Evidence map›Paper›PMID 32772080›Full record

ArticleThe Journal of antimicrobial chemotherapy2020

Performance of a high-throughput next-generation sequencing method for analysis of HIV drug resistance and viral load.

Jessica M Fogel, David Bonsall, Vanessa Cummings, Rory Bowden, Tanya Golubchik, Mariateresa de Cesare, Ethan A Wilson, Theresa Gamble, Carlos Del Rio, D Scott Batey and 7 more

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.4field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 24 citations in OpenAlex.

  1. Patterns of HIV-1 Viral Load Suppression and Drug Resistance During the Dolutegravir Transition: A Population-based Longitudinal Study.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 12 institutions in 2 countries.

Jessica M FogelDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
David BonsallBig Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Vanessa CummingsDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Rory BowdenWellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Tanya GolubchikBig Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Mariateresa de CesareWellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
Ethan A WilsonFred Hutchinson Cancer Research Center, Seattle, WA, USA.
Theresa GambleFHI 360, Durham, NC, USA.
Carlos Del RioHubert Department of Global Health, Emory University Rollins School of Public Health, Atlanta, GA, USA.
D Scott BateyDepartment of Social Work, University of Alabama at Birmingham, Birmingham, AL, USA.
Kenneth H MayerDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Jason E FarleyThe REACH Initiative, Johns Hopkins University School of Nursing, Baltimore, MD, USA.
James P HughesDepartment of Biostatistics, University of Washington, Seattle, WA, USA.
Robert H RemienHIV Center for Clinical and Behavioral Studies, NY State Psychiatric Institute, New York, NY, USA.
Chris BeyrerDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Christophe FraserBig Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Susan H EshlemanDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Johns Hopkins University · USCentre for Human Genetics · GBUniversity of Oxford · GBEmory University · USFamily Health International 360 · USFred Hutch Cancer Center · USHarvard University · USJohns Hopkins Medicine · USNew York Psychoanalytic Society and Institute · USOpen Data Institute · GBUniversity of Alabama at Birmingham · USUniversity of Washington · US

Funding

LOC: HIV Prevention Trials NetworkUM1AI068619 · NIAID · FAMILY HEALTH INTERNATIONAL · PI Sinead Delany-Moretlwe, RAPHAEL J LANDOVITZ · 2011 to 2026
$779.5M
SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
LC: HIV Prevention Trials Network - Laboratory Support for the SARS-CoV-2 Seroprevalence Study (CoVPN 5002)UM1AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI SUSAN H ESHLEMAN, Mark A Marzinke · 2011 to 2026
$100.2M
The impact of HIV viral diversity and cellular immunity on HIV pathogenesisP30AI060354 · NIAID · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI RAJESH T GANDHI · 2004 to 2026
$94.4M
HIV Prevention Trials Network: Network LaboratoryU01AI068613 · NIAID · JOHNS HOPKINS UNIVERSITY · PI ESHLEMAN, SUSAN H · 2006 to 2010
$16.5M
NIAID NIH HHS P30 AI060354NIAID NIH HHS U01 AI068613NIAID NIH HHS UM1 AI068613NIAID NIH HHS UM1 AI068617NIAID NIH HHS UM1 AI068619
6 · The paper itself

Abstract

objectivesTo evaluate the performance of a high-throughput research assay for HIV drug resistance testing based on whole genome next-generation sequencing (NGS) that also quantifies HIV viral load.

methodsPlasma samples (n = 145) were obtained from HIV-positive MSM (HPTN 078). Samples were analysed using clinical assays (the ViroSeq HIV-1 Genotyping System and the Abbott RealTime HIV-1 Viral Load assay) and a research assay based on whole-genome NGS (veSEQ-HIV).

resultsHIV protease and reverse transcriptase sequences (n = 142) and integrase sequences (n = 138) were obtained using ViroSeq. Sequences from all three regions were obtained for 100 (70.4%) of the 142 samples using veSEQ-HIV; results were obtained more frequently for samples with higher viral loads (93.5% for 93 samples with >5000 copies/mL; 50.0% for 26 samples with 1000-5000 copies/mL; 0% for 23 samples with <1000 copies/mL). For samples with results from both methods, drug resistance mutations (DRMs) were detected in 33 samples using ViroSeq and 42 samples using veSEQ-HIV (detection threshold: 5.0%). Overall, 146 major DRMs were detected; 107 were detected by both methods, 37 were detected by veSEQ-HIV only (frequency range: 5.0%-30.6%) and two were detected by ViroSeq only. HIV viral loads estimated by veSEQ-HIV strongly correlated with results from the Abbott RealTime Viral Load assay (R2 = 0.85; n = 142).

conclusionsThe NGS-based veSEQ-HIV method provided results for most samples with higher viral loads, was accurate for detecting major DRMs, and detected mutations at lower levels compared with a method based on population sequencing. The veSEQ-HIV method also provided HIV viral load data.

Indexed as

Anti-HIV AgentsHIV InfectionsSexual and Gender MinoritiesDrug Resistance, ViralGenotypeHigh-Throughput Nucleotide SequencingHomosexuality, MaleHumansMaleMutationRNA, ViralViral LoadAnti-HIV AgentsRNA, Viral

Identifiers

PMID32772080
PMCPMC7662169
OpenAlexW3047978686

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.