Abstract

Accurate designing of polymerase chain reaction (PCR) primers targeting conserved segments in viral genomes is desirable for preventing false-negative results and decreasing the need for standardization across different PCR protocols. In this work, we designed and described a set of primers and probes targeting conserved regions identified from a multiple sequence alignment of 2341 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genomes from the Global Initiative on Sharing All Influenza Data (GISAID). We subsequently validated those primers and probes in 211,833 SARS-CoV-2 whole-genome sequences. We obtained nine systems (forward primer + reverse primer + probe) that potentially anneal to highly conserved regions of the virus genome from these analyses. In silico predictions also demonstrated that those primers do not bind to nonspecific targets for human, bacterial, fungal, apicomplexan, and other Betacoronaviruses and less pathogenic sub-strains of coronavirus. The availability of these primer and probe sequences will make it possible to validate more efficient protocols for identifying SARS-CoV-2.

Details

Title
Design and in silico validation of polymerase chain reaction primers to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Author
Davi Maria Júlia P 1 ; Jeronimo Selma M B 2 ; Lima João P M S 3 ; Lanza Daniel C F 1 

 Federal University of Rio Grande do Norte, Applied Molecular Biology Lab - LAPLIC, Department of Biochemistry, Biosciences Center, Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X); Federal University of Rio Grande do Norte, Programa de Pós-Graduação Em Bioinformática (PPg-Bioinfo), Digital Metropolis Institute (IMD), Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X) 
 Federal University of Rio Grande do Norte, Institute of Tropical Medicine of Rio Grande Do Norte (IMT), Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X) 
 Federal University of Rio Grande do Norte, Programa de Pós-Graduação Em Bioinformática (PPg-Bioinfo), Digital Metropolis Institute (IMD), Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X); Federal University of Rio Grande do Norte, Institute of Tropical Medicine of Rio Grande Do Norte (IMT), Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X); Federal University of Rio Grande do Norte, Laboratory of Metabolic Systems and Bioinformatics - LASIS, Department of Biochemistry, Biosciences Center, Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X); Federal University of Rio Grande do Norte, Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute (IMD), Natal, Brazil (GRID:grid.411233.6) (ISNI:0000 0000 9687 399X) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2541123682
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.