Abstract
SummaryCulture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.
Highlights
Viruses that infect Bacteria and Archaea are the most abundant and diverse biological entities on Earth
Alignment-free methods (e.g., WIsH) show very high recall but usually have low precision, with reported host-prediction accuracy for genus-level predictions between 33% and 64% depending on the dataset.[2,3,4]
We tested the performance of Random Forest Assignment of Hosts (RaFAH) and other host-prediction approaches on an independent dataset of isolated viral genomes that did not overlap with those used for training the models (Test Set 1, composed of RefSeq viral genomes with less than 70% average amino acid identity when compared with those in Training Set 3, see experimental procedures)
Summary
Culture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery.
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