Multiplex Fragment Analysis for Flexible Detection of All SARS-CoV-2 Variants of Concern Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.1093/clinchem/hvac081
Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge, and effective tracking requires rapid return of results. Surveillance of variants is typically performed by whole genome sequencing (WGS), which can be financially prohibitive and requires specialized equipment and bioinformatic expertise. Genotyping approaches are rapid methods for monitoring SARS-CoV-2 variants but require continuous adaptation. Fragment analysis may represent an approach for improved SARS-CoV-2 variant detection. Methods A multiplex fragment analysis approach (CoVarScan) was validated using PCR targeting variants by size and fluorescent color. Eight SARS-CoV-2 mutational hot spots in variants of concern (VOCs) were targeted. Three primer pairs (recurrently deleted region [RDR] 1, RDR2, and RDR3–4) flank RDRs in the S-gene. Three allele-specific primers target recurrent spike receptor binding domain mutants. Lastly, 2 primer pairs target recurrent deletions or insertions in ORF1A and ORF8. Fragments were resolved and analyzed by capillary electrophoresis (ABI 3730XL), and mutational signatures were compared to WGS results. Results We validated CoVarScan using 3544 clinical respiratory specimens. The assay exhibited 96% sensitivity and 99% specificity compared to WGS. The limit of detection for the core targets (RDR1, RDR2, and ORF1A) was 5 copies/reaction. Variants were identified in 95% of samples with cycle threshold (CT) <30 and 75% of samples with a CT 34 to 35. Assay design was frozen April 2021, but all subsequent VOCs have been detected including Delta (n = 2820), Mu, (n = 6), Lambda (n = 6), and Omicron (n = 309). Genotyping results are available in as little as 4 h. Conclusions Multiplex fragment analysis is adaptable and rapid and has similar accuracy to WGS to classify SARS-CoV-2 variants.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/clinchem/hvac081
- https://academic.oup.com/clinchem/article-pdf/68/8/1042/45109620/hvac081.pdf
- OA Status
- bronze
- Cited By
- 19
- References
- 30
- Related Works
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- OpenAlex ID
- https://openalex.org/W4225669826
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225669826Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/clinchem/hvac081Digital Object Identifier
- Title
-
Multiplex Fragment Analysis for Flexible Detection of All SARS-CoV-2 Variants of ConcernWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-28Full publication date if available
- Authors
-
Andrew E. Clark, Zhaohui Wang, Emily Ostman, Hui Zheng, Huiyu Yao, Brandi L. Cantarel, Mohammed Kanchwala, Chao Xing, Li Chen, Pei Irwin, Yan Xu, Dwight Oliver, Francesca M Lee, Jeffrey Gagan, Laura Filkins, Alagarraju Muthukumar, Jason Y. Park, Ravi Sarode, Jeffrey A. SoRelleList of authors in order
- Landing page
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https://doi.org/10.1093/clinchem/hvac081Publisher landing page
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https://academic.oup.com/clinchem/article-pdf/68/8/1042/45109620/hvac081.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/clinchem/article-pdf/68/8/1042/45109620/hvac081.pdfDirect OA link when available
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Genotyping, Multiplex, Primer (cosmetics), Molecular biology, Biology, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Multiplex polymerase chain reaction, Genotype, Capillary electrophoresis, Genetics, Gene, Virology, Computational biology, Coronavirus disease 2019 (COVID-19), Polymerase chain reaction, Chemistry, Medicine, Pathology, Organic chemistry, Infectious disease (medical specialty), DiseaseTop concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
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2025: 2, 2024: 4, 2023: 4, 2022: 9Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-04-28 |
| publication_year | 2022 |
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