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Viruses
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Viruses
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dsRNA-Seq: Identification of viral infection by purifying and sequencing dsRNA

Authors: Carolyn J. Decker; Halley R. Steiner; Laura L. Hoon-Hanks; James H. Morrison; Kelsey C. Haist; Alex C. Stabell; Eric M. Poeschla; +4 Authors

dsRNA-Seq: Identification of viral infection by purifying and sequencing dsRNA

Abstract

AbstractRNA viruses are a major source of emerging and re-emerging infectious diseases around the world. We developed a method to identify RNA viruses that is based on the fact that all RNA viruses produce dsRNA while replicating. Purifying and sequencing dsRNA from total RNA isolated from infected tissue allowed us to recover replicating viral sequences. We refer to this approach as dsRNA-Seq. By assembling dsRNA sequences into contigs we identified full length RNA viruses of varying genome types infecting mammalian culture samples, identified a known viral disease agent in laboratory infected mice, and successfully detected naturally occurring RNA viral infections in reptiles. Here we show that dsRNA-Seq is a preferable method for identifying viruses in organisms that don’t have sequenced genomes and/or commercially available rRNA depletion reagents. Similar to other metagenomic strategies, dsRNA-Seq has the potential to identify unknown viral disease agents that share little to no similarity to known viruses. However, the significant advantage of this method is the ability to identify replicated viral sequences, which is useful for distinguishing infectious viral agents from potential noninfectious viral particles or contaminants.

Keywords

RNA virus, dsRNA, Genome, Viral, Virus Replication, Microbiology, Article, Mice, RNA Virus Infections, double-stranded rna, Chlorocebus aethiops, Animals, RNA Viruses, emerging disease, RNA-Seq, Vero Cells, RNA, Double-Stranded, Virion, dsrna, High-Throughput Nucleotide Sequencing, QR1-502, rna virus, double-stranded RNA, emerging viruses, rna-seq, RNA, Viral

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    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    27
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
Green
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