Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology
Abstract
:1. Introduction
2. Current Testing Methods
2.1. Molecular-Based Testing Methods
2.2. Serological and Immunological-Based Testing Methods
2.3. New Developments
3. Next-Generation Sequencing (NGS)
3.1. NGS Fundamentals
3.1.1. NGS Methods
3.1.2. Bioinformatic Processing of NGS Data
3.1.3. Quality Control for Bioinformatics Pipelines
3.2. NGS and the SARS-CoV-2 Pandemic
3.2.1. NGS for Pathogen Detection: Prepandemic
Key Findings: |
1. A large percentage of commonly clinical diseases are due to infections of unknown etiology [30,31]. |
2. NGS has been proven to be capable of identifying infectious microorganisms from various patient sample types [32]. |
3. NGS has been shown to provide clinically practical turnaround times [33]. |
3.2.2. NGS for Detection of SARS-CoV-2
3.2.3. Co-Infection in COVID-19 Patients
Key Findings: |
1. NGS is useful for yielding essential information about a pathogen at the outset of an infectious outbreak [1,4]. |
2. NGS is capable of being implemented as a diagnostic assay for SARS-CoV-2 infection [34,35]. |
3. NGS is capable of accurately identifying co-infection in COVID-19 patients [35]. |
3.2.4. NGS as a Tool for Understanding SARS-CoV-2: Additional Benefits of Adoption
3.2.5. Understanding Physical and Chemical Properties of the Virus
Key findings made possible by NGS technology |
1. The presence, structure, and function of the SARS-CoV-2 spike protein [1,43]. |
2. The presence and ramifications of a furin-like cleavage site on the SARS-CoV-2 spike protein [43,45]. |
3. Mechanisms of viral stability inside human cells [42,46]. |
4. The presence and structure of B-cell and T-cell epitopes on viral proteins [47]. |
3.2.6. SARS-CoV-2 Phylogenetics and Mutational Characteristics
Key findings made possible by NGS technology |
1. Information on the spread of SARS-CoV-2 into and across national borders [47,48,50] and identification of the emergence of distinct viral clades throughout the world [50]. |
2. Mutational rates and characteristics of distinct regions of the SARS-CoV-2 genome [49,50,52]. |
3. Information on important individual mutations that have an outsized impact on the continued spread of the virus including the D614G and P323L mutations [50,51]. |
4. Analysis of intra-host SARS-CoV-2 variants [53]. |
5. Implications of specific human genotypes on susceptibility to developing severe symptoms made possible by human genome sequencing [54]. |
3.3. Challenges Related to NGS Adoption
3.3.1. Contamination
3.3.2. Expertise
3.3.3. Difficulty in Validation
3.3.4. Cost–Benefit Analysis
4. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Parameters | Illumina COVIDSeq Test | Ion AmpliSeq™ SARS-CoV-2 | Oxford Nanopore Technologies |
---|---|---|---|
Sample and Systems | 1536 to 3072 results can be processed on the NovaSeq 6000 system in 12 h using two SP or S4 reagent kits or 384 results in 12 h using the NextSeq 2000 or the NextSeq 500/550/550Dx (in RUO mode) HO reagent kit | 3 samples (Ion 510™ Chip) to 130 samples (Ion 550™ Chip) | 12 to 2304 samples using MinION to PromethION |
Amplicon Size | 400 bp | 125–275 bp | _ |
Limit of Detection | <500 copies/mL | 20 copies/reaction | 10 copies/reaction |
TAT | ~24 h | ~24 h | ~9 h |
Cluster | Mutations |
---|---|
1 | [8782C>T] [28144T>C] |
2 | [14408C>T] |
3 | [3037C>T] [14408C>T] [23403A>G] |
4 | [3037C>T] [14408C>T] [23403A>G] [28881G>A] [28882G>A] [28883G>C] |
5 | [241C>T] [3037C>T] [14408C>T] [23403A>G] [25563G>T] |
US data | |
A | [11083C>Y] |
B | [17747C>T] [17858A>G] [28144T>C] |
C | [241C>T] [3037C>T] [14408C>T] [23403A>G] |
Time to Perform Assay | Limit of Detection(Viral Copies/uL) | Infection Status | Coinfection Identification | Ability to Detect Presence of Variant Strains ** | Ability to Provide Sequencing Data for Scientific Study | |
---|---|---|---|---|---|---|
qPCR | 4–6 h | 0.1–3.16 [13,66] | Active | If organism is actively targeted | Usually | No |
NGS | 12–18 h | 0.125–1 [67,68] | Active | Yes | Yes | Yes |
Serology | Variable | Sensitivity: 93.3–100% [69] | Persistent/Resolved * | If organism is actively targeted | Usually | No |
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John, G.; Sahajpal, N.S.; Mondal, A.K.; Ananth, S.; Williams, C.; Chaubey, A.; Rojiani, A.M.; Kolhe, R. Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology. Curr. Issues Mol. Biol. 2021, 43, 845-867. https://doi.org/10.3390/cimb43020061
John G, Sahajpal NS, Mondal AK, Ananth S, Williams C, Chaubey A, Rojiani AM, Kolhe R. Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology. Current Issues in Molecular Biology. 2021; 43(2):845-867. https://doi.org/10.3390/cimb43020061
Chicago/Turabian StyleJohn, Goldin, Nikhil Shri Sahajpal, Ashis K. Mondal, Sudha Ananth, Colin Williams, Alka Chaubey, Amyn M. Rojiani, and Ravindra Kolhe. 2021. "Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis, Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology" Current Issues in Molecular Biology 43, no. 2: 845-867. https://doi.org/10.3390/cimb43020061