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VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses | Microbiome
Description:
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 ). Video abstract
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Keywords {🔍}
viral, virsorter, viruses, pubmed, sequences, article, genomes, tools, genes, google, scholar, sequence, cas, genome, groups, classifiers, data, fragments, score, refseq, gene, virus, central, fig, percent, group, plasmids, features, size, rna, number, tool, eukaryotic, input, generated, detection, caudovirales, vibrant, archaea, bacterial, ncbi, marvel, identification, virome, virfinder, deepvirfinder, contigs, nat, roux, order,
Topics {✒️}
/iplant/home/shared/ivirus/xfams/version_0 /6489360d-1126-413b-a667-d18e39d5f2f1/viral_db_default article download pdf metagenome-wide association studies gov/pmc/articles/pmc7000223/ gov/pmc/articles/pmc4384744/ gov/pmc/articles/pmc6090037/ gov/pmc/articles/pmc6584876/ gov/pmc/articles/pmc6477088/ protein-protein interaction networks globally-distributed giant viruses paez-espino virus-host interactions resolved rna-dependent rna polymerase k-mer based tool k-mer-based methods multi-threading efficiency comparison k-mer based tools ohio supercomputer center metagenome-assembled viral genomes custom hmm database open reading frames �/usr/bin/time f1-score substantially decreased column headers correspond privacy choices/manage cookies automatic machine-learning classifiers high-throughput gene marker high-quality genomes assembled negative-sense rna viruses large-scale sequencing data handle large-scale datasets bmc evol biol comprehensive software distribution contemporary phage biology emerson jb peak memory comparison circular dna genomes enables robust detection transcription elongation factor memory usage stays sensitive virus identification detect diverse dna error rate ranged article guo providing computing resources plos comput biol train machine-learning classifiers eddy sr ohio state university
Questions {❓}
- Are there 1031 virus particles on earth, or more, or fewer?
- Prophages and bacterial genomics: what have we learned so far?
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headline:VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses
description: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. 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. 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
).
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headline:VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses
description: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. 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. 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
).
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