Metagenomic sequencing to detect respiratory viruses in persons under investigation for COVID-19

Babiker, Ahmed; Bradley, Heath L.; Stittleburg, Victoria; Key, Autum; Kraft, Colleen S.; Waggoner, Jesse J.; Piantadosi, Anne

doi:10.1101/2020.09.09.20178764

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Samples underwent DNAse treatment (ArcticZymes), cDNA synthesis with random primers and Superscript III (Invitrogen), Nextera XT tagmentation (Illumina), and Illumina sequencing ( Babiker et al. 2020a ).…”

Section: Methodsmentioning

confidence: 99%

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

et al. 2022

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In early 2020, as SARS-CoV-2 diagnostic and surveillance responses ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the United States by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state’s first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely-related viruses in multiple U.S. states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks, and point towards a critical need for implementing rapid, broadly-targeted surveillance efforts for future pandemics.

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Section: Methodsmentioning

confidence: 99%

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

et al. 2022

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Section: Sars-cov-2 Genome Sequencingmentioning

confidence: 99%

Unrecognized introductions of SARS-CoV-2 into the state of Georgia shaped the early epidemic

Babiker

Martin

Marvil

et al. 2021

Preprint

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In early 2020, as SARS-CoV-2 diagnostic and surveillance responses ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the U.S. by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single introduction from Asia at least two weeks prior to the states first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely-related viruses in multiple U.S. states by the end of March 2020. Our findings indicate that the early attention directed towards identifying SARS-CoV-2 in returning international travelers may have led to a failure to recognize locally circulating infections for several weeks, and points towards a critical need for rapid and broadly-targeted surveillance efforts in the future.

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“…mNGS can theoretically detect all pathogens in clinical samples and is especially suitable for detecting complex, rare, novel, and atypical infectious diseases ( Ge et al, 2021 ). In particular for some viruses, mNGS might be the only feasible method of detection ( Babiker et al, 2020 ; van Boheemen et al, 2020 ). mNGS can be used for a variety of common clinical microbiology samples, such as cerebrospinal fluid, whole blood, alveolar lavage fluid, pus, and tissue ( Wilson et al, 2014 ; Doan et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing

Jiang,

Yan,

Huang

et al. 2023

Front. Genet.

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Introduction: Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for rapid pathogen identification in clinical practice. However, the parameters used to interpret mNGS data, such as read count, genus rank, and coverage, lack explicit performance evaluation. In this study, the developed indicators as well as novel parameters were assessed for their performance in bacterium detection.Methods: We developed several relevant parameters, including 10M normalized reads, double-discard reads, Genus Rank Ratio, King Genus Rank Ratio, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank. These parameters, together with frequently used read indicators including raw reads, reads per million mapped reads (RPM), transcript per kilobase per million mapped reads (TPM), Genus Rank, and coverage were analyzed for their diagnostic efficiency in bronchoalveolar lavage fluid (BALF), a common source for detecting eight bacterium pathogens: Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Hemophilus influenzae, Stenotrophomonas maltophilia, Pseudomonas aeruginosa, and Aspergillus fumigatus.Results: The results demonstrated that these indicators exhibited good diagnostic efficacy for the eight pathogens. The AUC values of all indicators were almost greater than 0.9, and the corresponding sensitivity and specificity values were almost greater than 0.8, excepted coverage. The negative predictive value of all indicators was greater than 0.9. The results showed that the use of double-discarded reads, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank exhibited better diagnostic efficiency than that of raw reads, RPM, TPM, and in Genus Rank. These parameters can serve as a reference for interpreting mNGS data of BALF. Moreover, precision filters integrating our novel parameters were built to detect the eight bacterium pathogens in BALF samples through machine learning.Summary: In this study, we developed a set of novel parameters for pathogen identification in clinical mNGS based on reads and ranking. These parameters were found to be more effective in diagnosing pathogens than traditional approaches. The findings provide valuable insights for improving the interpretation of mNGS reports in clinical settings, specifically in BALF analysis.

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Metagenomic sequencing to detect respiratory viruses in persons under investigation for COVID-19

Cited by 6 publications

References 12 publications

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic

Unrecognized introductions of SARS-CoV-2 into the state of Georgia shaped the early epidemic

Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing

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