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Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort
Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074708/ https://www.ncbi.nlm.nih.gov/pubmed/32023939 http://dx.doi.org/10.3390/microorganisms8020196 |
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author | Mak, Lauren Perera, Deshan Lang, Raynell Kossinna, Pathum He, Jingni Gill, M. John Long, Quan van Marle, Guido |
author_facet | Mak, Lauren Perera, Deshan Lang, Raynell Kossinna, Pathum He, Jingni Gill, M. John Long, Quan van Marle, Guido |
author_sort | Mak, Lauren |
collection | PubMed |
description | Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology. |
format | Online Article Text |
id | pubmed-7074708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70747082020-03-20 Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort Mak, Lauren Perera, Deshan Lang, Raynell Kossinna, Pathum He, Jingni Gill, M. John Long, Quan van Marle, Guido Microorganisms Article Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology. MDPI 2020-01-31 /pmc/articles/PMC7074708/ /pubmed/32023939 http://dx.doi.org/10.3390/microorganisms8020196 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mak, Lauren Perera, Deshan Lang, Raynell Kossinna, Pathum He, Jingni Gill, M. John Long, Quan van Marle, Guido Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title | Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title_full | Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title_fullStr | Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title_full_unstemmed | Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title_short | Evaluation of A Phylogenetic Pipeline to Examine Transmission Networks in A Canadian HIV Cohort |
title_sort | evaluation of a phylogenetic pipeline to examine transmission networks in a canadian hiv cohort |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7074708/ https://www.ncbi.nlm.nih.gov/pubmed/32023939 http://dx.doi.org/10.3390/microorganisms8020196 |
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