Design of specific primer sets for SARS-CoV-2 variants using evolutionary algorithms

Publication date

2021-06-26

Authors

Lopez-Rincon, AlejandroISNI 0000000440268079
Romero, Carmina A.Perez
Maldonado, Lucero Mendoza
Claassen, Eric
Garssen, JohanORCID 0000-0002-8678-9182ISNI 0000000034097251
Kraneveld, Aletta D.ISNI 000000038803088X
Tonda, Alberto

Editors

Chicano, Francisco

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

Primer sets are short DNA sequences of 18-22 base pairs, that can be used to verify the presence of a virus, and designed to attach to a specific part of a viral DNA. Designing a primer set requires choosing a region of DNA, avoiding the possibility of hybridization to a similar sequence, as well as considering its GC content and Tm (melting temperature). Coronaviruses, such as SARS-CoV-2, have a considerably large genome (around 30 thousand nucleotides) when compared to other viruses. With the rapid rise and spread of SARS-CoV-2 variants, it has become a priority to breach our lack of specific primers available for diagnosis of this new variants. Here, we propose an evolutionary-based approach to primer design, able to rapidly deliver a high-quality primer set for a target sequence of the virus variant. Starting from viral sequences collected from open repositories, the proposed approach is proven able to uncover a specific primer set for the B.1.1.7 SARS-CoV-2 variant. Only recently identified, B.1.1.7 is already considered potentially dangerous, as it presents a considerably higher transmissibility when compared to other variants.

Keywords

B.1.1.7, Genetic algorithm, Primers, SARS-CoV-2, Taverne, Genetics, Computational Mathematics

Citation

Rincon, A L, Romero, C A P, Maldonado, L M, Claassen, E, Garssen, J, Kraneveld, A D & Tonda, A 2021, Design of specific primer sets for SARS-CoV-2 variants using evolutionary algorithms. in F Chicano (ed.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery, New York, pp. 982-990, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10/07/21. https://doi.org/10.1145/3449639.3459359, conference