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SARS-CoV-2 Recombination and Coinfection Events Identified in Clinical Samples in Russia.

Ekaterina N ChernyaevaAndrey A AygininAlexey V KosenkovSvetlana V RomanovaAnastasia V TsypkinaAndrey R LuparevIvan F StetsenkoNatalia I GnusarevaAlina D MatsvayYulia A SavochkinaGerman A Shipulin
Published in: Viruses (2023)
Recombination is one of the mechanisms of SARS-CoV-2 evolution along with the occurrence of point mutations, insertions, and deletions. Recently, recombinant variants of SARS-CoV-2 have been registered in different countries, and some of them have become circulating forms. In this work, we performed screening of SARS-CoV-2 genomic sequences to identify recombination events and co-infections with various strains of the SARS-CoV-2 virus detected in Russia from February 2020 to March 2022. The study included 9336 genomes of the COVID-19 pathogen obtained as a result of high-throughput sequencing on the Illumina platform. For data analysis, we used an algorithm developed by our group that can identify viral recombination variants and cases of co-infections by estimating the frequencies of characteristic substitutions in raw read alignment files and VCF files. The detected cases of recombination were confirmed by alternative sequencing methods, principal component analysis, and phylogenetic analysis. The suggested approach allowed for the identification of recombinant variants of strains BA.1 and BA.2, among which a new recombinant variant was identified, as well as a previously discovered one. The results obtained are the first evidence of the spread of recombinant variants of SARS-CoV-2 in Russia. In addition to cases of recombination we identified cases of coinfection: eight of them contained the genome of the Omicron line as one of the variants, six of them the genome of the Delta line, and two with the genome of the Alpha line.
Keyphrases
  • sars cov
  • copy number
  • dna repair
  • dna damage
  • respiratory syndrome coronavirus
  • data analysis
  • escherichia coli
  • genome wide
  • high throughput sequencing
  • cell free
  • risk assessment
  • deep learning
  • candida albicans