VODKA2: a fast and accurate method to detect non-standard viral genomes from large RNA-seq data sets
- Department of Molecular Microbiology and Center for Women's Infectious Disease Research, Washington University School of Medicine, St. Louis, Missouri 63110, USA
- Corresponding author: clopezzalaquett{at}wustl.edu
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Handling editor: Mihaela Zavolan
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 Algorithm 2 (VODKA2), that is optimized to run on a parallel computing environment for fast and accurate detection of nsVGs from large data sets.
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Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.079747.123.
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Freely available online through the RNA Open Access option.
- Received June 15, 2023.
- Accepted October 12, 2023.
This article, published in RNA, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
