Dataset Information

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.

SUBMITTER: Achouri E

PROVIDER: S-EPMC10168208 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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

Achouri Emna E Felt Sébastien A SA Hackbart Matthew M Rivera-Espinal Nicole S NS López Carolina B CB

bioRxiv : the preprint server for biology 20230715

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 dat ...[more]

PMID: 37163001

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Project description:Adeno-associated viral (AAV) vectors have shown great promise in gene delivery as evidenced by recent FDA approvals. Despite efforts to optimize manufacturing for good manufacturing practice (GMP) productions, few academic laboratories have the resources to assess vector composition. One critical component of vector quality is packaged genome fidelity. Errors in viral genome replication and packaging can result in the incorporation of faulty genomes with mutations, truncations, or rearrangements, compromising vector potency. Thus, sequence validation of packaged genome composition is an important quality control (QC), even in academic settings. We developed Fast-Seq, an end-to-end method for extraction, purification, sequencing, and data analysis of packaged single-stranded AAV (ssAAV) genomes intended for non-GMP preclinical environments. We validated Fast-Seq on ssAAV vectors with three different genome compositions (CAG-GFP, CAG-tdTomato, EF1α-FLuc), three different genome sizes (2.9, 3.6, 4.4 kb), packaged in four different capsid serotypes (AAV1, AAV2, AAV5, and AAV8), and produced using the two most common production methods (Baculovirus-Sf9 and human HEK293), from both common commercial vendors and academic core facilities supplying academic laboratories. We achieved an average genome coverage of >1,400 × and an average inverted terminal repeat coverage of >280 × , despite the many differences in composition of each ssAAV sample. When compared with other ssAAV next-generation sequencing (NGS) methods for GMP settings, Fast-Seq has several unique advantages: Tn5 transposase-based fragmentation rather than sonication, 125 × less input DNA, simpler adapter ligation, compatibility with commonly available inexpensive sequencing instruments, and free open-source data analysis code in a preassembled customizable Docker container designed for novices. Fast-Seq can be completed in 18 h, is more cost-effective than other NGS methods, and is more accurate than Sanger sequencing, which is generally only applied at 1-2 × sequencing depth. Fast-Seq is a rapid, simple, and inexpensive methodology to validate packaged ssAAV genomes in academic settings.

Dataset Information

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

Publications

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

Similar Datasets

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