Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right...

Full description

Saved in:
Bibliographic Details
Published in:Genome Biology 2024-04, Vol.25 (1), p.97-23, Article 97
Main Authors: Wu, Ling-Yi, Wijesekara, Yasas, Piedade, Gonçalo J, Pappas, Nikolaos, Brussaard, Corina P D, Dutilh, Bas E
Format: Article
Language:English
Subjects:
Benchmarking
Coastal waters
Computational Biology - methods
Databases, Genetic
Datasets
Ecosystem
Ecosystems
Genes
Genetic diversity
Genetic engineering
Genomes
Genomics
Identification
Machine learning
Metabolism
Metagenome
Metagenomics
Metagenomics - methods
Neural networks
Physiology
RNA polymerase
Seawater
Viral infections
Viruses
Viruses - genetics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-024-03236-4