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Background The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity. Results In this study, we introduce RNAVirHost, a machine learning–based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%. Conclusions The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.
Background The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity. Results In this study, we introduce RNAVirHost, a machine learning–based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%. Conclusions The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.
Rhizopus microsporus is a species in the order Mucorales that is known to cause mucormycosis, but it is poorly understood as a host of viruses. Here, we examined 25 clinical strains of R. microsporus for viral infection with a conventional double-stranded RNA (dsRNA) assay using agarose gel electrophoresis (AGE) and the recently established fragmented and primer-ligated dsRNA sequencing (FLDS) protocol. By AGE, five virus-infected strains were detected. Then, full-length genomic sequences of 12 novel RNA viruses were revealed by FLDS, which were related to the families Mitoviridae, Narnaviridae, and Endornaviridae, ill-defined groups of single-stranded RNA (ssRNA) viruses with similarity to the established families Virgaviridae and Phasmaviridae, and the proposed family “Ambiguiviridae.” All the characterized viruses, except a potential phasmavirid with a negative-sense RNA genome, had positive-sense RNA genomes. One virus belonged to a previously established species within the family Mitoviridae , whereas the other 11 viruses represented new species or even new genera. These results show that the fungal pathogen R. microsporus harbors diverse RNA viruses and extend our understanding of the diversity of RNA viruses in the fungal order Mucorales, division Mucoromycota. Identifying RNA viruses from clinical isolates of R. microsporus may expand the repertoire of natural therapeutic agents for mucormycosis in the future. IMPORTANCE The diversity of mycoviruses in fungal hosts in the division Mucoromycota has been underestimated, mainly within the species Rhizopus microsporus . Only five positive-sense RNA genomes had previously been discovered in this species. Because current sequencing methods poorly complete the termini of genomes, we used fragmented and primer-ligated double-stranded RNA sequencing to acquire the full-length genomes. Eleven novel mycoviruses were detected in this study, including the first negative-sense RNA genome reported in R. microsporus . Our findings extend the understanding of the viral diversity in clinical strains of Mucoromycota, may provide insights into the pathogenesis and ecology of this fungus, and may offer therapeutic options.
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