Article: A one-step, one-tube real-time RT-PCR based assay with an automated analysis for detection of SARS-CoV-2
Heliyon. 2020 July, v. 6, no. 7
2020
Abstract: Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically ...
| Abstract | Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically analyse the data. Our assay could detect to a limit of 15 copies of SARS-CoV-2 transcripts—based on experiments performed by spiking total human RNA with in vitro synthesized viral transcripts. The assay was evaluated by performing 184 validations for the SARS-CoV-2 Nucleocapsid gene and human RNase P as an internal control reference gene with dilutions ranging from 1-100 ng for human RNA on a cohort of 26 clinical samples. 5 of 26 patients were confirmed to be infected with SARS-CoV-2, while 21 tested negative, consistent with the standards. The accuracy of the assay was found to be 100% sensitive and 100% specific based on the 26 clinical samples that need to be further verified using a large number of clinical samples. In summary, we present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories. |
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| Keywords | COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; automation ; early diagnosis ; genes ; humans ; nucleocapsid ; quantitative polymerase chain reaction ; ribonucleases ; user interface |
| Language | English |
| Dates of publication | 2020-07 |
| Publishing place | Elsevier Ltd |
| Document type | Article |
| Note | NAL-AP-2-clean |
| ZDB-ID | 2835763-2 |
| ISSN | 2405-8440 |
| ISSN | 2405-8440 |
| DOI | 10.1016/j.heliyon.2020.e04405 |
| Database | NAL-Catalogue (AGRICOLA) |
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