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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Vydáno v:	Microbiome Ročník 9; číslo 1; s. 37 - 13
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 01.02.2021 Springer Nature B.V BMC
Témata:	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
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Shrnutí:	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
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 USDOE AC02-05CH11231; SC0020173 USDOE Office of Science (SC)
ISSN:	2049-2618 2049-2618
DOI:	10.1186/s40168-020-00990-y