VirusDetect: an automated pipeline for efficient virus discovery using deep sequencing of small RNAs

Accurate detection of viruses in plants and animals is critical for agriculture production and human health. Deep sequencing and assembly of virus-derived small interfering RNAs has proven to be a highly efficient approach for virus discovery. Here we present VirusDetect, a bioinformatics pipeline that can efficiently analyze largescale small RNA (sRNA) datasets for both known and novel virus identification. VirusDetect performs both reference-guided assemblies through aligning sRNA sequences to a curated virus reference database and de novo assemblies of sRNA sequences with automated parameter optimization and the option of host sRNA subtraction. The assembled contigs are compared to a curated and classified reference virus database for known and novel virus identification, and evaluated for their sRNA size profiles to identify novel viruses. Extensive evaluations using plant and insect sRNA datasets suggest that VirusDetect is highly sensitive and efficient in identifying known and novel viruses.

Bibliographic Details

Main Authors:	Yi Zheng, Shan Gao, Padmanabhan, C., Li, R., Galvez, M., Gutiérrez, D., Fuentes, S., Kai-Shu Ling, Kreuze, Jan F., Zhangjun Fei
Format:	Journal Article biblioteca
Language:	English
Published:	Elsevier 2017-01-01
Subjects:	virus indexing, rna, rna sequence, research,
Online Access:	https://hdl.handle.net/10568/80028 https://doi.org/10.1016/j.virol.2016.10.017
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