VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses

Background Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools. Results Here...

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Bibliographic Details
Published in:Microbiome Vol. 9; no. 1; pp. 37 - 13
Main Authors: Guo, Jiarong, Bolduc, Ben, Zayed, Ahmed A., Varsani, Arvind, Dominguez-Huerta, Guillermo, Delmont, Tom O., Pratama, Akbar Adjie, Gazitúa, M. Consuelo, Vik, Dean, Sullivan, Matthew B., Roux, Simon
Format: Journal Article
Language:English
Published: London BioMed Central 01.02.2021
Springer Nature B.V
BMC
Subjects:
Bacteria
BASIC BIOLOGICAL SCIENCES
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Biosphere
Deoxyribonucleic acid
DNA
DNA viruses
DNA Viruses - classification
DNA Viruses - genetics
Earth
Ecosystem
Ecosystems
Genes
Genome, Viral - genetics
Genomes
Humans
Identification
Life Sciences
Medical Microbiology
Metagenomics
Microbial Ecology
Microbial Genetics and Genomics
Microbiology
Plasmids
Proteins
RNA polymerase
RNA viruses
RNA Viruses - classification
RNA Viruses - genetics
Software
Software Article
Virology
Viruses
ISSN:2049-2618, 2049-2618
Online Access:Get full text
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Summary:Background Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools. Results Here, we introduce VirSorter2, a DNA and RNA virus identification tool that leverages genome-informed database advances across a collection of customized automatic classifiers to improve the accuracy and range of virus sequence detection. When benchmarked against genomes from both isolated and uncultivated viruses, VirSorter2 uniquely performed consistently with high accuracy (F1-score > 0.8) across viral diversity, while all other tools under-detected viruses outside of the group most represented in reference databases (i.e., those in the order Caudovirales ). Among the tools evaluated, VirSorter2 was also uniquely able to minimize errors associated with atypical cellular sequences including eukaryotic genomes and plasmids. Finally, as the virosphere exploration unravels novel viral sequences, VirSorter2’s modular design makes it inherently able to expand to new types of viruses via the design of new classifiers to maintain maximal sensitivity and specificity. Conclusion With multi-classifier and modular design, VirSorter2 demonstrates higher overall accuracy across major viral groups and will advance our knowledge of virus evolution, diversity, and virus-microbe interaction in various ecosystems. Source code of VirSorter2 is freely available ( https://bitbucket.org/MAVERICLab/virsorter2 ), and VirSorter2 is also available both on bioconda and as an iVirus app on CyVerse ( https://de.cyverse.org/de ). 1yUdaVB8pb7ryNSffJakk6 Video abstract
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USDOE
AC02-05CH11231; SC0020173
USDOE Office of Science (SC)
ISSN:2049-2618
2049-2618
DOI:10.1186/s40168-020-00990-y