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Updated: Nov 2, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Simulation study and comparative evaluation of viral contiguous sequence identification tools.

Cody Glickman^1,2, Jo Hendrix^3,4, Michael Strong^3,4

¹Center for Genes, Environment, and Health, National Jewish Health, 1400 Jackson Street, Denver, CO, 80206, USA. cody.glickman@cuanschutz.edu.

BMC Bioinformatics

|June 16, 2021

Summary

This summary is machine-generated.

This study compared nine bioinformatics tools for identifying viral sequences in metagenomes. VirSorter and Vibrant performed best for integrated prophages, while Kraken2 offered high precision for viral contigs.

Keywords:

Bacteriophage Metagenomics Prophage Tool comparison Virus

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Published on: June 16, 2011

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Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Published on: March 22, 2016

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Viruses, including bacteriophages, are crucial in microbial communities, influencing gene transfer and pathogen virulence.
Metagenomic techniques often miss large and integrated viruses, limiting comprehensive viral analysis.
Understanding viral communities is vital for environmental and human health research.

Purpose of the Study:

To benchmark nine state-of-the-art bioinformatics tools for viral contiguous sequence (contig) identification.
To evaluate tool performance across simulated metagenomes with varying complexity and viral elements.
To assess the capability of tools in detecting integrated prophages often missed by other methods.

Main Methods:

Comparative analysis of nine bioinformatic tools: Vibrant, VirSorter, VirSorter2, VirFinder, DeepVirFinder, MetaPhinder, Kraken 2, Phybrid, and BLAST.
Testing on simulated metagenomes with diverse read distributions, taxonomic compositions, and complexities.
Evaluation metrics included F1 score, precision, and recall for viral contig and prophage identification.

Main Results:

VirSorter and Vibrant showed strong performance in identifying integrated prophages.
Kraken 2 achieved the highest average precision for viral contigs.
Tool performance was influenced by contig length and taxonomic complexity, with some phage hosts being more consistently identified than others.
The machine learning tool Phybrid demonstrated improved performance using both gene content and nucleotide features.

Conclusions:

This study provides a comprehensive benchmark of viral identification tools for metagenomic data.
The findings offer valuable insights for researchers selecting tools to analyze viral elements, including integrated prophages.
The performance variations highlight the need to consider specific viral and metagenomic characteristics when choosing identification methods.