RNA viromes from terrestrial sites across China expand environmental viral diversity
Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540539–543 (2016).
Zhang, Y.-Z., Shi, M. & Holmes, EC Using metagenomics to characterize an expanding virosphere. Cell 1721168–1172 (2018).
Li, C.-X. et al. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife 4e05378 (2015).
Starr, EP, Nuccio, EE, Pett-Ridge, J., Banfield, JF & Firestone, MK Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 11625900–25908 (2019).
Wolf, YI et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat. Microbiol. 51262–1270 (2020).
Zayed, AA et al. Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome. Science 376156–162 (2022).
Simmonds, P. et al. Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15161–168 (2017).
Trubl, G., Hyman, P., Roux, S. & Abedon, ST Coming-of-age characterization of soil viruses: a user’s guide to virus isolation, detection within metagenomes, and viromics. Soil Syst. 423 (2020).
Jin, M. et al. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome 758 (2019).
Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3e00076-18 (2018).
Steward, GF et al. Are we missing half of the viruses in the ocean? ISME J. 7672–679 (2013).
Paul, KI, Scott Black, A. & Conyers, MK in Advances in Agronomy. Sparks, DL, Vol. 78 187–214 (Elsevier, 2003).
Urayama, S., Takaki, Y. & Nunoura, T. FLDS: a comprehensive dsRNA sequencing method for intracellular RNA virus surveillance. Microbes Environ. 3133–40 (2016).
Armbrust, EV The life of diatoms in the world’s oceans. Nature 459185–192 (2009).
Wu, W., Jin, Y., Bai, F. & Jin, S. in Molecular Medical Microbiology. Tang, YW, Liu, D., Schwartzman, J., Sussman, M., Poxton, I., 753–767 (Elsevier, 2015).
Cooney, S., O’Brien, S., Iversen, C. & Fanning, S. in Encyclopedia of Food Safety. Motarjemi, Y., 433–441 (Elsevier, 2014).
Geoghegan, JL et al. Hidden diversity and evolution of viruses in market fish. Virus Evol. 4vey031 (2018).
Lauber, C. et al. Deciphering the origin and evolution of hepatitis B viruses by means of a family of non-enveloped fish viruses. Cell Host Microbe 22387–399.e6 (2017).
Shi, M., Zhang, Y.-Z. & Holmes, EC Meta-transcriptomics and the evolutionary biology of RNA viruses. Virus Res. 24383–90 (2018).
Turnbull, OMH et al. Meta-transcriptomic identification of divergent Amnoonviridae in Fish. Viruses 121254 (2020).
Bauermann, FV, Hause, B., Buysse, AR, Joshi, LR & Diel, DG Identification and genetic characterization of a porcine herpes-astrovirus (bastrovirus) in the United States. Arch. Virol. 1642321–2326 (2019).
Oude Munnink, BB et al. A novel astrovirus-like RNA virus detected in human stool. Virus Evol. 2vew005 (2016).
Williamson, KE et al. Estimates of viral abundance in soils are strongly influenced by extraction and enumeration methods. Biol. Fertil. Soils 49857–869 (2013).
Wang, C., Liu, D. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120126–133 (2018).
Wang, Q. et al. Effects of nitrogen and phosphorus inputs on soil bacterial abundance, diversity, and community composition in Chinese fir plantations. Front. Microbiol. 91543 (2018).
Payne, S. in Viruses. Payne, S., 219–226 (Elsevier, 2017).
Hillman, BI & Cai, G. The family Narnaviridae. Adv. Virus Res. 86149–176 (2013).
Wolf, YI et al. Origins and evolution of the global RNA virome. mBio 9e02329-18 (2018).
Bolger, AM, Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 302114–2120 (2014).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 311674–1676 (2015).
Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579265–269 (2020).
Buchfink, B., Xie, C. & Huson, DH Fast and sensitive protein alignment using DIAMOND. Nat. Methods 1259–60 (2015).
Katoh, K. & Standley, DM MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30772–780 (2013).
Capella-Gutierrez, S., Silla-Martinez, JM & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 251972–1973 (2009).
Nguyen, L.-T., Schmidt, HA, von Haeseler, A. & Minh, BQ IQ-TREE: a fast and efficient stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32268–274 (2015).
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analysis in R. Bioinformatics 35526–528 (2019).
Yu, G., Smith, DK, Zhu, H., Guan, Y. & Lam, TT ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 828–36 (2017).
Langmead, B. & Salzberg, SL Fast gapped-read alignment with Bowtie 2. Nat. Methods 9357–359 (2012).
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39578–585 (2021).
Almagro Armenteros, JJ et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37420–423 (2019).
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, ELL Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305567–580 (2001).
Gupta, R., Jung, E. & Brunak, S. NetNGlyc 1.0 Server (2017). DTU Health Tech. http://www.cbs.dtu.dk/services/NetNGlyc/
Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45D170–D176 (2017).
Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9173–175 (2012).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41D590–D596 (2012).
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetic parameter estimation from sequencing data. Bioinformatics 272987–2993 (2011).
Lagkouvardos, I., Fischer, S., Kumar, N. & Clavel, T. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ 5e2836 (2017).
McLeod, A., Xu, C. & Lai, Y. Package ‘bestglm’. CRAN. (2020).
Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 283150–3152 (2012).