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Benchmark datasets for SARS-CoV-2 surveillance bioinformatics
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome s...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454940/ https://www.ncbi.nlm.nih.gov/pubmed/36093336 http://dx.doi.org/10.7717/peerj.13821 |
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author | Xiaoli, Lingzi Hagey, Jill V. Park, Daniel J. Gulvik, Christopher A. Young, Erin L. Alikhan, Nabil-Fareed Lawsin, Adrian Hassell, Norman Knipe, Kristen Oakeson, Kelly F. Retchless, Adam C. Shakya, Migun Lo, Chien-Chi Chain, Patrick Page, Andrew J. Metcalf, Benjamin J. Su, Michelle Rowell, Jessica Vidyaprakash, Eshaw Paden, Clinton R. Huang, Andrew D. Roellig, Dawn Patel, Ketan Winglee, Kathryn Weigand, Michael R. Katz, Lee S. |
author_facet | Xiaoli, Lingzi Hagey, Jill V. Park, Daniel J. Gulvik, Christopher A. Young, Erin L. Alikhan, Nabil-Fareed Lawsin, Adrian Hassell, Norman Knipe, Kristen Oakeson, Kelly F. Retchless, Adam C. Shakya, Migun Lo, Chien-Chi Chain, Patrick Page, Andrew J. Metcalf, Benjamin J. Su, Michelle Rowell, Jessica Vidyaprakash, Eshaw Paden, Clinton R. Huang, Andrew D. Roellig, Dawn Patel, Ketan Winglee, Kathryn Weigand, Michael R. Katz, Lee S. |
author_sort | Xiaoli, Lingzi |
collection | PubMed |
description | BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. METHODS: We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. RESULTS: The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. DISCUSSION: The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines. |
format | Online Article Text |
id | pubmed-9454940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94549402022-09-09 Benchmark datasets for SARS-CoV-2 surveillance bioinformatics Xiaoli, Lingzi Hagey, Jill V. Park, Daniel J. Gulvik, Christopher A. Young, Erin L. Alikhan, Nabil-Fareed Lawsin, Adrian Hassell, Norman Knipe, Kristen Oakeson, Kelly F. Retchless, Adam C. Shakya, Migun Lo, Chien-Chi Chain, Patrick Page, Andrew J. Metcalf, Benjamin J. Su, Michelle Rowell, Jessica Vidyaprakash, Eshaw Paden, Clinton R. Huang, Andrew D. Roellig, Dawn Patel, Ketan Winglee, Kathryn Weigand, Michael R. Katz, Lee S. PeerJ Bioinformatics BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. METHODS: We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. RESULTS: The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. DISCUSSION: The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines. PeerJ Inc. 2022-09-05 /pmc/articles/PMC9454940/ /pubmed/36093336 http://dx.doi.org/10.7717/peerj.13821 Text en ©2022 Xiaoli et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Xiaoli, Lingzi Hagey, Jill V. Park, Daniel J. Gulvik, Christopher A. Young, Erin L. Alikhan, Nabil-Fareed Lawsin, Adrian Hassell, Norman Knipe, Kristen Oakeson, Kelly F. Retchless, Adam C. Shakya, Migun Lo, Chien-Chi Chain, Patrick Page, Andrew J. Metcalf, Benjamin J. Su, Michelle Rowell, Jessica Vidyaprakash, Eshaw Paden, Clinton R. Huang, Andrew D. Roellig, Dawn Patel, Ketan Winglee, Kathryn Weigand, Michael R. Katz, Lee S. Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title | Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title_full | Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title_fullStr | Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title_full_unstemmed | Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title_short | Benchmark datasets for SARS-CoV-2 surveillance bioinformatics |
title_sort | benchmark datasets for sars-cov-2 surveillance bioinformatics |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454940/ https://www.ncbi.nlm.nih.gov/pubmed/36093336 http://dx.doi.org/10.7717/peerj.13821 |
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