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

AbstractAccurate detection of viruses in plants and animals is critical for agriculture production and human health. Upon viral infection, plants and animals activate an antiviral defense system called RNA silencing or RNA interference (RNAi) which cleaves and amplifies viral RNA into pieces of 21-24 nucleotide small interfering RNAs (siRNAs). Deep sequencing and assembly of these virus-derived siRNAs has proven to be a highly efficient approach for virus discovery. However, to date very few computational tools are available for virus discovery from siRNA sequences. Here we present VirusDetect, an advanced automated bioinformatics pipeline that can efficiently analyze large-scale siRNA datasets for both known and novel virus identification. The pipeline first aligns siRNA sequences to a curated virus reference database and performs reference-guided assembly. The pipeline then performs host siRNA subtraction and de novo assembly of siRNA sequences with automated parameter optimization. The assembled contigs are compared to the reference virus database for known and novel virus identification. Extensive evaluations using plant and Drosophila melanogaster siRNA datasets and comparison with the other tool suggest that VirusDetect is highly sensitive and efficient in identifying known and novel viruses with the ability to replace current standard virus indexing protocols. Furthermore, VirusDetect shows good performance in virus discovery using other types of next-generation sequencing (NGS) datasets, indicating it can be used as a universal virus discovery tool. VirusDetect is freely available at http://bioinfo.bti.cornell.edu/tool/VirusDetect/.