bioRxivPathology
Randomly primed, strand-switching MinION-based sequencing for the detection and characterization of cultured RNA viruses
K. T. Young, K. K. Lahmers, H. S. Sellers, D. E. Stallknecht, R. L. Poulson, J. T. Saliki, S. M. Tompkins, I. Padykula, C. Siepker, E. W. Howerth, M. Todd, J. B. Stanton
Abstract
RNA viruses rapidly mutate, which can result in increased virulence, increased escape from vaccine protection, and false negative detection results. Targeted detection methods have a limited ability to detect unknown viruses and often provide insufficient data to detect coinfections or identify antigenic variants. Random, deep sequencing is a method that can more fully detect and characterize RNA viruses and is often coupled with molecular techniques or culture methods for viral enrichment. Viral culture coupled with third-generation sequencing were tested for the ability to detect and characterize RNA viruses. Cultures of bovine viral diarrhea virus, canine distemper virus, epizootic hemorrhagic disease virus, infectious bronchitis virus, two influenza A viruses, and porcine respiratory and reproductive syndrome virus were sequenced on the MinION platform using a random, reverse primer in a strand-switching reaction, coupled with PCR-based barcoding. Reads were taxonomically classified and used for reference-based sequence building using a stock personal computer. This method accurately detected and identified complete coding sequence genomes with a minimum of 20x coverage depth for all seven viruses, including a sample containing two viruses. Each lineage-typing region had at least 26x coverage depth for all viruses. Furthermore, analyzing the canine distemper virus sample through a pipeline devoid of canine distemper virus reference sequences modeled the ability of this protocol to detect unknown viruses. These results show the ability of this technique to detect and characterize dsRNA, negative- and positive-sense ssRNA, nonsegmented, and segmented RNA viruses.
- assay: bulk RNA-seq
- research focus: variant calling
- approach: deep learning
- approach: statistical model
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