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

RNA viruses are distributed throughout various environments, and most RNA viruses 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 their 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 three. We further refined the 426 HMM profiles by detecting the RefSeq RNA virus sequences and subsequently combined the hit sequences with the RdRp domains. As a result, a total of 1,182 HMM profiles were generated from 12,502 RdRp domain sequences, and the dataset was named NeoRdRp. Almost all 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 most of the RNA viruses in the datasets; however, some RNA viruses were not detected, as in the cases of the other two methods. The NeoRdRp can be repeatedly improved by adding new RdRp sequences and is applicable as a system for detecting various RNA viruses from diverse metatranscriptome data.