Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets

With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorit...

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Autores principales: Kille, Bryce, Liu, Yunxi, Sapoval, Nicolae, Nute, Michael, Rauchwerger, Lawrence, Amato, Nancy, Treangen, Todd J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109184/
https://www.ncbi.nlm.nih.gov/pubmed/33972927
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author Kille, Bryce
Liu, Yunxi
Sapoval, Nicolae
Nute, Michael
Rauchwerger, Lawrence
Amato, Nancy
Treangen, Todd J.
author_facet Kille, Bryce
Liu, Yunxi
Sapoval, Nicolae
Nute, Michael
Rauchwerger, Lawrence
Amato, Nancy
Treangen, Todd J.
author_sort Kille, Bryce
collection PubMed
description With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to this, the interface for running in parallel could be simplified, allowing for multithreading as well as distributing jobs to a cluster. In this work we describe some specific contributions to LoFreq that remedy these issues.
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spelling pubmed-81091842021-05-11 Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets Kille, Bryce Liu, Yunxi Sapoval, Nicolae Nute, Michael Rauchwerger, Lawrence Amato, Nancy Treangen, Todd J. ArXiv Article With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to this, the interface for running in parallel could be simplified, allowing for multithreading as well as distributing jobs to a cluster. In this work we describe some specific contributions to LoFreq that remedy these issues. Cornell University 2021-05-07 /pmc/articles/PMC8109184/ /pubmed/33972927 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Kille, Bryce
Liu, Yunxi
Sapoval, Nicolae
Nute, Michael
Rauchwerger, Lawrence
Amato, Nancy
Treangen, Todd J.
Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title_full Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title_fullStr Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title_full_unstemmed Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title_short Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets
title_sort accelerating sars-cov-2 low frequency variant calling on ultra deep sequencing datasets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109184/
https://www.ncbi.nlm.nih.gov/pubmed/33972927
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