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Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants

OBJECTIVE: Reliable detection of HIV minority resistant variants (MRVs) requires bioinformatics analysis with specific algorithms to obtain good quality alignments. The aim of this study was to analyze ultra-deep sequencing (UDS) data using different analysis pipelines. METHODS: HIV-1 protease, reve...

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Autores principales: Perrier, Marine, Désiré, Nathalie, Storto, Alexandre, Todesco, Eve, Rodriguez, Christophe, Bertine, Mélanie, Le Hingrat, Quentin, Visseaux, Benoit, Calvez, Vincent, Descamps, Diane, Marcelin, Anne-Geneviève, Charpentier, Charlotte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983569/
https://www.ncbi.nlm.nih.gov/pubmed/29856864
http://dx.doi.org/10.1371/journal.pone.0198334
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author Perrier, Marine
Désiré, Nathalie
Storto, Alexandre
Todesco, Eve
Rodriguez, Christophe
Bertine, Mélanie
Le Hingrat, Quentin
Visseaux, Benoit
Calvez, Vincent
Descamps, Diane
Marcelin, Anne-Geneviève
Charpentier, Charlotte
author_facet Perrier, Marine
Désiré, Nathalie
Storto, Alexandre
Todesco, Eve
Rodriguez, Christophe
Bertine, Mélanie
Le Hingrat, Quentin
Visseaux, Benoit
Calvez, Vincent
Descamps, Diane
Marcelin, Anne-Geneviève
Charpentier, Charlotte
author_sort Perrier, Marine
collection PubMed
description OBJECTIVE: Reliable detection of HIV minority resistant variants (MRVs) requires bioinformatics analysis with specific algorithms to obtain good quality alignments. The aim of this study was to analyze ultra-deep sequencing (UDS) data using different analysis pipelines. METHODS: HIV-1 protease, reverse transcriptase (RT) and integrase sequences from antiretroviral-naïve patients were obtained using GS-Junior(®) (Roche) and MiSeq(®) (Illumina) platforms. MRVs were defined as variants harbouring resistance-mutation present at a frequency of 1%–20%. Reads were analyzed using different alignment algorithms: Amplicon Variant Analyzer(®), Geneious(®) compared to SmartGene(®) NGS HIV-1 module. RESULTS: 101 protease and 51 RT MRVs identified in 139 protease and 124 RT sequences generated with a GS-Junior(®) platform were analyzed using AVA(®) and SmartGene(®) software. The correlation coefficients for the MRVs were R(2) = 0.974 for protease and R(2) = 0.972 for RT. Discordances (n = 13 in protease and n = 15 in RT) mainly concerned low-level MRVs (i.e., with frequencies of 1%–2%, n = 18/28) and they were located in homopolymeric regions (n = 10/15). Geneious(®) and SmartGene(®) software were used to analyze 143 protease, 45 RT and 26 integrase MRVs identified in 172 protease, 69 RT, and 72 integrase sequences generated with a MiSeq(®) platform. The correlation coefficients for the MRVs were R(2) = 0.987 for protease, R(2) = 0.995 for RT and R(2) = 0.993 for integrase. Discordances (n = 9 in protease, n = 3 in RT, and n = 3 in integrase) mainly concerned low-level MRVs (n = 13/15). CONCLUSION: We found an excellent correlation between the various UDS analysis pipelines that we tested. However, our results indicate that specific attention should be paid to low-level MRVs, for which the use of two different analysis pipelines and visual inspection of sequences alignments might be beneficial. Thus, our results argue for use of a 2% threshold for MRV detection, rather than the 1% threshold, to minimize misalignments and time-consuming sight reading steps essential to ensure accurate results for MRV frequencies below 2%.
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spelling pubmed-59835692018-06-16 Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants Perrier, Marine Désiré, Nathalie Storto, Alexandre Todesco, Eve Rodriguez, Christophe Bertine, Mélanie Le Hingrat, Quentin Visseaux, Benoit Calvez, Vincent Descamps, Diane Marcelin, Anne-Geneviève Charpentier, Charlotte PLoS One Research Article OBJECTIVE: Reliable detection of HIV minority resistant variants (MRVs) requires bioinformatics analysis with specific algorithms to obtain good quality alignments. The aim of this study was to analyze ultra-deep sequencing (UDS) data using different analysis pipelines. METHODS: HIV-1 protease, reverse transcriptase (RT) and integrase sequences from antiretroviral-naïve patients were obtained using GS-Junior(®) (Roche) and MiSeq(®) (Illumina) platforms. MRVs were defined as variants harbouring resistance-mutation present at a frequency of 1%–20%. Reads were analyzed using different alignment algorithms: Amplicon Variant Analyzer(®), Geneious(®) compared to SmartGene(®) NGS HIV-1 module. RESULTS: 101 protease and 51 RT MRVs identified in 139 protease and 124 RT sequences generated with a GS-Junior(®) platform were analyzed using AVA(®) and SmartGene(®) software. The correlation coefficients for the MRVs were R(2) = 0.974 for protease and R(2) = 0.972 for RT. Discordances (n = 13 in protease and n = 15 in RT) mainly concerned low-level MRVs (i.e., with frequencies of 1%–2%, n = 18/28) and they were located in homopolymeric regions (n = 10/15). Geneious(®) and SmartGene(®) software were used to analyze 143 protease, 45 RT and 26 integrase MRVs identified in 172 protease, 69 RT, and 72 integrase sequences generated with a MiSeq(®) platform. The correlation coefficients for the MRVs were R(2) = 0.987 for protease, R(2) = 0.995 for RT and R(2) = 0.993 for integrase. Discordances (n = 9 in protease, n = 3 in RT, and n = 3 in integrase) mainly concerned low-level MRVs (n = 13/15). CONCLUSION: We found an excellent correlation between the various UDS analysis pipelines that we tested. However, our results indicate that specific attention should be paid to low-level MRVs, for which the use of two different analysis pipelines and visual inspection of sequences alignments might be beneficial. Thus, our results argue for use of a 2% threshold for MRV detection, rather than the 1% threshold, to minimize misalignments and time-consuming sight reading steps essential to ensure accurate results for MRV frequencies below 2%. Public Library of Science 2018-06-01 /pmc/articles/PMC5983569/ /pubmed/29856864 http://dx.doi.org/10.1371/journal.pone.0198334 Text en © 2018 Perrier et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Perrier, Marine
Désiré, Nathalie
Storto, Alexandre
Todesco, Eve
Rodriguez, Christophe
Bertine, Mélanie
Le Hingrat, Quentin
Visseaux, Benoit
Calvez, Vincent
Descamps, Diane
Marcelin, Anne-Geneviève
Charpentier, Charlotte
Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title_full Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title_fullStr Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title_full_unstemmed Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title_short Evaluation of different analysis pipelines for the detection of HIV-1 minority resistant variants
title_sort evaluation of different analysis pipelines for the detection of hiv-1 minority resistant variants
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983569/
https://www.ncbi.nlm.nih.gov/pubmed/29856864
http://dx.doi.org/10.1371/journal.pone.0198334
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