Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
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Author
Matranga, Christian B.
Gladden-Young, Adrianne
Qu, James
Winnicki, Sarah
Nosamiefan, Dolo
Levin, Joshua Z.
Published Version
https://doi.org/10.3791/54117Metadata
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Matranga, Christian B., Adrianne Gladden-Young, James Qu, Sarah Winnicki, Dolo Nosamiefan, Joshua Z. Levin, and Pardis C. Sabeti. 2016. “Unbiased Deep Sequencing of RNA Viruses from Clinical Samples.” Journal of Visualized Experiments : JoVE (113): 54117. doi:10.3791/54117. http://dx.doi.org/10.3791/54117.Abstract
Here we outline a next-generation RNA sequencing protocol that enables de novo assemblies and intra-host variant calls of viral genomes collected from clinical and biological sources. The method is unbiased and universal; it uses random primers for cDNA synthesis and requires no prior knowledge of the viral sequence content. Before library construction, selective RNase H-based digestion is used to deplete unwanted RNA — including poly(rA) carrier and ribosomal RNA — from the viral RNA sample. Selective depletion improves both the data quality and the number of unique reads in viral RNA sequencing libraries. Moreover, a transposase-based 'tagmentation' step is used in the protocol as it reduces overall library construction time. The protocol has enabled rapid deep sequencing of over 600 Lassa and Ebola virus samples-including collections from both blood and tissue isolates-and is broadly applicable to other microbial genomics studies.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4993327/pdf/Terms of Use
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