VsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data. Issue 6 (15th November 2022)

• Record Type: Journal Article
• Authors: Cai, Zena; Fu, Ping; Qiu, Ye; Wu, Aiping; Zhang, Gaihua; Wang, Yirong; Jiang, Taijiao; Ge, Xing-Yi; Zhu, Haizhen; Peng, Yousong
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-11-15
• Subjects: virus -- small RNAs -- small RNA-Seq -- bioinformatics -- software; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac496
• Languages: English
• ISSNs: 1467-5463

Abstract

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.