VODKA2: a fast and accurate method to detect nonstandard viral genomes from large RNA-seq data sets

Emna Achouri, Sébastien A. Felt, Matthew Hackbart, Nicole S. Rivera-Espinal, Carolina B. López

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)16-25
Number of pages10
JournalRNA
Volume30
Issue number1
DOIs
StatePublished - Jan 2024

Keywords

  • RNA-seq
  • VODKA2
  • bioinformatics
  • defective viral genomes
  • viral genomes

Fingerprint

Dive into the research topics of 'VODKA2: a fast and accurate method to detect nonstandard viral genomes from large RNA-seq data sets'. Together they form a unique fingerprint.

Cite this