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Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing

Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparis...

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Autores principales: Lee, Emma R., Parkin, Neil, Jennings, Cheryl, Brumme, Chanson J., Enns, Eric, Casadellà, Maria, Howison, Mark, Coetzer, Mia, Avila-Rios, Santiago, Capina, Rupert, Marinier, Eric, Van Domselaar, Gary, Noguera-Julian, Marc, Kirkby, Don, Knaggs, Jeff, Harrigan, Richard, Quiñones-Mateu, Miguel, Paredes, Roger, Kantor, Rami, Sandstrom, Paul, Ji, Hezhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994664/
https://www.ncbi.nlm.nih.gov/pubmed/32005884
http://dx.doi.org/10.1038/s41598-020-58544-z
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author Lee, Emma R.
Parkin, Neil
Jennings, Cheryl
Brumme, Chanson J.
Enns, Eric
Casadellà, Maria
Howison, Mark
Coetzer, Mia
Avila-Rios, Santiago
Capina, Rupert
Marinier, Eric
Van Domselaar, Gary
Noguera-Julian, Marc
Kirkby, Don
Knaggs, Jeff
Harrigan, Richard
Quiñones-Mateu, Miguel
Paredes, Roger
Kantor, Rami
Sandstrom, Paul
Ji, Hezhao
author_facet Lee, Emma R.
Parkin, Neil
Jennings, Cheryl
Brumme, Chanson J.
Enns, Eric
Casadellà, Maria
Howison, Mark
Coetzer, Mia
Avila-Rios, Santiago
Capina, Rupert
Marinier, Eric
Van Domselaar, Gary
Noguera-Julian, Marc
Kirkby, Don
Knaggs, Jeff
Harrigan, Richard
Quiñones-Mateu, Miguel
Paredes, Roger
Kantor, Rami
Sandstrom, Paul
Ji, Hezhao
author_sort Lee, Emma R.
collection PubMed
description Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HIVDR variants.
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spelling pubmed-69946642020-02-06 Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing Lee, Emma R. Parkin, Neil Jennings, Cheryl Brumme, Chanson J. Enns, Eric Casadellà, Maria Howison, Mark Coetzer, Mia Avila-Rios, Santiago Capina, Rupert Marinier, Eric Van Domselaar, Gary Noguera-Julian, Marc Kirkby, Don Knaggs, Jeff Harrigan, Richard Quiñones-Mateu, Miguel Paredes, Roger Kantor, Rami Sandstrom, Paul Ji, Hezhao Sci Rep Article Next generation sequencing (NGS) is a trending new standard for genotypic HIV-1 drug resistance (HIVDR) testing. Many NGS HIVDR data analysis pipelines have been independently developed, each with variable outputs and data management protocols. Standardization of such analytical methods and comparison of available pipelines are lacking, yet may impact subsequent HIVDR interpretation and other downstream applications. Here we compared the performance of five NGS HIVDR pipelines using proficiency panel samples from NIAID Virology Quality Assurance (VQA) program. Ten VQA panel specimens were genotyped by each of six international laboratories using their own in-house NGS assays. Raw NGS data were then processed using each of the five different pipelines including HyDRA, MiCall, PASeq, Hivmmer and DEEPGEN. All pipelines detected amino acid variants (AAVs) at full range of frequencies (1~100%) and demonstrated good linearity as compared to the reference frequency values. While the sensitivity in detecting low abundance AAVs, with frequencies between 1~20%, is less a concern for all pipelines, their specificity dramatically decreased at AAV frequencies <2%, suggesting that 2% threshold may be a more reliable reporting threshold for ensured specificity in AAV calling and reporting. More variations were observed among the pipelines when low abundance AAVs are concerned, likely due to differences in their NGS read quality control strategies. Findings from this study highlight the need for standardized strategies for NGS HIVDR data analysis, especially for the detection of minority HIVDR variants. Nature Publishing Group UK 2020-01-31 /pmc/articles/PMC6994664/ /pubmed/32005884 http://dx.doi.org/10.1038/s41598-020-58544-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lee, Emma R.
Parkin, Neil
Jennings, Cheryl
Brumme, Chanson J.
Enns, Eric
Casadellà, Maria
Howison, Mark
Coetzer, Mia
Avila-Rios, Santiago
Capina, Rupert
Marinier, Eric
Van Domselaar, Gary
Noguera-Julian, Marc
Kirkby, Don
Knaggs, Jeff
Harrigan, Richard
Quiñones-Mateu, Miguel
Paredes, Roger
Kantor, Rami
Sandstrom, Paul
Ji, Hezhao
Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title_full Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title_fullStr Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title_full_unstemmed Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title_short Performance comparison of next generation sequencing analysis pipelines for HIV-1 drug resistance testing
title_sort performance comparison of next generation sequencing analysis pipelines for hiv-1 drug resistance testing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994664/
https://www.ncbi.nlm.nih.gov/pubmed/32005884
http://dx.doi.org/10.1038/s41598-020-58544-z
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