RNA viruses rapidly mutate, which can result in increased virulence, increased escape 2 0 from vaccine protection, and false negative detection results. Targeted detection methods have a 2 1 limited ability to detect unknown viruses and often provide insufficient data to detect 2 2 coinfections or identify antigenic variants. Random, deep sequencing is a method that can more 2 3 fully detect and characterize RNA viruses and is often coupled with molecular techniques or 2 4 culture methods for viral enrichment. Viral culture coupled with third-generation sequencing 2 5 were tested for the ability to detect and characterize RNA viruses. Cultures of bovine viral 2 6 diarrhea virus, canine distemper virus, epizootic hemorrhagic disease virus, infectious bronchitis 2 7 virus, two influenza A viruses, and porcine respiratory and reproductive syndrome virus were 2 8 sequenced on the MinION platform using a random, reverse primer in a strand-switching 2 9 reaction, coupled with PCR-based barcoding. Reads were taxonomically classified and used for 3 0 reference-based sequence building using a stock personal computer. This method accurately 3 1 detected and identified complete coding sequence genomes with a minimum of 20Ă— coverage 3 2 depth for all seven viruses, including a sample containing two viruses. Each lineage-typing 3 3 region had at least 26Ă— coverage depth for all viruses. Furthermore, analyzing the canine 3 4 distemper virus sample through a pipeline devoid of canine distemper virus reference sequences 3 5 modeled the ability of this protocol to detect unknown viruses. These results show the ability of 3 6 this technique to detect and characterize dsRNA, negative-and positive-sense ssRNA, 3 7nonsegmented, and segmented RNA viruses. 3 8 3 9