VODKA2: A fast and accurate method to detect non-standard viral genomes from large RNA-seq datasets

Abstract

During viral replication, viruses carrying an RNA genome produce non-standard viral genomes (nsVGs), including copy-back viral genomes (cbVGs) and deletion viral genomes (delVGs), that play a crucial role in regulating viral replication and pathogenesis. Because of their critical roles in determining the outcome of RNA virus infections, the study of nsVGs has flourished in recent years exposing a need for bioinformatic tools that can accurately identify them within Next-Generation Sequencing data obtained from infected samples. Here, we present our data analysis pipeline, Viral Opensource DVG Key Algorithm2 (VODKA2), that is optimized to run on a High Performance Computing (HPC) environment for fast and accurate detection of nsVGs from large data sets. Availability and implementationVODKA2 is freely available at GitHub (https://github.com/lopezlab-washu/VODKA2)