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Related Experiment Video

Updated: Aug 29, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Published on: June 30, 2023

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics.

Lingzi Xiaoli¹, Jill V Hagey¹, Daniel J Park²

¹Strain Surveillance and Emerging Variant Team, Centers for Disease Control and Prevention, Atlanta, GA, United States of America.

|September 12, 2022

Summary

This summary is machine-generated.

Standardized SARS-CoV-2 genomic datasets were created to improve sequencing quality and bioinformatics capacity for public health laboratories. These resources aid in accurate outbreak investigation and pandemic management.

Keywords:

Benchmarking COVID-19 Standardization WGS sha256

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Last Updated: Aug 29, 2025

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

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Published on: June 30, 2023

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Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Area of Science:

Genomics
Bioinformatics
Public Health

Background:

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surveillance relies on genome sequencing and bioinformatics tools.
Existing methods for classifying variants of concern (VOC) and interest (VOI) require robust data.
Public health laboratories need high-throughput sequencing and data analysis capabilities.

Purpose of the Study:

To develop standardized SARS-CoV-2 sequence datasets for benchmarking.
To enhance the capacity of public health laboratories in sequencing and bioinformatics.
To support accurate and timely genomic epidemiology and outbreak surveillance.

Main Methods:

Identified and curated six SARS-CoV-2 sequence datasets from publications, public databases, and internal resources.
Developed a method to mine public databases for representative viral genomes.
Enhanced a script for downloading and verifying sequence data.

Main Results:

Created six benchmark datasets focusing on Oxford Nanopore and Illumina sequencing platforms.
Datasets include published data, data mined from public databases, and data representing sequence failures.
All data, including QC values and a data mining script, are publicly available on GitHub.

Conclusions:

The developed datasets aid public health laboratories in building sequencing and bioinformatics capacity.
These resources enable benchmarking of workflows and calibration of quality control thresholds.
Standardized data facilitates the development and validation of new bioinformatics pipelines for pandemic management.