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

RNA-seq VODKA2 bioinformatics defective viral genomes viral genomes

Journal

RNA (New York, N.Y.)
ISSN: 1469-9001
Titre abrégé: RNA
Pays: United States
ID NLM: 9509184

Informations de publication

Date de publication:
27 Oct 2023
Historique:
received: 15 06 2023
accepted: 12 10 2023
pubmed: 28 10 2023
medline: 28 10 2023
entrez: 27 10 2023
Statut: aheadofprint

Résumé

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 parallel computing environment for fast and accurate detection of nsVGs from large data sets.

Identifiants

pubmed: 37891004
pii: rna.079747.123
doi: 10.1261/rna.079747.123
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Commentaires et corrections

Type : UpdateOf

Informations de copyright

Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Auteurs

Classifications MeSH