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The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction
Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943356/ https://www.ncbi.nlm.nih.gov/pubmed/35332213 http://dx.doi.org/10.1038/s41598-022-09035-w |
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author | Moshiri, Niema Fisch, Kathleen M. Birmingham, Amanda DeHoff, Peter Yeo, Gene W. Jepsen, Kristen Laurent, Louise C. Knight, Rob |
author_facet | Moshiri, Niema Fisch, Kathleen M. Birmingham, Amanda DeHoff, Peter Yeo, Gene W. Jepsen, Kristen Laurent, Louise C. Knight, Rob |
author_sort | Moshiri, Niema |
collection | PubMed |
description | Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow’s simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts. |
format | Online Article Text |
id | pubmed-8943356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89433562022-03-24 The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction Moshiri, Niema Fisch, Kathleen M. Birmingham, Amanda DeHoff, Peter Yeo, Gene W. Jepsen, Kristen Laurent, Louise C. Knight, Rob Sci Rep Article Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow’s simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts. Nature Publishing Group UK 2022-03-24 /pmc/articles/PMC8943356/ /pubmed/35332213 http://dx.doi.org/10.1038/s41598-022-09035-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Moshiri, Niema Fisch, Kathleen M. Birmingham, Amanda DeHoff, Peter Yeo, Gene W. Jepsen, Kristen Laurent, Louise C. Knight, Rob The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title | The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title_full | The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title_fullStr | The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title_full_unstemmed | The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title_short | The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction |
title_sort | vireflow pipeline enables user friendly large scale viral consensus genome reconstruction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943356/ https://www.ncbi.nlm.nih.gov/pubmed/35332213 http://dx.doi.org/10.1038/s41598-022-09035-w |
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