RNA Viral Metagenome Analysis of Subnanogram dsRNA Using Fragmented and Primer Ligated dsRNA Sequencing (FLDS)

Hirai, Miho; Takaki, Yoshihiro; Kondo, Fumie; Horie, Masayuki; Urayama, Syun-ichi; Nunoura, Takuro

doi:10.1264/jsme2.me20152

Cited by 21 publications

(28 citation statements)

References 18 publications

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“…Nucleic acid contamination from DNA viruses, hosts, and environments may occur when analyzing RNA viromes. To overcome this issue, techniques to selectively extract and sequence RNA virus-derived nucleic acids, such as the FLDS method ( Urayama et al , 2016 ; Hirai et al , 2021 ), have been developed. However, it is still challenging to eliminate all contamination from sequencing data.…”

Section: Discussionmentioning

confidence: 99%

“…However, identifying RNA virus-derived sequences from metatranscriptome data is challenging because of the low abundance of viral RNA in sequencing reads and high nucleotide diversity among RNA viruses ( Cobbin et al , 2021 ). Therefore, we developed a method of double-stranded RNA (dsRNA) sequencing named “fragmented and primer-ligated dsRNA sequencing” (FLDS) to enrich RNA virus sequences in the cellular transcriptome ( Urayama et al , 2016 , 2018 ; Hirai et al , 2021 ). Long dsRNAs are rare in eukaryotic cells; therefore, they are mainly derived from the genomes of dsRNA viruses or replicative intermediates of single-strand RNA (ssRNA) viruses ( Kumar and Carmichael, 1998 ).…”

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confidence: 99%

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NeoRdRp: A Comprehensive Dataset for Identifying RNA-dependent RNA Polymerases of Various RNA Viruses from Metatranscriptomic Data

et al. 2022

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RNA viruses are distributed throughout various environments, and most have recently been identified by metatranscriptome sequencing. However, due to the high nucleotide diversity of RNA viruses, it is still challenging to identify novel RNA viruses from metatranscriptome data. To overcome this issue, we created a dataset of RNA-dependent RNA polymerase (RdRp) domains that are essential for all RNA viruses belonging to Orthornavirae . Genes with RdRp domains from various RNA viruses were clustered based on amino acid sequence similarities. A multiple sequence alignment was generated for each cluster, and a hidden Markov model (HMM) profile was created when the number of sequences was greater than three. We further refined 426 HMM profiles by detecting RefSeq RNA virus sequences and subsequently combined the hit sequences with the RdRp domains. As a result, 1,182 HMM profiles were generated from 12,502 RdRp domain sequences, and the dataset was named NeoRdRp. The majority of NeoRdRp HMM profiles successfully detected RdRp domains, specifically in the UniProt dataset. Furthermore, we compared the NeoRdRp dataset with two previously reported methods for RNA virus detection using metatranscriptome sequencing data. Our methods successfully identified the majority of RNA viruses in the datasets; however, some RNA viruses were not detected, similar to the other two methods. NeoRdRp may be repeatedly improved by the addition of new RdRp sequences and is applicable as a system for detecting various RNA viruses from diverse metatranscriptome data.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

NeoRdRp: A Comprehensive Dataset for Identifying RNA-dependent RNA Polymerases of Various RNA Viruses from Metatranscriptomic Data

et al. 2022

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“…Nucleic acid contamination from DNA viruses, hosts, and environments is an issue when analyzing RNA viromes. To overcome this issue, techniques to selectively extract and sequence RNA virus-derived nucleic acids, such as the FLDS method (Urayama et al, 2016; Hirai et al, 2021), have been developed. However, it is still challenging to eliminate all contamination from the sequencing data.…”

Section: Discussionmentioning

confidence: 99%

“…However, identifying RNA virus-derived sequences from metatranscriptome data is challenging because of the low abundance of viral RNA in sequencing reads and the high nucleotide diversity among RNA viruses (Cobbin et al, 2021). Thus, we have developed a method of double-stranded RNA (dsRNA) sequencing named “fragmented and primer ligated dsRNA sequencing” (FLDS) to enrich the RNA virus sequences in the cellular transcriptome (Urayama et al, 2016, 2018; Hirai et al, 2021). Long dsRNAs are rare in eukaryotic cells; therefore, they are mainly derived from the genomes of dsRNA viruses or replicative intermediates of single-strand RNA (ssRNA) viruses (Kumar and Carmichael, 1998).…”

Section: Introductionmentioning

confidence: 99%

NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerase of various RNA viruses from metatranscriptomic data

Sakaguchi

Urayama

Takaki

et al. 2022

Preprint

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RNA viruses are distributed in various environments, and most RNA viruses have been recently identified by metatranscriptome sequencing. However, due to the high nucleotide diversity of RNA viruses, it is still challenging to identify their sequences. Therefore, this study generated a dataset of RNA-dependent RNA polymerase (RdRp) domains essential for all RNA viruses belonging to Orthornavirae. Also, the collected genes with RdRp domains from various RNA viruses were clustered by amino acid sequence similarity. For each cluster, a multiple sequence alignment was generated, and a hidden Markov model (HMM) profile was created if the number of sequences was greater than five. Using the 1,467 HMM profiles, we detected RdRp domains in the RefSeq RNA virus sequences, combined the hit sequences with the RdRp domains, and reconstructed the HMM profiles. As a result, 2,234 HMM profiles were generated from 12,316 RdRp domain sequences, and the dataset was named NeoRdRp. Additionally, using the UniProt dataset, we confirmed that almost all NeoRdRp HMM profiles could specifically detect RdRps in Orthornavirae. Furthermore, we compared the NeoRdRp dataset with two previously reported RNA virus detection methods to detect RNA virus sequences from metatranscriptome sequencing data. Our methods can identify most of the RNA viruses in the datasets; however, some RNA viruses were not detected, similar to the other two methods. The NeoRdRp can be improved by repeatedly adding new RdRp sequences and can be expected to be widely applied as a system for detecting various RNA viruses from metatranscriptome data.

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“…Quality filtering of raw sequence data, including removal of the Illumina adaptor, cDNA amplification adaptors, and low-quality, low-complexity, and experimentally contaminated sequences, was performed as previously described ( Urayama et al , 2018 ; Hirai et al , 2021 ). rRNA sequences were removed using SortMeRNA 2.0 ( Kopylova et al , 2012 ).…”

Section: Methodsmentioning

confidence: 99%

RNA Virosphere in a Marine Zooplankton Community in the Subtropical Western North Pacific

Hirai

Urayama

Takaki³

et al. 2022

Microb. Environ.

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Zooplankton and viruses play a key role in marine ecosystems; however, their interactions have not been examined in detail. In the present study, the diversity of viruses associated with zooplankton collected using a plankton net (mesh size: 100 μm) in the subtropical western North Pacific was investigated by fragmented and primer ligated dsRNA sequencing. We obtained 21 and 168 operational taxonomic units (OTUs) of ssRNA and dsRNA viruses, respectively, containing RNA-dependent RNA polymerase (RdRp). These OTUs presented average amino acid similarities of 43.5 and 44.0% to the RdRp genes of known viruses in ssRNA viruses and dsRNA viruses, respectively. Dominant OTUs mainly belonged to narna-like and picorna-like ssRNA viruses and chryso-like, partiti-like, picobirna-like, reo-like, and toti-like dsRNA viruses. Phylogenetic analyses of the RdRp gene revealed that OTUs were phylogenetically diverse and clustered into distinct clades from known viral groups. The community structure of the same zooplankton sample was investigated using small subunit (SSU) rRNA sequences assembled from the metatranscriptome of single-stranded RNA. More than 90% of the sequence reads were derived from metazoan zooplankton; copepods comprised approximately 70% of the sequence reads. Although this analysis provided no direct evidence of the host species of RNA viruses, these dominant zooplankton are expected to be associated with the RNA viruses detected in the present study. The present results indicate that zooplankton function as a reservoir of diverse RNA viruses and suggest that investigations of zooplankton viruses will provide a more detailed understanding of the role of viruses in marine ecosystems.

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RNA Viral Metagenome Analysis of Subnanogram dsRNA Using Fragmented and Primer Ligated dsRNA Sequencing (FLDS)

Cited by 21 publications

References 18 publications

NeoRdRp: A Comprehensive Dataset for Identifying RNA-dependent RNA Polymerases of Various RNA Viruses from Metatranscriptomic Data

NeoRdRp: A Comprehensive Dataset for Identifying RNA-dependent RNA Polymerases of Various RNA Viruses from Metatranscriptomic Data

NeoRdRp: A comprehensive dataset for identifying RNA-dependent RNA polymerase of various RNA viruses from metatranscriptomic data

RNA Virosphere in a Marine Zooplankton Community in the Subtropical Western North Pacific

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