Monday, January 17, 2022
HomeLifestyleA listing of 1,167 genomes from the human intestine archaeome

A listing of 1,167 genomes from the human intestine archaeome

- Advertisement -


  • 1.

    Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A. & Alm, E. J. Meta-analysis of intestine microbiome research identifies disease-specific and shared responses. Nat. Commun. 8, 1784 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 2.

    Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human intestine microbiome. Nat. Biotechnol. 39, 105–114 (2021).

    CAS 

    Google Scholar 

  • 3.

    Gregory, A. C. et al. The intestine virome database reveals age-dependent patterns of virome variety within the human intestine. Cell Host Microbe 28, 724–740 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D. & Lawley, T. D. Large enlargement of human intestine bacteriophage variety. Cell 184, 1098–1109 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 5.

    Moissl-Eichinger, C. et al. Archaea are interactive elements of complicated microbiomes. Traits Microbiol. 26, 70–85 (2018).

  • 6.

    Mahnert, A., Blohs, M., Pausan, M. R. & Moissl-Eichinger, C. The human archaeome: methodological pitfalls and data gaps. Emerg. Prime. Life Sci. 2.4, 469–482 (2018).

    Google Scholar 

  • 7.

    Bang, C. & Schmitz, R. A. Archaea: forgotten gamers within the microbiome. Emerg. Prime. Life Sci. 2, 459–468 (2018).

  • 8.

    Pausan, M. R. et al. Exploring the archaeome: detection of archaeal signatures within the human physique. Entrance. Microbiol. 10, 2796 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 9.

    Borrel, G., Brugère, J. F., Gribaldo, S., Schmitz, R. A. & Moissl-Eichinger, C. The host-associated archaeome. Nat. Rev. Microbiol. 18, 622–636 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 10.

    Dridi, B., Henry, M., El Khechine, A., Raoult, D. & Drancourt, M. Excessive prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected within the human intestine utilizing an improved DNA detection protocol. PLoS ONE 4, e7063–e7063 (2009).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

    Miller, T. L., Wolin, M. J., Conway de Macario, E. & Macario, A. J. Isolation of Methanobrevibacter smithii from human feces. Appl. Environ. Microbiol. 43, 227–232 (1982).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 12.

    Miller, T. L. & Wolin, M. J. Methanosphaera stadtmaniae gen. nov., sp. nov.: a species that types methane by lowering methanol with hydrogen. Arch. Microbiol. 141, 116–122 (1985).

    CAS 

    Google Scholar 

  • 13.

    Dridi, B., Fardeau, M.-L., Ollivier, B., Raoult, D. & Drancourt, M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon remoted from human faeces. Int. J. Syst. Evol. Microbiol 62, 1902–1907 (2012).

    CAS 

    Google Scholar 

  • 14.

    Borrel, G. et al. Genome sequence of ‘Candidatus Methanomassiliicoccus intestinalis’ Issoire-Mx1, a 3rd Thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e00453–13 (2013).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 15.

    Borrel, G. et al. Genome sequence of ‘Candidatus Methanomethylophilus alvus’ Mx1201, a methanogenic archaeon from the human intestine belonging to a seventh order of methanogens. J. Bacteriol. 194, 6944–6945 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 16.

    Borrel, G. et al. Genomics and metagenomics of trimethylamine-utilizing Archaea within the human intestine microbiome. ISME J. 11, 2059–2074 (2017).

  • 17.

    Koskinen, Okay. et al. First insights into the various human archaeome: particular detection of archaea within the gastrointestinal tract, lung, and nostril and on pores and skin. mBio 8, e00824-17 (2017).

  • 18.

    Kumpitsch, C. et al. Decreased B12 uptake and elevated gastrointestinal formate are related to archaeome-mediated breath methane emission in people. Microbiome 9, 193 (2021).

  • 19.

    Gaci, N., Borrel, G., Tottey, W., O’Toole, P. W. & Brugère, J.-F. Archaea and the human intestine: new starting of an previous story. World J. Gastroenterol. 20, 16062 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 20.

    Hansen, E. E. et al. Pan-genome of the dominant human gut-associated archaeon, Methanobrevibacter smithii, studied in twins. Proc. Natl Acad. Sci. USA 108, 4599–4606 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 21.

    Brugère, J.-F. et al. Archaebiotics: proposed therapeutic use of archaea to forestall trimethylaminuria and heart problems. Intestine Microbes 5, 5–10 (2014).

    Google Scholar 

  • 22.

    Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in purple meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 23.

    Bang, C., Weidenbach, Okay., Gutsmann, T., Heine, H. & Schmitz, R. A. The intestinal archaea Methanosphaera stadtmanae and Methanobrevibacter smithii activate human dendritic cells. PLoS ONE 9, e99411 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 24.

    Vierbuchen, T., Bang, C., Rosigkeit, H., Schmitz, R. A. & Heine, H. The human-associated archaeon Methanosphaera stadtmanae is acknowledged by its rna and induces Tlr8-Dependent nlrP3 inflammasome activation. Entrance. Immunol. 8, 1535 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 25.

    Pasolli, E. et al. In depth unexplored human microbiome variety revealed by over 150,000 genomes from metagenomes spanning age, geography, and way of life. Cell 176, 649–662 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 26.

    Almeida, A. et al. A brand new genomic blueprint of the human intestine microbiota. Nature 568, 499 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 27.

    Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, Okay. S. & Kyrpides, N. C. New insights from uncultivated genomes of the worldwide human intestine microbiome. Nature 568, 505–510 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 28.

    Biavati, B., Vasta, M. & Ferry, J. G. Isolation and characterization of ‘Methanosphaera cuniculi’ sp. nov. Appl. Environ. Microbiol. 54, 768–771 (1988).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 29.

    Stewart, R. D. et al. Meeting of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 30.

    Clemente, J. C. et al. The microbiome of uncontacted Amerindians. Sci. Adv. 1, e1500183 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 31.

    Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).

  • 32.

    Self, W. T., Grunden, A. M., Hasona, A. & Shanmugam, Okay. T. Molybdate transport. Res. Microbiol. 152, 311–321 (2001).

    CAS 

    Google Scholar 

  • 33.

    Jennings, M. E., Chia, N., Boardman, L. A. & Metcalf, W. W. Draft genome sequence of Methanobrevibacter smithii Isolate WWM1085, obtained from a human stool pattern. Genome Announc. 5, e01055–17 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 34.

    Torsvik, T. & Dundas, I. D. Bacteriophage of Halobacterium salinarium. Nature 248, 680–681 (1974).

    CAS 

    Google Scholar 

  • 35.

    Torsvik, T. & Dundas, I. D. Persisting phage an infection in Halobacterium salinarium str. 1. J. Gen. Virol. 47, 29–36 (1980).

    CAS 

    Google Scholar 

  • 36.

    Snyder, J. C., Bolduc, B. & Younger, M. J. 40 years of archaeal virology: increasing viral variety. Virology 479, 369–378 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 37.

    Prangishvili, D. et al. The enigmatic archaeal virosphere. Nat. Rev. Microbiol. 15, 724–739 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 38.

    Prangishvili, D., Forterre, P. & Garrett, R. A. Viruses of the Archaea: a unifying view. Nat. Rev. Microbiol. 4, 837–848 (2006).

    CAS 

    Google Scholar 

  • 39.

    Munson-McGee, J. H., Snyder, J. C. & Younger, M. J. Archaeal viruses from high-temperature environments. Genes (Basel) 9, 128 (2018).

    Google Scholar 

  • 40.

    Jang, H. Bin et al. Taxonomic project of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).

    Google Scholar 

  • 41.

    Cui, H.-L., Tohty, D., Zhou, P.-J. & Liu, S.-J. Halorubrum lipolyticum sp. nov. and Halorubrum aidingense sp. nov., remoted from two salt lakes in Xin-Jiang, China. Int. J. Syst. Evol. Microbiol. 56, 1631–1634 (2006).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 42.

    Khelaifia, S., Garibal, M., Robert, C., Raoult, D. & Drancourt, M. Draft genome sequence of a human-associated isolate of Methanobrevibacter arboriphilicus, the lowest-G+C-content archaeon. Genome Announc. 2, e01181–13 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 43.

    Zeikus, J. G. & Henning, D. L. Methanobacterium arbophilicum sp. nov. an obligate anaerobe remoted from wetwood of residing timber. Antonie Van Leeuwenhoek 41, 543–552 (1975).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 44.

    Lyu, Z. & Lu, Y. Metabolic shift on the class degree sheds gentle on adaptation of methanogens to oxidative environments. ISME J. 12, 411–423 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 45.

    Hoedt, E. C. et al. Variations down-under: alcohol-fueled methanogenesis by archaea current in Australian macropodids. ISME J. 10, 2376–2388 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 46.

    Srinivasan, G., James, C. M. & Krzycki, J. A. Pyrrolysine encoded by UAG in Archaea: charging of a UAG-decoding specialised tRNA. Science 296, 1459–1462 (2002).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 47.

    Brugère, J.-F., Atkins, J. F., O’Toole, P. W. & Borrel, G. Pyrrolysine in archaea: a twenty second amino acid encoded by a genetic code enlargement. Emerg. Prime. Life Sci. 2, 607–618 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 48.

    Söllinger, A. et al. Phylogenetic and genomic evaluation of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, fiv149 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 49.

    Bang, C. et al. Biofilm formation of mucosa-associated methanoarchaeal strains. Entrance. Microbiol. 5, 353 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 50.

    De La Cuesta-Zuluaga, J., Spector, T. D., Youngblut, N. D. & Ley, R. E. Genomic insights into diversifications of trimethylamine-utilizing methanogens to various habitats, together with the human intestine. mSystems 6, e00939–20 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 51.

    Thomas, C. M., Taib, N., Gribaldo, S. & Borrel, G. Comparative genomic evaluation of Methanimicrococcus blatticola offers insights into host adaptation in archaea and the evolution of methanogenesis. ISME Communications 1(1), 1–11 (2021).

    Google Scholar 

  • 52.

    Taffner, J., Cernava, T., Erlacher, A. & Berg, G. Novel insights into plant-associated archaea and their functioning in arugula (Eruca sativa Mill.). J. Adv. Res. 19, 39–48 (2019).

  • 53.

    Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).

    CAS 

    Google Scholar 

  • 54.

    Youngblut, N. D. et al. Vertebrate host phylogeny influences intestine archaeal variety. Nat. Microbiol. 6, 1443–1454 (2021).

  • 55.

    Youngblut, N. D. et al. Massive-scale metagenome meeting reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and different genetic variety. mSystems 5, e01045–20 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 56.

    Thomas, C., Desmond-Le Quemener, E., Gribaldo, S. & Borrel, G. Elements shaping the abundance and variety of archaea within the animal intestine. Analysis Sq.. https://doi.org/10.21203/rs.3.rs-789824/v1 (2021).

  • 57.

    Mitchell, A. L. et al. MGnify: the microbiome evaluation useful resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • 58.

    Kitts, P. A. et al. Meeting: a useful resource for assembled genomes at NCBI. Nucleic Acids Res. 44, D73–D80 (2016).

    CAS 

    Google Scholar 

  • 59.

    Wattam, A. R. et al. Enhancements to PATRIC, the all-bacterial bioinformatics database and evaluation useful resource heart. Nucleic Acids Res. 45, D535–D542 (2017).

    CAS 

    Google Scholar 

  • 60.

    Chen, I.-M. A. et al. IMG/M v. 5.0: an built-in knowledge administration and comparative evaluation system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).

    CAS 

    Google Scholar 

  • 61.

    Ondov, B. D. et al. Mash: quick genome and metagenome distance estimation utilizing MinHash. Genome Biol. 17, 132 (2016).

  • 62.

    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the standard of microbial genomes recovered from. Chilly Spring Harb. Lab. Press Methodology 1, 1043–1055 (2015).

    Google Scholar 

  • 63.

    Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol. 22, 178 (2021).

  • 64.

    Langmead, B. & Salzberg, S. L. Quick gapped-read alignment with Bowtie 2. Nat. Strategies 9, 357 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 65.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 66.

    Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. DRep: a software for quick and correct genomic comparisons that allows improved genome restoration from metagenomes by de-replication. ISME J. 11, 2864–2868 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 67.

    Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, Okay. T. & Aluru, S. Excessive throughput ANI evaluation of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

  • 68.

    Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to categorise genomes with the genome taxonomy database. Bioinformatics 36, 1925–1927 (2020).

    CAS 

    Google Scholar 

  • 69.

    Seemann, T. Prokka: fast prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    CAS 

    Google Scholar 

  • 70.

    Huerta-Cepas, J. et al. Quick genome-wide useful annotation by orthology project by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 71.

    Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology useful resource primarily based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).

    CAS 

    Google Scholar 

  • 72.

    Makarova, Okay. S., Wolf, Y. I. & Koonin, E. V. Archaeal clusters of orthologous genes (arCOGs): an replace and software for evaluation of shared options between Thermococcales, Methanococcales, and Methanobacteriales. Life 5, 818–840 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 73.

    Steinegger, M. & Söding, J. MMseqs2 allows delicate protein sequence trying to find the evaluation of huge knowledge units. Nat. Biotechnol. 35, 1026–1028 (2017).

    CAS 

    Google Scholar 

  • 74.

    Kolde, R. & Kolde, M. R. Bundle ‘pheatmap’. R. Packag 1, 790 (2015).

    Google Scholar 

  • 75.

    Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: complete and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).

    CAS 

    Google Scholar 

  • 76.

    Vallenet, D. et al. MicroScope: a platform for microbial genome annotation and comparative genomics. Database 2009, bap021 (2009).

  • 77.

    Wooden, D. E., Lu, J. & Langmead, B. Improved metagenomic evaluation with Kraken 2. Genome Biol. 20, 257 (2019).

  • 78.

    Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics knowledge. PeerJ Comput. Sci. 3, e104 (2017).

    Google Scholar 

  • 79.

    Buchfink, B., Xie, C. & Huson, D. H. Quick and delicate protein alignment utilizing DIAMOND. Nat. Strategies 12, 59 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 80.

    McKinney, W. pandas: a foundational Python library for knowledge evaluation and statistics. Python Excessive Carry out. Sci. Comput. 14, 1–9 (2011).

    Google Scholar 

  • 81.

    Bokulich, N. et al. q2-sample-classifier: machine-learning instruments for microbiome classification and regression. J. Open Supply Softw. 3, 934 (2018).

    Google Scholar 

  • 82.

    Pedregosa, F. et al. Scikit-learn: machine studying in Python. J. Machine Be taught. Res. 12, 2825–2830 (2011).

  • 83.

    Tonkin-Hill, G. et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol. 21, 180 (2020).

  • 84.

    Tettelin, H., Riley, D., Cattuto, C. & Medini, D. Comparative genomics: the bacterial pan-genome. Curr. Opin. Microbiol. 11, 472–477 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 85.

    Snipen, L. & Liland, Okay. H. micropan: an R-package for microbial pan-genomics. BMC Bioinform. 16, 79 (2015).

  • 86.

    Emiola, A. & Oh, J. Excessive throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing protection. Nat. Commun. 9, 4956 (2018).

  • 87.

    Rodriguez-R, L. M. & Konstantinidis, Okay. T. The enveomics assortment: a toolbox for specialised analyses of microbial genomes and metagenomes. PeerJ 4, e1900v1 (2016).

    Google Scholar 

  • 88.

    Rodriguez-R, L. M. et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomic and gene variety evaluation of Archaea and Micro organism on the complete genome degree. Nucleic Acids Res. 46, W282–W288 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 89.

    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: latest updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 90.

    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, Okay. MEGA X: molecular evolutionary genetics evaluation throughout computing platforms. Mol. Biol. Evol. 35, 1547 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 91.

    Music, Z. et al. Taxonomic profiling and populational patterns of bacterial bile salt hydrolase (BSH) genes primarily based on worldwide human intestine microbiome. Microbiome 7, 9 (2019).

  • 92.

    Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in an online browser. BMC Bioinform. 12, 1 (2011).

    Google Scholar 

  • 93.

    Jia, B. et al. CARD 2017: enlargement and model-centric curation of the great antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).

  • 94.

    Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 95.

    Carattoli, A. et al. In silico detection and typing of plasmids utilizing PlasmidFinder and plasmid multilocus sequence typing. Antimicrob. Brokers Chemother. 58, 3895–3903 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 96.

    Gupta, S. Okay. et al. ARG-ANNOT, a brand new bioinformatic software to find antibiotic resistance genes in bacterial genomes. Antimicrob. Brokers Chemother. 58, 212–220 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 97.

    Ingle, D. J. et al. In silico serotyping of E. coli from brief learn knowledge identifies restricted novel O-loci however intensive variety of O: H serotype mixtures inside and between pathogenic lineages. Microb. Genom. https://doi.org/10.1099/mgen.0.000064 (2016).

  • 98.

    Doster, E. et al. MEGARes 2.0: a database for classification of antimicrobial drug, biocide and steel resistance determinants in metagenomic sequence knowledge. Nucleic Acids Res. 48, D561–D569 (2020).

    CAS 

    Google Scholar 

  • 99.

    Feldgarden, M. et al. Validating the AMRFinder software and resistance gene database through the use of antimicrobial resistance genotype–phenotype correlations in a set of isolates. Antimicrob. Brokers Chemother. 63, e00483–19 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 100.

    Chen, L. et al. VFDB: a reference database for bacterial virulence elements. Nucleic Acids Res. 33, D325–D328 (2005).

    CAS 

    Google Scholar 

  • 101.

    Liu, B., Zheng, D., Jin, Q., Chen, L. & Yang, J. VFDB 2019: a comparative pathogenomic platform with an interactive net interface. Nucleic Acids Res. 47, D687–D692 (2019).

    CAS 

    Google Scholar 

  • 102.

    Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the great antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).

    CAS 

    Google Scholar 

  • 103.

    Guo, J. et al. VirSorter2: a multi-classifier, expert-guided method to detect various DNA and RNA viruses. Microbiome 9, 37 (2021).

  • 104.

    Nayfach, S. et al. CheckV assesses the standard and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).

    CAS 

    Google Scholar 

  • 105.

    Roux, S. et al. Minimal details about an uncultivated virus genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).

    CAS 

    Google Scholar 

  • 106.

    Duhaime, M. B. & Sullivan, M. B. Ocean viruses: rigorously evaluating the metagenomic sample-to-sequence pipeline. Virology 434, 181–186 (2012).

    CAS 

    Google Scholar 

  • 107.

    Duhaime, M. B. et al. Comparative omics and trait analyses of marine Pseudoalteromonas phages advance the phage OTU idea. Entrance. Microbiol. 8, 1241 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 108.

    Bobay, L.-M. & Ochman, H. Organic species within the viral world. Proc. Natl Acad. Sci. USA 115, 6040–6045 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 109.

    Gregory, A. C. et al. Genomic differentiation amongst wild cyanophages regardless of widespread horizontal gene switch. BMC Genom. 17, 930 (2016).

  • 110.

    Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, 1261498 (2015).

  • 111.

    Bengtsson‐Palme, J. et al. METAXA2: improved identification and taxonomic classification of small and enormous subunit rRNA in metagenomic knowledge. Mol. Ecol. Resour. 15, 1403–1414 (2015).

    Google Scholar 

  • 112.

    Quast, C. et al. The SILVA ribosomal RNA gene database undertaking: improved knowledge processing and web-based instruments. Nucleic Acids Res. 41, D590–D596 (2013).

    CAS 

    Google Scholar 

  • 113.

    Carver, T. J. et al. ACT: the Artemis comparability software. Bioinformatics 21, 3422–3423 (2005).

    CAS 

    Google Scholar 

  • 114.

    Parks, D. H. et al. Restoration of almost 8,000 metagenome-assembled genomes considerably expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).

    CAS 

    Google Scholar 

  • 115.

    Abby, S. S., Néron, B., Ménager, H., Touchon, M. & Rocha, E. P. C. MacSyFinder: a program to mine genomes for molecular programs with an software to CRISPR–Cas programs. PLoS ONE 9, e110726 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 116.

    Bolyen, E. et al. QIIME 2: reproducible, interactive, scalable, and extensible microbiome knowledge science. PeerJ https://doi.org/10.7287/peerj.preprints.27295 (2018).

  • 117.

    Zakrzewski, M. et al. Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions. Bioinformatics 33, 782–783 (2017).

    CAS 

    Google Scholar 

  • 118.

    Mallick, H. et al. Multivariable affiliation discovery in population-scale meta-omics research. PLoS Comput. Biol. 17, e1009442 (2021).

    Google Scholar 

  • - Advertisement -
    RELATED ARTICLES

    LEAVE A REPLY

    Please enter your comment!
    Please enter your name here

    - Advertisment -

    Most Popular