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An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern

Authors :
Carmina Angelica Perez-Romero
Lucero Mendoza-Maldonado
Alberto Tonda
Etienne Coz
Patrick Tabeling
Jessica Vanhomwegen
John MacSharry
Joanna Szafran
Lucina Bobadilla-Morales
Alfredo Corona-Rivera
Eric Claassen
Johan Garssen
Aletta D. Kraneveld
Alejandro Lopez-Rincon
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract As the COVID-19 pandemic winds down, it leaves behind the serious concern that future, even more disruptive pandemics may eventually surface. One of the crucial steps in handling the SARS-CoV-2 pandemic was being able to detect the presence of the virus in an accurate and timely manner, to then develop policies counteracting the spread. Nevertheless, as the pandemic evolved, new variants with potentially dangerous mutations appeared. Faced by these developments, it becomes clear that there is a need for fast and reliable techniques to create highly specific molecular tests, able to uniquely identify VOCs. Using an automated pipeline built around evolutionary algorithms, we designed primer sets for SARS-CoV-2 (main lineage) and for VOC, B.1.1.7 (Alpha) and B.1.1.529 (Omicron). Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for the main lineage and each variant in a matter of hours. Preliminary in-silico validation showed that the sequences in the primer sets featured high accuracy. A pilot test in a laboratory setting confirmed the results: the developed primers were favorably compared against existing commercial versions for the main lineage, and the specific versions for the VOCs B.1.1.7 and B.1.1.529 were clinically tested successfully.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.2bbaccce7d84463bda65db8ea3ef76d
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-42348-y