vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data

Abstract

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

Systematic identification and characterization of viral small RNAs from small RNA‐Seq data

“…The vsRNAfinder that was developed in our previous study was used to identify, annotate and quantify vsRNAs from sRNA-Seq data with the default parameters. 13 The candidate vsRNAs with both of the boundary sites having at least 5 RPM (Reads Per Million) and a p value smaller than 0.05 were considered statistically significant and highly confident. The miRNAs were annotated from vsRNAs with vsRNAfinder.…”