Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT–qPCR primer–probe sets
The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription–PCR (RT–qPCR) assays are being used by cl...
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| Published in | Nature microbiology Vol. 5; no. 10; pp. 1299 - 1305 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.10.2020
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription–PCR (RT–qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer–probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT–qPCR analytical efficiency and sensitivity, we show that all primer–probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer–probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes.
This is a comparative analysis of the performance of the primer–probe sets from four open-source molecular diagnostic assays for SARS-CoV-2 recommended by the World Health Organization. |
|---|---|
| AbstractList | The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription–PCR (RT–qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer–probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT–qPCR analytical efficiency and sensitivity, we show that all primer–probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer–probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes.
This is a comparative analysis of the performance of the primer–probe sets from four open-source molecular diagnostic assays for SARS-CoV-2 recommended by the World Health Organization. The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription-PCR (RT-qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer-probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT-qPCR analytical efficiency and sensitivity, we show that all primer-probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer-probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes. The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription-PCR (RT-qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer-probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT-qPCR analytical efficiency and sensitivity, we show that all primer-probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer-probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes.The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription-PCR (RT-qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer-probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT-qPCR analytical efficiency and sensitivity, we show that all primer-probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer-probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes. The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse transcription–PCR (RT–qPCR) assays are being used by clinical, research and public health laboratories. However, it is currently unclear whether results from different tests are comparable. Our goal was to make independent evaluations of primer–probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT–qPCR analytical efficiency and sensitivity, we show that all primer–probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer–probe set which has low sensitivity, probably due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used, to ease comparability between outcomes.This is a comparative analysis of the performance of the primer–probe sets from four open-source molecular diagnostic assays for SARS-CoV-2 recommended by the World Health Organization. The recent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for accurate and rapid diagnostic assays to prompt clinical and public health interventions. Currently, several quantitative reverse-transcription polymerase chain reaction (RT-qPCR) assays are being used by clinical, research, and public health laboratories. However, it is currently unclear if results from different tests are comparable. Our goal was to make independent evaluations of primer-probe sets used in four common SARS-CoV-2 diagnostic assays. From our comparisons of RT-qPCR analytical efficiency and sensitivity, we show that all primer-probe sets can be used to detect SARS-CoV-2 at 500 viral RNA copies per reaction. The exception for this is the RdRp-SARSr (Charité) confirmatory primer-probe set which has low sensitivity, likely due to a mismatch to circulating SARS-CoV-2 in the reverse primer. We did not find evidence for background amplification with pre-COVID-19 samples or recent SARS-CoV-2 evolution decreasing sensitivity. Our recommendation for SARS-CoV-2 diagnostic testing is to select an assay with high sensitivity and that is regionally used to ease comparability between outcomes. |
| Author | Ott, Isabel M. Kalinich, Chaney C. Grubaugh, Nathan D. Oh, Ji Eun Taura, Manabu Wong, Patrick Song, Eric Moore, Adam J. Vogels, Chantal B. F. Weizman, Orr-El Casanovas-Massana, Arnau Moriyama, Miyu Park, Annsea Lapidus, Sarah Farhadian, Shelli Klein, Jonathan Kim, Daniel J. White, Elizabeth B. Odio, Camila Wyllie, Anne L. Cheemarla, Nagarjuna R. Lu, Peiwen Earnest, Rebecca Geng, Bertie Brito, Anderson F. Bermejo, Santos Lu-Culligan, Alice Mao, Tianyang Venkataraman, Arvind Iwasaki, Akiko Ko, Albert I. Jiang, Xiaodong Yang, Yexin Takahashi, Takehiro Silva, Julio Kudo, Eriko Landry, Marie L. Dela Cruz, Charles S. Catherine Muenker, M. Tokuyama, Maria Petrone, Mary E. Vijayakumar, Pavithra Fournier, John Foxman, Ellen F. Fauver, Joseph R. |
| AuthorAffiliation | 2 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA 5 Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, New Haven, CT, 06510, USA 6 Department of Medicine, Northeast Medical Group, Yale-New Haven Health, New Haven, CT 06510, USA 3 Department of Immunobiology, Yale University School of Medicine, New Haven, CT, 06510, USA 10 Clinical Virology Laboratory, Yale-New Haven Hospital, New Haven, CT, 06510, USA 8 Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA 7 Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, 06510, USA 1 Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA 4 Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA 9 Howard Hughes Medical Institut |
| AuthorAffiliation_xml | – name: 2 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA – name: 8 Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA – name: 1 Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA – name: 6 Department of Medicine, Northeast Medical Group, Yale-New Haven Health, New Haven, CT 06510, USA – name: 7 Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, 06510, USA – name: 5 Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine, New Haven, CT, 06510, USA – name: 9 Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA – name: 10 Clinical Virology Laboratory, Yale-New Haven Hospital, New Haven, CT, 06510, USA – name: 3 Department of Immunobiology, Yale University School of Medicine, New Haven, CT, 06510, USA – name: 4 Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA |
| Author_xml | – sequence: 1 givenname: Chantal B. F. orcidid: 0000-0003-0027-6480 surname: Vogels fullname: Vogels, Chantal B. F. email: chantal.vogels@yale.edu organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 2 givenname: Anderson F. orcidid: 0000-0001-5785-7222 surname: Brito fullname: Brito, Anderson F. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 3 givenname: Anne L. surname: Wyllie fullname: Wyllie, Anne L. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 4 givenname: Joseph R. orcidid: 0000-0001-8099-2991 surname: Fauver fullname: Fauver, Joseph R. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 5 givenname: Isabel M. surname: Ott fullname: Ott, Isabel M. organization: Department of Ecology and Evolutionary Biology, Yale University – sequence: 6 givenname: Chaney C. orcidid: 0000-0001-9675-0065 surname: Kalinich fullname: Kalinich, Chaney C. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 7 givenname: Mary E. surname: Petrone fullname: Petrone, Mary E. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 8 givenname: Arnau orcidid: 0000-0002-3301-6143 surname: Casanovas-Massana fullname: Casanovas-Massana, Arnau organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 9 givenname: M. surname: Catherine Muenker fullname: Catherine Muenker, M. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 10 givenname: Adam J. surname: Moore fullname: Moore, Adam J. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 11 givenname: Jonathan surname: Klein fullname: Klein, Jonathan organization: Department of Immunobiology, Yale University School of Medicine – sequence: 12 givenname: Peiwen orcidid: 0000-0001-6118-872X surname: Lu fullname: Lu, Peiwen organization: Department of Immunobiology, Yale University School of Medicine – sequence: 13 givenname: Alice orcidid: 0000-0003-0097-0923 surname: Lu-Culligan fullname: Lu-Culligan, Alice organization: Department of Immunobiology, Yale University School of Medicine – sequence: 14 givenname: Xiaodong surname: Jiang fullname: Jiang, Xiaodong organization: Department of Immunobiology, Yale University School of Medicine – sequence: 15 givenname: Daniel J. surname: Kim fullname: Kim, Daniel J. organization: Department of Immunobiology, Yale University School of Medicine – sequence: 16 givenname: Eriko surname: Kudo fullname: Kudo, Eriko organization: Department of Immunobiology, Yale University School of Medicine – sequence: 17 givenname: Tianyang orcidid: 0000-0001-9251-8592 surname: Mao fullname: Mao, Tianyang organization: Department of Immunobiology, Yale University School of Medicine – sequence: 18 givenname: Miyu surname: Moriyama fullname: Moriyama, Miyu organization: Department of Immunobiology, Yale University School of Medicine – sequence: 19 givenname: Ji Eun surname: Oh fullname: Oh, Ji Eun organization: Department of Immunobiology, Yale University School of Medicine – sequence: 20 givenname: Annsea surname: Park fullname: Park, Annsea organization: Department of Immunobiology, Yale University School of Medicine – sequence: 21 givenname: Julio surname: Silva fullname: Silva, Julio organization: Department of Immunobiology, Yale University School of Medicine – sequence: 22 givenname: Eric orcidid: 0000-0001-5448-5865 surname: Song fullname: Song, Eric organization: Department of Immunobiology, Yale University School of Medicine – sequence: 23 givenname: Takehiro surname: Takahashi fullname: Takahashi, Takehiro organization: Department of Immunobiology, Yale University School of Medicine – sequence: 24 givenname: Manabu surname: Taura fullname: Taura, Manabu organization: Department of Immunobiology, Yale University School of Medicine – sequence: 25 givenname: Maria surname: Tokuyama fullname: Tokuyama, Maria organization: Department of Immunobiology, Yale University School of Medicine – sequence: 26 givenname: Arvind surname: Venkataraman fullname: Venkataraman, Arvind organization: Department of Immunobiology, Yale University School of Medicine – sequence: 27 givenname: Orr-El surname: Weizman fullname: Weizman, Orr-El organization: Department of Immunobiology, Yale University School of Medicine – sequence: 28 givenname: Patrick surname: Wong fullname: Wong, Patrick organization: Department of Immunobiology, Yale University School of Medicine – sequence: 29 givenname: Yexin surname: Yang fullname: Yang, Yexin organization: Department of Immunobiology, Yale University School of Medicine – sequence: 30 givenname: Nagarjuna R. orcidid: 0000-0002-8860-2457 surname: Cheemarla fullname: Cheemarla, Nagarjuna R. organization: Department of Laboratory Medicine, Yale University School of Medicine – sequence: 31 givenname: Elizabeth B. orcidid: 0000-0002-9242-3507 surname: White fullname: White, Elizabeth B. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 32 givenname: Sarah surname: Lapidus fullname: Lapidus, Sarah organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 33 givenname: Rebecca surname: Earnest fullname: Earnest, Rebecca organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 34 givenname: Bertie orcidid: 0000-0001-9764-7043 surname: Geng fullname: Geng, Bertie organization: Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine – sequence: 35 givenname: Pavithra surname: Vijayakumar fullname: Vijayakumar, Pavithra organization: Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University School of Medicine – sequence: 36 givenname: Camila surname: Odio fullname: Odio, Camila organization: Department of Medicine, Northeast Medical Group, Yale-New Haven Health – sequence: 37 givenname: John surname: Fournier fullname: Fournier, John organization: Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine – sequence: 38 givenname: Santos surname: Bermejo fullname: Bermejo, Santos organization: Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine – sequence: 39 givenname: Shelli surname: Farhadian fullname: Farhadian, Shelli organization: Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine – sequence: 40 givenname: Charles S. surname: Dela Cruz fullname: Dela Cruz, Charles S. organization: Department of Internal Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine – sequence: 41 givenname: Akiko orcidid: 0000-0002-7824-9856 surname: Iwasaki fullname: Iwasaki, Akiko organization: Department of Immunobiology, Yale University School of Medicine, Howard Hughes Medical Institute – sequence: 42 givenname: Albert I. surname: Ko fullname: Ko, Albert I. organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health – sequence: 43 givenname: Marie L. surname: Landry fullname: Landry, Marie L. organization: Department of Laboratory Medicine, Yale University School of Medicine, Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine, Clinical Virology Laboratory, Yale-New Haven Hospital – sequence: 44 givenname: Ellen F. orcidid: 0000-0001-9767-9942 surname: Foxman fullname: Foxman, Ellen F. organization: Department of Immunobiology, Yale University School of Medicine, Department of Laboratory Medicine, Yale University School of Medicine – sequence: 45 givenname: Nathan D. orcidid: 0000-0003-2031-1933 surname: Grubaugh fullname: Grubaugh, Nathan D. email: nathan.grubaugh@yale.edu organization: Department of Epidemiology of Microbial Diseases, Yale School of Public Health |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32651556$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature Limited 2020 The Author(s), under exclusive licence to Springer Nature Limited 2020. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3 CBFV, AFB, JRF, and NDG designed the study, NRC and EFF collected pre-COVID-19 nasopharyngeal swabs, CBFV, ALW, IMO, CCK, MEP, AC-M, MCM, AJM, JK, PL, AL-C, XJ, DJK, EEK, TM, MM, JEO, AP, JS, ES, TT, MT, MT, AV, O-EW, PW, YY, EBW, SL, RE, BG, PV, CO, JF, SB, SF, CSDC, AI, AIK, MLL, and NDG contributed to collection of clinical data, CBFV performed experiments, CBFV, AFB, and NDG analyzed the data and wrote the first draft. All authors read and approved the manuscript. Contributions |
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| References_xml | – reference: SvecDTichopadANovosadovaVPfafflMWKubistaMHow good is a PCR efficiency estimate? Recommendations for precise and robust qPCR efficiency assessmentsBiomol. Detect. Quantif.201539161:CAS:528:DC%2BC28Xht1Shu73E10.1016/j.bdq.2015.01.005270770294822216 – reference: Corman, V. M. et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT–PCR. Euro Surveill. 25, 2000045 (2020). – reference: Vogels, C. B. F., Fauver, J. R., Ott, I. & Grubaugh, N. D. Generation of SARS-COV-2 RNA transcript standards for qRT–PCR detection assay. protocols.iohttps://doi.org/10.17504/protocols.io.bdv6i69e (2020). – reference: GinzingerDGGene quantification using real-time quantitative PCR: an emerging technology hits the mainstreamExp. Hematol.2002305035121:CAS:528:DC%2BD38XksFOksb8%3D10.1016/S0301-472X(02)00806-812063017 – reference: The Nextstrain team. Genomic epidemiology of novel coronavirus—global subsampling (2020); https://nextstrain.org/ncov?c=gt-ORF14_50 – reference: BruDMartin-LaurentFPhilippotLQuantification of the detrimental effect of a single primer–template mismatch by real-time PCR using the 16S rRNA gene as an exampleAppl. Environ. Microbiol.200874166016631:CAS:528:DC%2BD1cXjt1aku70%3D10.1128/AEM.02403-07181924132258636 – reference: Gorbalenya, A. E. et al. Severe acute respiratory syndrome-related coronavirus: the species and its viruses – a statement of the Coronavirus Study Group. Preprint at https://doi.org/10.1101/2020.02.07.937862 (2020). – reference: ZhouPA pneumonia outbreak associated with a new coronavirus of probable bat originNature20205792702731:CAS:528:DC%2BB3cXksFKlsLg%3D320155077095418 – reference: Zulkower, V. & Rosser, S. DNA Features Viewer, a sequence annotations formatting and plotting library for Python. Preprint at https://doi.org/10.1101/2020.01.09.900589 (2020). – reference: BroedersSGuidelines for validation of qualitative real-time PCR methodsTrends Food Sci. Technol.2014371151261:CAS:528:DC%2BC2cXmtFaksLc%3D10.1016/j.tifs.2014.03.008 – reference: Chu, D. K. W. et al. Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia. Clin. Chem. 66, 549–555 (2020). – reference: Centers for Disease Control and Prevention. Research use only 2019—novel coronavirus (2019-nCoV) real-time RT–PCR primers and probes (2020); https://www.cdc.gov/coronavirus/2019-ncov/lab/rt-pcr-panel-primer-probes.html – reference: Harcourt, J. et al. Severe acute respiratory syndrome coronavirus 2 from patient with 2019 novel coronavirus disease, United States. Emerg. Infect. Dis. 26, 1266–1273 (2020). – reference: WuFA new coronavirus associated with human respiratory disease in ChinaNature20205792652691:CAS:528:DC%2BB3cXksFKlsLc%3D10.1038/s41586-020-2008-3320155087094943 – reference: Ott, I. M., Vogels, C. B. F., Grubaugh, N. D., & Wyllie, A. L. Saliva collection and RNA extraction for SARS-CoV-2 detection. protocols.iohttps://doi.org/10.17504/protocols.io.bh6mj9c6 (2020). – reference: Genomeweb. CDC revises SARS-CoV-2 assay protocol; surveillance testing on track to start next week. 360Dxhttps://www.360dx.com/pcr/cdc-revises-sars-cov-2-assay-protocol-surveillance-testing-track-start-next-week (2020). – reference: NallaAKComparative performance of SARS-CoV-2 detection assays using seven different primer/probe sets and one assay kitJ. Clin. Microbiol.202058e00557-2010.1128/JCM.00557-20322691007269385 – reference: National Institute for Viral Disease Control and Prevention. Specific primers and probes for detection of 2019 novel coronavirus (2020); http://ivdc.chinacdc.cn/kyjz/202001/t20200121_211337.html – volume: 579 start-page: 265 year: 2020 ident: 761_CR2 publication-title: Nature doi: 10.1038/s41586-020-2008-3 – ident: 761_CR4 doi: 10.1093/clinchem/hvaa029 – volume: 37 start-page: 115 year: 2014 ident: 761_CR16 publication-title: Trends Food Sci. Technol. doi: 10.1016/j.tifs.2014.03.008 – ident: 761_CR15 doi: 10.1101/2020.01.09.900589 – volume: 74 start-page: 1660 year: 2008 ident: 761_CR13 publication-title: Appl. Environ. Microbiol. doi: 10.1128/AEM.02403-07 – volume: 58 start-page: e00557-20 year: 2020 ident: 761_CR14 publication-title: J. Clin. Microbiol. doi: 10.1128/JCM.00557-20 – ident: 761_CR1 doi: 10.1101/2020.02.07.937862 – ident: 761_CR6 – ident: 761_CR7 – ident: 761_CR10 doi: 10.17504/protocols.io.bdv6i69e – volume: 3 start-page: 9 year: 2015 ident: 761_CR9 publication-title: Biomol. Detect. Quantif. doi: 10.1016/j.bdq.2015.01.005 – ident: 761_CR5 doi: 10.2807/1560-7917.ES.2020.25.3.2000045 – ident: 761_CR8 doi: 10.3201/eid2606.200516 – volume: 579 start-page: 270 year: 2020 ident: 761_CR3 publication-title: Nature doi: 10.1038/s41586-020-2012-7 – ident: 761_CR11 – volume: 30 start-page: 503 year: 2002 ident: 761_CR17 publication-title: Exp. Hematol. doi: 10.1016/S0301-472X(02)00806-8 – ident: 761_CR12 – ident: 761_CR18 doi: 10.17504/protocols.io.bh6mj9c6 |
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| SubjectTerms | 45/77 631/326/596/4130 631/337 Analysis Betacoronavirus - genetics Betacoronavirus - isolation & purification Biomedical and Life Sciences Clinical Laboratory Techniques - methods Clinical Laboratory Techniques - statistics & numerical data Comparative analysis Coronavirus Infections - diagnosis Coronavirus Infections - epidemiology Coronavirus Infections - virology Coronaviruses COVID-19 COVID-19 Testing Genetic Variation Genome, Viral Humans Infectious Diseases Life Sciences Medical Microbiology Microbiology Molecular Probe Techniques - statistics & numerical data Pandemics Parasitology Pneumonia, Viral - diagnosis Pneumonia, Viral - epidemiology Pneumonia, Viral - virology Public health Reverse Transcriptase Polymerase Chain Reaction - methods Reverse Transcriptase Polymerase Chain Reaction - statistics & numerical data Reverse transcription Ribonucleic acid RNA RNA - genetics RNA Probes - genetics RNA, Viral - analysis RNA, Viral - genetics SARS-CoV-2 Sensitivity and Specificity Severe acute respiratory syndrome coronavirus 2 Virology |
| Title | Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT–qPCR primer–probe sets |
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