著者

So Nakagawa Toshiaki Katayama Lihua Jin Jiaqi Wu Kirill Kryukov Rise Oyachi Junko S Takeuchi Takatomo Fujisawa Satomi Asano Momoka Komatsu Jun-ichi Onami Takashi Abe Masanori Arita

出版者

The Genetics Society of Japan

雑誌

Genes & Genetic Systems (ISSN:13417568)

巻号頁・発行日

pp.23-00085, (Released:2023-10-14)

参考文献数

53

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Since the early phase of the coronavirus disease 2019 (COVID-19) pandemic, a number of research institutes have been sequencing and sharing high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes to trace the route of infection in Japan. To provide insight into the spread of COVID-19, we developed a web platform named SARS-CoV-2 HaploGraph to visualize the emergence timing and geographical transmission of SARS-CoV-2 haplotypes. Using data from the GISAID EpiCoV database as of June 4, 2022, we created a haplotype naming system by determining the ancestral haplotype for each epidemic wave and showed prefecture- or region-specific haplotypes in each of four waves in Japan. The SARS-CoV-2 HaploGraph allows for interactive tracking of virus evolution and of geographical prevalence of haplotypes, and aids in developing effective public health control strategies during the global pandemic. The code and the data used for this study are publicly available at: https://github.com/ktym/covid19/.

著者

Shoichi Sakaguchi Syun-ichi Urayama Yoshihiro Takaki Kensuke Hirosuna Hong Wu Youichi Suzuki Takuro Nunoura Takashi Nakano So Nakagawa

出版者

Japanese Society of Microbial Ecology / Japanese Society of Soil Microbiology / Taiwan Society of Microbial Ecology / Japanese Society of Plant Microbe Interactions / Japanese Society for Extremophiles

雑誌

Microbes and Environments (ISSN:13426311)

巻号頁・発行日

vol.37, no.3, pp.ME22001, 2022 (Released:2022-08-24)

参考文献数

30

被引用文献数

9

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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.