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Tiled-ClickSeq for targeted sequencing of complete coronavirus genomes with simultaneous capture of RNA recombination and minority variants
Elizabeth Jaworski; Rose M Langsjoen; Brooke Mitchell; Barbara Judy; Patrick Newman; Yiyang Zhou; Daniele Swetnam; Stephanea Sotcheff; Victoria Morris; Nehad Saada; Rafael Machado; Allan McConnel; Aaron L Miller; Jessica A Plante; Kenneth S Plante; Jianli Dong; Ping Ren; Thomas Ksiazek; Vineet D Menachery; Scott Weaver; Andrew Laurence Routh.
Afiliação
  • Elizabeth Jaworski; ClickSeq Technologies LLC
  • Rose M Langsjoen; University of Texas Medical Branch, Galveston
  • Brooke Mitchell; University of Texas Medical Branch, Galveston
  • Barbara Judy; University of Texas Medical Branch at Galveston
  • Patrick Newman; University of Texas Medical Branch, Galveston
  • Yiyang Zhou; University of Texas Medical Branch
  • Daniele Swetnam; University of Texas Medical Branch, Galveston
  • Stephanea Sotcheff; University of Texas Medical Branch, Galveston
  • Victoria Morris; University of Texas Medical Branch, Galveston
  • Nehad Saada; University of Texas Medical Branch, Galveston
  • Rafael Machado; University of Texas Medical Branch, Galveston
  • Allan McConnel; University of Texas Medical Branch, Galveston
  • Aaron L Miller; University of Texas Medical Branch, Galveston
  • Jessica A Plante; University of Texas Medical Branch
  • Kenneth S Plante; University of Texas Medical Branch, Galveston
  • Jianli Dong; University of Texas Medical Branch, Galveston
  • Ping Ren; University of Texas Medical Branch
  • Thomas Ksiazek; University of Texas Medical Branch at Galveston
  • Vineet D Menachery; University of Texas Medical Branch
  • Scott Weaver; University of Texas Medical Branch
  • Andrew Laurence Routh; University of Texas Medical Branch, Galveston
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-434828
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ABSTRACT
High-throughput genomics of SARS-CoV-2 is essential to characterize virus evolution and to identify adaptations that affect pathogenicity or transmission. While single-nucleotide variations (SNVs) are commonly considered as driving virus adaption, RNA recombination events that delete or insert nucleic acid sequences are also critical. Whole genome targeting sequencing of SARS-CoV-2 is typically achieved using pairs of primers to generate cDNA amplicons suitable for Next-Generation Sequencing (NGS). However, paired-primer approaches impose constraints on where primers can be designed, how many amplicons are synthesized and requires multiple PCR reactions with non-overlapping primer pools. This imparts sensitivity to underlying SNVs and fails to resolve RNA recombination junctions that are not flanked by primer pairs. To address these limitations, we have designed an approach called Tiled-ClickSeq, which uses hundreds of tiled-primers spaced evenly along the virus genome in a single reverse-transcription reaction. The other end of the cDNA amplicon is generated by azido-nucleotides that stochastically terminate cDNA synthesis, removing the need for a paired-primer. A sequencing adaptor containing a Unique Molecular Identifier (UMI) is appended to the cDNA fragment using click-chemistry and a PCR reaction generates a final NGS library. Tiled-ClickSeq provides complete genome coverage, including the 5UTR, at high depth and specificity to the virus on both Illumina and Nanopore NGS platforms. Here, we analyze multiple SARS-CoV-2 isolates and clinical samples to simultaneously characterize minority variants, sub-genomic mRNAs (sgmRNAs), structural variants (SVs) and D-RNAs. Tiled-ClickSeq therefore provides a convenient and robust platform for SARS-CoV-2 genomics that captures the full range of RNA species in a single, simple assay.
Licença
cc_by_nd
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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