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ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens

The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-...

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Autores principales: Bonsall, David, Ansari, M. Azim, Ip, Camilla, Trebes, Amy, Brown, Anthony, Klenerman, Paul, Buck, David, Piazza, Paolo, Barnes, Eleanor, Bowden, Rory
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821293/
https://www.ncbi.nlm.nih.gov/pubmed/27092241
http://dx.doi.org/10.12688/f1000research.7111.1
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author Bonsall, David
Ansari, M. Azim
Ip, Camilla
Trebes, Amy
Brown, Anthony
Klenerman, Paul
Buck, David
Piazza, Paolo
Barnes, Eleanor
Bowden, Rory
author_facet Bonsall, David
Ansari, M. Azim
Ip, Camilla
Trebes, Amy
Brown, Anthony
Klenerman, Paul
Buck, David
Piazza, Paolo
Barnes, Eleanor
Bowden, Rory
author_sort Bonsall, David
collection PubMed
description The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-virus genomes at up to 20% divergence from a reference sequence and 1,000-fold greater sensitivity than direct sequencing. The extreme genetic diversity of HCV led us to implement an algorithm for the efficient design of panels of oligonucleotide probes to capture any sequence among a defined set of targets without detectable bias. ve-SEQ enables efficient detection and sequencing of any HCV genome, including mixtures and intra-host variants, in a single experiment, with greater tolerance of sequence diversity than standard amplification methods and greater sensitivity than metagenomic sequencing, features that are directly applicable to other pathogens or arbitrary groups of target organisms, allowing the combination of sensitive detection with sequencing in many settings.
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spelling pubmed-48212932016-04-17 ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens Bonsall, David Ansari, M. Azim Ip, Camilla Trebes, Amy Brown, Anthony Klenerman, Paul Buck, David Piazza, Paolo Barnes, Eleanor Bowden, Rory F1000Res Research Article The routine availability of high-depth virus sequence data would allow the sensitive detection of resistance-associated variants that can jeopardize HIV or hepatitis C virus (HCV) treatment. We introduce ve-SEQ, a high-throughput method for sequence-specific enrichment and characterization of whole-virus genomes at up to 20% divergence from a reference sequence and 1,000-fold greater sensitivity than direct sequencing. The extreme genetic diversity of HCV led us to implement an algorithm for the efficient design of panels of oligonucleotide probes to capture any sequence among a defined set of targets without detectable bias. ve-SEQ enables efficient detection and sequencing of any HCV genome, including mixtures and intra-host variants, in a single experiment, with greater tolerance of sequence diversity than standard amplification methods and greater sensitivity than metagenomic sequencing, features that are directly applicable to other pathogens or arbitrary groups of target organisms, allowing the combination of sensitive detection with sequencing in many settings. F1000Research 2015-10-13 /pmc/articles/PMC4821293/ /pubmed/27092241 http://dx.doi.org/10.12688/f1000research.7111.1 Text en Copyright: © 2015 Bonsall D et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bonsall, David
Ansari, M. Azim
Ip, Camilla
Trebes, Amy
Brown, Anthony
Klenerman, Paul
Buck, David
Piazza, Paolo
Barnes, Eleanor
Bowden, Rory
ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title_full ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title_fullStr ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title_full_unstemmed ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title_short ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens
title_sort ve-seq: robust, unbiased enrichment for streamlined detection and whole-genome sequencing of hcv and other highly diverse pathogens
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821293/
https://www.ncbi.nlm.nih.gov/pubmed/27092241
http://dx.doi.org/10.12688/f1000research.7111.1
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