clustering differed by method and threshold and depended more on distance than topological support thresholds. Clustering concordance analyses demonstrated some differences across analytical approaches, with RAxML having the highest (91%) mean summary percent concordance when strict thresholds were applied, and three (RAxML-, FastTree regular bootstrap- and IQ-Tree regular bootstrap-based) analytical approaches having the highest (86%) mean summary percent concordance when relaxed thresholds were applied. We conclude that different analytical approaches can yield diverse HIV-1 clustering outcomes and may need to be differentially used in diverse public health scenarios. Recognizing the variability and limitations of commonly-used methods in cluster identification is important for guiding clustering-triggered interventions to disrupt new transmissions and end the HIV epidemic.
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