ArticleViruses2022
DVGfinder: A Metasearch Tool for Identifying Defective Viral Genomes in RNA-Seq Data.
Article in Viruses, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Host genotype and sex shape influenza evolution and defective viral genomes.Nature communications · 2026Article
- Detailed single-cell mapping of the transcriptional response to a virus infection driven by copy-back viral genomes.PLoS pathogens · 2026Article
- Detailed single-cell mapping of the transcriptional response to a virus infection driven by copy-back viral genomes.bioRxiv : the preprint server for biology · 2026Article
- Cosmic silence and viral noise: transcriptomic crosstalk inFrontiers in microbiology · 2026Article
- Bioinformatics identification of copyback and multihost-adapted defective viral genomes in dengue virus.Frontiers in cellular and infection microbiology · 2026Article
- Deletion detection in SARS-CoV-2 genomes from COVID-19 patients: elimination of false positives.Virus evolution · 2026Article
- Defective but promising: evaluating the utility of currently available bioinformatic pipelines for detecting defective viral genomes in RNA-Seq data.The Journal of general virology · 2025Article
- Validation of diverse and previously untraceable Sendai virus copyback viral genomes by direct RNA sequencing.Journal of virology · 2025Article
- Review
- Identification, functional analysis, and clinical applications of defective viral genomes.Frontiers in microbiology · 2025Review
- Population dynamics of defective viral genomes of tomato black ring virus during host-to-host transmission.Journal of virology · 2024Article
- Quasispecies theory and emerging viruses: challenges and applications.Npj viruses · 2024Review
- Quantifying defective and wild-type viruses from high-throughput RNA sequencing.Bioinformatics (Oxford, England) · 2024Article
- Intra-Host Citrus Tristeza Virus Populations during Prolonged Infection Initiated by a Well-Defined Sequence Variant inViruses · 2024Article
- Accumulation Dynamics of Defective Genomes during Experimental Evolution of Two Betacoronaviruses.Viruses · 2024Article
- VODKA2: a fast and accurate method to detect non-standard viral genomes from large RNA-seq data sets.RNA (New York, N.Y.) · 2023Article
- High-resolution mapping reveals the mechanism and contribution of genome insertions and deletions to RNA virus evolution.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection.mBio · 2023Article
- Review
- Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection.bioRxiv : the preprint server for biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generation of different types of defective viral genomes (DVG) is an unavoidable consequence of the error-prone replication of RNA viruses. In recent years, a particular class of DVGs, those containing long deletions or genome rearrangements, has gain interest due to their potential therapeutic and biotechnological applications. Identifying such DVGs in high-throughput sequencing (HTS) data has become an interesting computational problem. Several algorithms have been proposed to accomplish this goal, though all incur false positives, a problem of practical interest if such DVGs have to be synthetized and tested in the laboratory. We present a metasearch tool, DVGfinder, that wraps the two most commonly used DVG search algorithms in a single workflow for the identification of the DVGs in HTS data. DVGfinder processes the results of ViReMa-a and DI-tector and uses a gradient boosting classifier machine learning algorithm to reduce the number of false-positive events. The program also generates output files in user-friendly HTML format, which can help users to explore the DVGs identified in the sample. We evaluated the performance of DVGfinder compared to the two search algorithms used separately and found that it slightly improves sensitivities for low-coverage synthetic HTS data and DI-tector precision for high-coverage samples. The metasearch program also showed higher sensitivity on a real sample for which a set of copy-backs were previously validated.
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