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

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Title VirusDetect: an automated pipeline for efficient virus discovery using deep sequencing of small RNAs.
 
Creator Yi Zheng
Shan Gao
Padmanabhan, C.
Li, R.
Galvez, M.
Gutiérrez, D.
Fuentes, S.
Kai-Shu Ling
Kreuze, J.F.
Zhangjun Fei
 
Subject VIRUS INDEXING
RNA
RNA SEQUENCE
RESEARCH
 
Description 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.
Peer Review
 
Date 2017-02-28T13:35:50Z
2017-02-28T13:35:50Z
2017-01
 
Type Journal Article
 
Identifier Zheng, Y.; Gaoa, S.; Padmanabhan, C.; Li, R.; Galvez, M.; Gutierrez, D.; Fuentes, S.; Ling, K.S.; Kreuze, J.; Fei, Z. 2017. VirusDetect: an automated pipeline for efficient virus discovery using deep sequencing of small RNAs. Virology. (USA). ISSN 0042-6822. 500:130-138.
0042-6822
https://hdl.handle.net/10568/80028
 
Language en
 
Format 130-138
 
Source Virology