Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data

A key task of genomic surveillance of infectious viral diseases lies in the early detection of dangerous variants. Unexpected help to this end is provided by the analysis of deep sequencing data of viral samples, which are typically discarded after creating consensus sequences. Such analysis allows...

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Detalles Bibliográficos
Autores: Ramazzotti, Daniele, Maspero, Davide, Angaroni, Fabrizio, Spinelli, Silvia, Antoniotti, Marco, Piazza, Rocco, Graudenzi, Alex
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/54647
Acceso en línea:http://hdl.handle.net/10230/54647
http://dx.doi.org/10.1016/j.isci.2022.104487
Access Level:acceso abierto
Palabra clave:Bioinformatics
Genomic analysis
Microbiology
Virology
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spelling Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing dataRamazzotti, DanieleMaspero, DavideAngaroni, FabrizioSpinelli, SilviaAntoniotti, MarcoPiazza, RoccoGraudenzi, AlexBioinformaticsGenomic analysisMicrobiologyVirologyA key task of genomic surveillance of infectious viral diseases lies in the early detection of dangerous variants. Unexpected help to this end is provided by the analysis of deep sequencing data of viral samples, which are typically discarded after creating consensus sequences. Such analysis allows one to detect intra-host low-frequency mutations, which are a footprint of mutational processes underlying the origination of new variants. Their timely identification may improve public-health decision-making with respect to traditional approaches exploiting consensus sequences. We present the analysis of 220,788 high-quality deep sequencing SARS-CoV-2 samples, showing that many spike and nucleocapsid mutations of interest associated to the most circulating variants, including Beta, Delta, and Omicron, might have been intercepted several months in advance. Furthermore, we show that a refined genomic surveillance system leveraging deep sequencing data might allow one to pinpoint emerging mutation patterns, providing an automated data-driven support to virologists and epidemiologists.Elsevier202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/54647http://dx.doi.org/10.1016/j.isci.2022.104487reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésiScience. 2022 Jun 17;25(6):104487© 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/546472026-06-12T07:21:37Z
dc.title.none.fl_str_mv Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
title Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
spellingShingle Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
Ramazzotti, Daniele
Bioinformatics
Genomic analysis
Microbiology
Virology
title_short Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
title_full Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
title_fullStr Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
title_full_unstemmed Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
title_sort Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data
dc.creator.none.fl_str_mv Ramazzotti, Daniele
Maspero, Davide
Angaroni, Fabrizio
Spinelli, Silvia
Antoniotti, Marco
Piazza, Rocco
Graudenzi, Alex
author Ramazzotti, Daniele
author_facet Ramazzotti, Daniele
Maspero, Davide
Angaroni, Fabrizio
Spinelli, Silvia
Antoniotti, Marco
Piazza, Rocco
Graudenzi, Alex
author_role author
author2 Maspero, Davide
Angaroni, Fabrizio
Spinelli, Silvia
Antoniotti, Marco
Piazza, Rocco
Graudenzi, Alex
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Bioinformatics
Genomic analysis
Microbiology
Virology
topic Bioinformatics
Genomic analysis
Microbiology
Virology
description A key task of genomic surveillance of infectious viral diseases lies in the early detection of dangerous variants. Unexpected help to this end is provided by the analysis of deep sequencing data of viral samples, which are typically discarded after creating consensus sequences. Such analysis allows one to detect intra-host low-frequency mutations, which are a footprint of mutational processes underlying the origination of new variants. Their timely identification may improve public-health decision-making with respect to traditional approaches exploiting consensus sequences. We present the analysis of 220,788 high-quality deep sequencing SARS-CoV-2 samples, showing that many spike and nucleocapsid mutations of interest associated to the most circulating variants, including Beta, Delta, and Omicron, might have been intercepted several months in advance. Furthermore, we show that a refined genomic surveillance system leveraging deep sequencing data might allow one to pinpoint emerging mutation patterns, providing an automated data-driven support to virologists and epidemiologists.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/54647
http://dx.doi.org/10.1016/j.isci.2022.104487
url http://hdl.handle.net/10230/54647
http://dx.doi.org/10.1016/j.isci.2022.104487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv iScience. 2022 Jun 17;25(6):104487
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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