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HIV‐1 proviral landscape characterization varies by pipeline analysis
Introduction HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence de...
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Published in: | Journal of the International AIDS Society 2021-07, Vol.24 (7), p.e25725-n/a |
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description | Introduction
HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence defects that may be present. We investigated the effects that different analysis pipelines had on the characterization of HIV‐1 proviral sequences.
Methods
We used single genome amplification to generate near full‐length (NFL) HIV‐1 proviral DNA sequences, defined as amplicons greater than 8000 base pairs in length, isolated from peripheral blood mononuclear cells (PBMC) of treated suppressed participants with HIV‐1. Amplicons underwent direct next‐generation single genome sequencing and were analysed using four HIV‐1 proviral characterization pipelines. Sequences were characterized as intact or defective; defective sequences were assessed for the number and types of defects present. To confirm and extend our findings, 691 proviruses from the Proviral Sequence Database (PSD) were analysed and the ProSeq‐IT tool of the PSD was used to characterize both the participant and PSD proviruses.
Results and discussion
Virus sequences derived from thirteen ART‐treated virologically suppressed participants with HIV were studied. A total of 693 HIV‐1 proviral sequences were generated, 282 of which were NFL. An average of 53 sequences per participant was analysed. We found that proviruses often harbour multiple sequence defect types (mean 2.7, 95% confidence interval [CI] 2.5, 3.0); the elimination order used by each pipeline affected the percentage of proviruses allotted into each defect category. These differences varied between participants, depending on the number of defect categories present in a given provirus sequence. Pipeline‐specific differences in characterizing the HIV‐1 5′ untranslated region (5′ UTR) led to an overestimation of the number of intact NFL proviral sequences, a finding corroborated in the independent PSD analysis. A comparison of the four published pipelines to ProSeq‐IT found that ProSeq IT was more likely to characterize proviruses as intact.
Conclusions
The choice of pipeline used for HIV‐1 provirus landscape analysis may bias the classification of defective sequences. To improve the comparison of provirus characterizations across research groups, the development of a consensus elimination pipeline should be prioritized. |
doi_str_mv | 10.1002/jia2.25725 |
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HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence defects that may be present. We investigated the effects that different analysis pipelines had on the characterization of HIV‐1 proviral sequences.
Methods
We used single genome amplification to generate near full‐length (NFL) HIV‐1 proviral DNA sequences, defined as amplicons greater than 8000 base pairs in length, isolated from peripheral blood mononuclear cells (PBMC) of treated suppressed participants with HIV‐1. Amplicons underwent direct next‐generation single genome sequencing and were analysed using four HIV‐1 proviral characterization pipelines. Sequences were characterized as intact or defective; defective sequences were assessed for the number and types of defects present. To confirm and extend our findings, 691 proviruses from the Proviral Sequence Database (PSD) were analysed and the ProSeq‐IT tool of the PSD was used to characterize both the participant and PSD proviruses.
Results and discussion
Virus sequences derived from thirteen ART‐treated virologically suppressed participants with HIV were studied. A total of 693 HIV‐1 proviral sequences were generated, 282 of which were NFL. An average of 53 sequences per participant was analysed. We found that proviruses often harbour multiple sequence defect types (mean 2.7, 95% confidence interval [CI] 2.5, 3.0); the elimination order used by each pipeline affected the percentage of proviruses allotted into each defect category. These differences varied between participants, depending on the number of defect categories present in a given provirus sequence. Pipeline‐specific differences in characterizing the HIV‐1 5′ untranslated region (5′ UTR) led to an overestimation of the number of intact NFL proviral sequences, a finding corroborated in the independent PSD analysis. A comparison of the four published pipelines to ProSeq‐IT found that ProSeq IT was more likely to characterize proviruses as intact.
Conclusions
The choice of pipeline used for HIV‐1 provirus landscape analysis may bias the classification of defective sequences. To improve the comparison of provirus characterizations across research groups, the development of a consensus elimination pipeline should be prioritized.</description><identifier>ISSN: 1758-2652</identifier><identifier>EISSN: 1758-2652</identifier><identifier>DOI: 10.1002/jia2.25725</identifier><identifier>PMID: 34235860</identifier><language>eng</language><publisher>Switzerland: John Wiley & Sons, Inc</publisher><subject>Acquired immune deficiency syndrome ; AIDS ; analysis pipeline ; Antiretroviral agents ; Defects ; Development and progression ; DNA sequencing ; DNA, Viral ; Drug therapy ; Gene mutations ; Genetic aspects ; Genomes ; Health aspects ; Highly active antiretroviral therapy ; HIV ; HIV (Viruses) ; HIV infection ; HIV Infections - drug therapy ; HIV-1 - genetics ; HIV‐1 ; Host-virus relationships ; Human immunodeficiency virus ; Humans ; Laboratories ; Leukocytes, Mononuclear ; Measurement ; Methods ; Mutation ; near full‐length genome ; Nucleotide sequencing ; proviral landscape ; provirus characterization ; Proviruses - genetics ; reservoir ; Viremia ; Virus research</subject><ispartof>Journal of the International AIDS Society, 2021-07, Vol.24 (7), p.e25725-n/a</ispartof><rights>2021 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c6795-8a1d29632b65ef7da60aa4bcd260db814d5718074bbc703377c2e1aa1ea076953</cites><orcidid>0000-0002-2681-3216</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2555366436/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2555366436?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,11562,25753,27924,27925,37012,37013,44590,46052,46476,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34235860$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ferreira, Fernanda A</creatorcontrib><creatorcontrib>He, Qianjing</creatorcontrib><creatorcontrib>Banning, Stephanie</creatorcontrib><creatorcontrib>Roberts‐Sano, Olivia</creatorcontrib><creatorcontrib>Wilkins, Olivia</creatorcontrib><creatorcontrib>Kuritzkes, Daniel R.</creatorcontrib><creatorcontrib>Tsibris, Athe</creatorcontrib><title>HIV‐1 proviral landscape characterization varies by pipeline analysis</title><title>Journal of the International AIDS Society</title><addtitle>J Int AIDS Soc</addtitle><description>Introduction
HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence defects that may be present. We investigated the effects that different analysis pipelines had on the characterization of HIV‐1 proviral sequences.
Methods
We used single genome amplification to generate near full‐length (NFL) HIV‐1 proviral DNA sequences, defined as amplicons greater than 8000 base pairs in length, isolated from peripheral blood mononuclear cells (PBMC) of treated suppressed participants with HIV‐1. Amplicons underwent direct next‐generation single genome sequencing and were analysed using four HIV‐1 proviral characterization pipelines. Sequences were characterized as intact or defective; defective sequences were assessed for the number and types of defects present. To confirm and extend our findings, 691 proviruses from the Proviral Sequence Database (PSD) were analysed and the ProSeq‐IT tool of the PSD was used to characterize both the participant and PSD proviruses.
Results and discussion
Virus sequences derived from thirteen ART‐treated virologically suppressed participants with HIV were studied. A total of 693 HIV‐1 proviral sequences were generated, 282 of which were NFL. An average of 53 sequences per participant was analysed. We found that proviruses often harbour multiple sequence defect types (mean 2.7, 95% confidence interval [CI] 2.5, 3.0); the elimination order used by each pipeline affected the percentage of proviruses allotted into each defect category. These differences varied between participants, depending on the number of defect categories present in a given provirus sequence. Pipeline‐specific differences in characterizing the HIV‐1 5′ untranslated region (5′ UTR) led to an overestimation of the number of intact NFL proviral sequences, a finding corroborated in the independent PSD analysis. A comparison of the four published pipelines to ProSeq‐IT found that ProSeq IT was more likely to characterize proviruses as intact.
Conclusions
The choice of pipeline used for HIV‐1 provirus landscape analysis may bias the classification of defective sequences. To improve the comparison of provirus characterizations across research groups, the development of a consensus elimination pipeline should be prioritized.</description><subject>Acquired immune deficiency syndrome</subject><subject>AIDS</subject><subject>analysis pipeline</subject><subject>Antiretroviral agents</subject><subject>Defects</subject><subject>Development and progression</subject><subject>DNA sequencing</subject><subject>DNA, Viral</subject><subject>Drug therapy</subject><subject>Gene mutations</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Health aspects</subject><subject>Highly active antiretroviral therapy</subject><subject>HIV</subject><subject>HIV (Viruses)</subject><subject>HIV infection</subject><subject>HIV Infections - drug therapy</subject><subject>HIV-1 - genetics</subject><subject>HIV‐1</subject><subject>Host-virus relationships</subject><subject>Human immunodeficiency virus</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Leukocytes, Mononuclear</subject><subject>Measurement</subject><subject>Methods</subject><subject>Mutation</subject><subject>near full‐length genome</subject><subject>Nucleotide sequencing</subject><subject>proviral landscape</subject><subject>provirus characterization</subject><subject>Proviruses - genetics</subject><subject>reservoir</subject><subject>Viremia</subject><subject>Virus research</subject><issn>1758-2652</issn><issn>1758-2652</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><recordid>eNqNkt-KEzEUhwdR3LV64wPIgCAitCaZ_Jm5WSiL7lYWvFFvw5nMaZsynYzJtFKvfASf0ScxY9d1KqUsuUhIvnwnh_yS5DklE0oIe7uywCZMKCYeJOdUiXzMpGAPB-uz5EkIK0Iky3nxODnLOMtELsl5cnU9-_Lrx0-att5trYc6raGpgoEWU7MED6ZDb79DZ12TbsFbDGm5S1vbYm0bTKGBehdseJo8mkMd8NntPEo-v3_36fJ6fPPxanY5vRkbqQoxzoFWrJAZK6XAuapAEgBemopJUpU55ZVQNCeKl6VRJMuUMgwpAEUgShYiGyUXe2-7KddYGWy6-GjdersGv9MOrD48aexSL9xW50xyHo2j5PWtwLuvGwydXttgsI5to9sEzQQvZMGk6mu9_A9duY2PDfeUEJmUPJP_qAXUqG0zd7Gu6aV6qhhhijN-mpKKsChkeaTGR6gFNhhbcQ3Obdw-sN6HH_onR_g4Klxbc7TAvS4MK7waXFgi1N0yuHrT5yccmk-CQ-ObPWi8C8Hj_O6zKdF9_nWff_0n_xF-MYzHHfo38BGge-BbfPjuhEp_mE3ZXvob5hcKcA</recordid><startdate>202107</startdate><enddate>202107</enddate><creator>Ferreira, Fernanda A</creator><creator>He, Qianjing</creator><creator>Banning, Stephanie</creator><creator>Roberts‐Sano, Olivia</creator><creator>Wilkins, Olivia</creator><creator>Kuritzkes, Daniel R.</creator><creator>Tsibris, Athe</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>WIN</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-2681-3216</orcidid></search><sort><creationdate>202107</creationdate><title>HIV‐1 proviral landscape characterization varies by pipeline analysis</title><author>Ferreira, Fernanda A ; He, Qianjing ; Banning, Stephanie ; Roberts‐Sano, Olivia ; Wilkins, Olivia ; Kuritzkes, Daniel R. ; Tsibris, Athe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6795-8a1d29632b65ef7da60aa4bcd260db814d5718074bbc703377c2e1aa1ea076953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Acquired immune deficiency syndrome</topic><topic>AIDS</topic><topic>analysis pipeline</topic><topic>Antiretroviral agents</topic><topic>Defects</topic><topic>Development and progression</topic><topic>DNA sequencing</topic><topic>DNA, Viral</topic><topic>Drug therapy</topic><topic>Gene mutations</topic><topic>Genetic aspects</topic><topic>Genomes</topic><topic>Health aspects</topic><topic>Highly active antiretroviral therapy</topic><topic>HIV</topic><topic>HIV (Viruses)</topic><topic>HIV infection</topic><topic>HIV Infections - drug therapy</topic><topic>HIV-1 - genetics</topic><topic>HIV‐1</topic><topic>Host-virus relationships</topic><topic>Human immunodeficiency virus</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Leukocytes, Mononuclear</topic><topic>Measurement</topic><topic>Methods</topic><topic>Mutation</topic><topic>near full‐length genome</topic><topic>Nucleotide sequencing</topic><topic>proviral landscape</topic><topic>provirus characterization</topic><topic>Proviruses - genetics</topic><topic>reservoir</topic><topic>Viremia</topic><topic>Virus research</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ferreira, Fernanda A</creatorcontrib><creatorcontrib>He, Qianjing</creatorcontrib><creatorcontrib>Banning, Stephanie</creatorcontrib><creatorcontrib>Roberts‐Sano, Olivia</creatorcontrib><creatorcontrib>Wilkins, Olivia</creatorcontrib><creatorcontrib>Kuritzkes, Daniel R.</creatorcontrib><creatorcontrib>Tsibris, Athe</creatorcontrib><collection>Open Access: Wiley-Blackwell Open Access Journals</collection><collection>Wiley Free Archive</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of the International AIDS Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ferreira, Fernanda A</au><au>He, Qianjing</au><au>Banning, Stephanie</au><au>Roberts‐Sano, Olivia</au><au>Wilkins, Olivia</au><au>Kuritzkes, Daniel R.</au><au>Tsibris, Athe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>HIV‐1 proviral landscape characterization varies by pipeline analysis</atitle><jtitle>Journal of the International AIDS Society</jtitle><addtitle>J Int AIDS Soc</addtitle><date>2021-07</date><risdate>2021</risdate><volume>24</volume><issue>7</issue><spage>e25725</spage><epage>n/a</epage><pages>e25725-n/a</pages><issn>1758-2652</issn><eissn>1758-2652</eissn><abstract>Introduction
HIV rebounds after cessation of antiretroviral therapy, representing a barrier to cure. To better understand the virus reservoir, analysis pipelines have been developed that categorize proviral sequences as intact or defective, and further determine the precise nature of the sequence defects that may be present. We investigated the effects that different analysis pipelines had on the characterization of HIV‐1 proviral sequences.
Methods
We used single genome amplification to generate near full‐length (NFL) HIV‐1 proviral DNA sequences, defined as amplicons greater than 8000 base pairs in length, isolated from peripheral blood mononuclear cells (PBMC) of treated suppressed participants with HIV‐1. Amplicons underwent direct next‐generation single genome sequencing and were analysed using four HIV‐1 proviral characterization pipelines. Sequences were characterized as intact or defective; defective sequences were assessed for the number and types of defects present. To confirm and extend our findings, 691 proviruses from the Proviral Sequence Database (PSD) were analysed and the ProSeq‐IT tool of the PSD was used to characterize both the participant and PSD proviruses.
Results and discussion
Virus sequences derived from thirteen ART‐treated virologically suppressed participants with HIV were studied. A total of 693 HIV‐1 proviral sequences were generated, 282 of which were NFL. An average of 53 sequences per participant was analysed. We found that proviruses often harbour multiple sequence defect types (mean 2.7, 95% confidence interval [CI] 2.5, 3.0); the elimination order used by each pipeline affected the percentage of proviruses allotted into each defect category. These differences varied between participants, depending on the number of defect categories present in a given provirus sequence. Pipeline‐specific differences in characterizing the HIV‐1 5′ untranslated region (5′ UTR) led to an overestimation of the number of intact NFL proviral sequences, a finding corroborated in the independent PSD analysis. A comparison of the four published pipelines to ProSeq‐IT found that ProSeq IT was more likely to characterize proviruses as intact.
Conclusions
The choice of pipeline used for HIV‐1 provirus landscape analysis may bias the classification of defective sequences. To improve the comparison of provirus characterizations across research groups, the development of a consensus elimination pipeline should be prioritized.</abstract><cop>Switzerland</cop><pub>John Wiley & Sons, Inc</pub><pmid>34235860</pmid><doi>10.1002/jia2.25725</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-2681-3216</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acquired immune deficiency syndrome AIDS analysis pipeline Antiretroviral agents Defects Development and progression DNA sequencing DNA, Viral Drug therapy Gene mutations Genetic aspects Genomes Health aspects Highly active antiretroviral therapy HIV HIV (Viruses) HIV infection HIV Infections - drug therapy HIV-1 - genetics HIV‐1 Host-virus relationships Human immunodeficiency virus Humans Laboratories Leukocytes, Mononuclear Measurement Methods Mutation near full‐length genome Nucleotide sequencing proviral landscape provirus characterization Proviruses - genetics reservoir Viremia Virus research |
title | HIV‐1 proviral landscape characterization varies by pipeline analysis |
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