Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Christian B. Matranga, Adrianne Gladden-Young, James Qu, S Winnicki, Dolo Nosamiefan, Joshua Z. Levin, Pardis C. Sabeti

Journal of Visualized Experiments · 2016 · 50 citations · 18 references

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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.

References

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