Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

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Alejandro Lopez Rincon

Division of Pharmacology,
Utrecht Institute for Pharmaceutical Sciences,
Faculty of Science,
Utrecht University

Department of Data science and Biostatistics,
Division Julius Center for Health Sciences and Primary Care
University Medical Center Utrecht

Abstract:
As the COVID-19 pandemic continues, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Using a completely automated pipeline built around deep learning and evolutionary algorithms techniques, we designed primer sets specific to variants B.1.1.7, B.1.351, P.1, B.1.617.2 and Omicron. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets for each variant. In-silico tests show that the sequences in the primer sets present high accuracy and are based on 2 mutations or more and have been succesfully tested in laboratory.