ArticleBMC bioinformatics2019
TAR-VIR: a pipeline for TARgeted VIRal strain reconstruction from metagenomic data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An evaluation of computational methods for reconstruction of human viral DNA genomes.GigaScience · 2026Article
- Setu: a pipeline for the robust assembly of SARS-CoV-2 genomes.Microbiology resource announcements · 2024Article
- UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example.BMC genomics · 2024Article
- Current trends in RNA virus detection through metatranscriptome sequencing data.FEBS open bio · 2023Review
- Agnostic Sequencing for Detection of Viral Pathogens.Clinical microbiology reviews · 2023Review
- VirStrain: a strain identification tool for RNA viruses.Genome biology · 2022Article
- Benchmark of thirteen bioinformatic pipelines for metagenomic virus diagnostics using datasets from clinical samples.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundStrain-level RNA virus characterization is essential for developing prevention and treatment strategies. Viral metagenomic data, which can contain sequences of both known and novel viruses, provide new opportunities for characterizing RNA viruses. Although there are a number of pipelines for analyzing viruses in metagenomic data, they have different limitations. First, viruses that lack closely related reference genomes cannot be detected with high sensitivity. Second, strain-level analysis is usually missing.
resultsIn this study, we developed a hybrid pipeline named TAR-VIR that reconstructs viral strains without relying on complete or high-quality reference genomes. It is optimized for identifying RNA viruses from metagenomic data by combining an effective read classification method and our in-house strain-level de novo assembly tool. TAR-VIR was tested on both simulated and real viral metagenomic data sets. The results demonstrated that TAR-VIR competes favorably with other tested tools.
conclusionTAR-VIR can be used standalone for viral strain reconstruction from metagenomic data. Or, its read recruiting stage can be used with other de novo assembly tools for superior viral functional and taxonomic analyses. The source code and the documentation of TAR-VIR are available at https://github.com/chjiao/TAR-VIR .
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