Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
Higuera, P. E. & Abatzoglou, J. T. Record‐setting climate enabled the extraordinary 2020 fire season in the western United States. Glob. Change Biol. https://doi.org/10.1111/gcb.15388 (2020).
Parks, S. A. & Abatzoglou, J. T. Warmer and drier fire seasons contribute to increases in area burned at high severity in western US forests from 1985 to 2017. Geophys. Res. Lett. 47, e2020GL089858 (2020).
Benavides-Solorio, J. D. D. & MacDonald, L. H. Measurement and prediction of post-fire erosion at the hillslope scale, Colorado Front Range. Int. J. Wildl. Fire 14, 457–474 (2005).
Pierson, D. N., Robichaud, P. R., Rhoades, C. C. & Brown, R. E. Soil carbon and nitrogen eroded after severe wildfire and erosion mitigation treatments. Int. J. Wildl. Fire 28, 814–821 (2019).
Google Scholar
Rhoades, C. C., Entwistles, D. & Butler, D. The influence of wildfire extent and severity on streamwater chemistry, sediment and temperature following the Hayman Fire, Colorado. Int. J. Wildl. Fire 20, 430–442 (2011).
Google Scholar
Chambers, M. E., Fornwalt, P. J., Malone, S. L. & Battaglia, M. A. Patterns of conifer regeneration following high severity wildfire in ponderosa pine – dominated forests of the Colorado Front Range. For. Ecol. Manage. 378, 57–67 (2016).
Rhoades, C. C. et al. The legacy of a severe wildfire on stream nitrogen and carbon in headwater catchments. Ecosystems 22, 643–657 (2019).
Google Scholar
Strickland, M. S., Lauber, C., Fierer, N. & Bradford, M. A. Testing the functional significance of microbial community composition. Ecology 90, 441–451 (2009).
Google Scholar
van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
Google Scholar
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).
Google Scholar
Hart, S. C., DeLuca, T. H., Newman, G. S., MacKenzie, M. D. & Boyle, S. I. Post-fire vegetative dynamics as drivers of microbial community structure and function in forest soils. For. Ecol. Manage. 220, 166–184 (2005).
Pressler, Y., Moore, J. C. & Cotrufo, M. F. Belowground community responses to fire: meta-analysis reveals contrasting responses of soil microorganisms and mesofauna. Oikos 128, 309–327 (2019).
Pulido-Chavez, M. F., Alvarado, E. C., DeLuca, T. H., Edmonds, R. L. & Glassman, S. I. High-severity wildfire reduces richness and alters composition of ectomycorrhizal fungi in low-severity adapted ponderosa pine forests. For. Ecol. Manage. 485, 118923 (2021).
Villadas, P. J. et al. The soil microbiome of the Laurel Forest in Garajonay National Park (La Gomera, Canary Islands): comparing unburned and burned habitats after a wildfire. Forests 10, 1051 (2019).
Dove, N. C. & Hart, S. C. Fire reduces fungal species richness and in situ mycorrhizal colonization: a meta-analysis. Fire Ecol. 13, 37–65 (2017).
Ibáñez, T. S., Wardle, D. A., Gundale, M. J. & Nilsson, M.-C. Effects of soil abiotic and biotic factors on tree seedling regeneration following a boreal forest wildfire. Ecosystems https://doi.org/10.1007/s10021-021-00666-0 (2021).
Whitman, T. et al. Soil bacterial and fungal response to wildfires in the Canadian boreal forest across a burn severity gradient. Soil Biol. Biochem. 138, 107571 (2019).
Google Scholar
Brown, S. P. et al. Context dependent fungal and bacterial soil community shifts in response to recent wildfires in the Southern Appalachian Mountains. For. Ecol. Manage. 451, 117520 (2019).
Ferrenberg, S. et al. Changes in assembly processes in soil bacterial communities following a wildfire disturbance. ISME J. 7, 1102–1111 (2013).
Google Scholar
Knelman, J. E., Schmidt, S. K., Garayburu-Caruso, V., Kumar, S. & Graham, E. B. Multiple, compounding disturbances in a forest ecosystem: fire increases susceptibility of soil edaphic properties, bacterial community structure, and function to change with extreme precipitation event. Soil Syst. 3, 1–1, 40 (2019).
Zhang, L. et al. Habitat heterogeneity induced by pyrogenic organic matter in wildfire-perturbed soils mediates bacterial community assembly processes. ISME J. 5, 1943–1955 (2021).
Tas, N. et al. Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest. ISME J. https://doi.org/10.1038/ismej.2014.36 (2014).
Yang, S. et al. Fire affects the taxonomic and functional composition of soil microbial communities, with cascading effects on grassland ecosystem functioning. Glob. Change Biol. 26, 431–442 (2020).
Dove, N. C., Safford, H. D., Bohlman, G. N., Estes, B. L. & Hart, S. C. High‐severity wildfire leads to multi‐decadal impacts on soil biogeochemistry in mixed‐conifer forests. Ecol. Appl. 30, eap.2072 (2020).
Pérez-Valera, E., Goberna, M. & Verdú, M. Fire modulates ecosystem functioning through the phylogenetic structure of soil bacterial communities. Soil Biol. Biochem. 129, 80–89 (2019).
SantaCruz-Calvo, L., González-López, J. & Manzanera, M. Arthrobacter siccitolerans sp. nov., a highly desiccation-tolerant, xeroprotectant-producing strain isolated from dry soil. Int. J. Syst. Evol. Microbiol. 63, 4174–4180 (2013).
Google Scholar
Mongodin, E. F. et al. Secrets of soil survival revealed by the genome sequence of Arthrobacter aurescens TC1. PLoS Genet. 2, 2094–2106 (2006).
Google Scholar
Bourguignon, N., Isaac, P., Alvarez, H., Amoroso, M. J. & Ferrero, M. A. Enhanced polyaromatic hydrocarbon degradation by adapted cultures of actinomycete strains. J. Basic Microbiol. 54, 1288–1294 (2014).
Google Scholar
Fischer, M. S. et al. Pyrolyzed substrates induce aromatic compound metabolism in the post-fire fungus, Pyronema domesticum. Front. Microbiol. 12, 729289 (2021).
Google Scholar
Arora, P. K. & Sharma, A. New metabolic pathway for degradation of 2-nitrobenzoate by Arthrobacter sp. SPG. Front. Microbiol. 6:551, 1–6 (2015).
Ren, L. et al. Insight into metabolic versatility of an aromatic compounds-degrading Arthrobacter sp. YC-RL1. Front. Microbiol. 9:2438, 1–15 (2018).
Cobo-Díaz, J. F. et al. Metagenomic assessment of the potential microbial nitrogen pathways in the rhizosphere of a mediterranean forest after a wildfire. Microb. Ecol. 69, 895–904 (2015).
Google Scholar
Dove, N. C., Taş, N. & Hart, S. C. Ecological and genomic responses of soil microbiomes to high-severity wildfire: linking community assembly to functional potential. ISME J. https://doi.org/10.1038/s41396-022-01232-9 (2022).
Adkins, J., Docherty, K. M., Gutknecht, J. L. M. & Miesel, J. R. How do soil microbial communities respond to fire in the intermediate term? Investigating direct and indirect effects associated with fire occurrence and burn severity. Sci. Total Environ. 745, 140957 (2020).
Google Scholar
Newton, G. L., Buchmeier, N. & Fahey, R. C. Biosynthesis and functions of mycothiol, the unique protective thiol of Actinobacteria. Microbiol. Mol. Biol. Rev. 72, 471–494 (2008).
Google Scholar
Reina-Bueno, M. et al. Role of trehalose in heat and desiccation tolerance in the soil bacterium Rhizobium etli. BMC Microbiol. 12, 207 (2012).
Google Scholar
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Musto, H. et al. Correlations between genomic GC levels and optimal growth temperatures in prokaryotes. FEBS Lett. 573, 73–77 (2004).
Google Scholar
Yakovchuk, P. Base-stacking and base-pairing contributions into thermal stability of the DNA double helix. Nucleic Acids Res. 34, 564–574 (2006).
Google Scholar
Mooshammer, M. et al. Decoupling of microbial carbon, nitrogen, and phosphorus cycling in response to extreme temperature events. Sci. Adv. 3, e1602781 (2017).
Google Scholar
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, 1–10 e2016810118 (2020).
Long, A. M., Hou, S., Ignacio-Espinoza, J. C. & Fuhrman, J. A. Benchmarking microbial growth rate predictions from metagenomes. ISME J. 15, 183–195 (2021).
Google Scholar
Karlin, S., Mrázek, J., Campbell, A. & Kaiser, D. Characterizations of highly expressed genes of four fast-growing bacteria. J. Bacteriol. 183, 5025–5040 (2001).
Google Scholar
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).
Google Scholar
Faria, S. R. et al. Wildfire-induced alterations of topsoil organic matter and their recovery in Mediterranean eucalypt stands detected with biogeochemical markers. Eur. J. Soil Sci. 66, 699–713 (2015).
Google Scholar
Chen, H., Rhoades, C. C. & Chow, A. T. Characteristics of soil organic matter 14 years after a wildfire: a pyrolysis-gas-chromatography mass spectrometry (Py-GC-MS) study. J. Anal. Appl. Pyrolysis 152, 104922 (2020).
Google Scholar
Knicker, H. Pyrogenic organic matter in soil: its origin and occurrence, its chemistry and survival in soil environments. Quat. Int. 243, 251–263 (2011).
Bahureksa, W. et al. Nitrogen enrichment during soil organic matter burning and molecular evidence of Maillard reactions. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.1c06745 (2022).
Boye, K. et al. Thermodynamically controlled preservation of organic carbon in floodplains. Nat. Geosci. 10, 415–419 (2017).
Google Scholar
LaRowe, D. E. & Van Cappellen, P. Degradation of natural organic matter: a thermodynamic analysis. Geochim. Cosmochim. Acta 75, 2030–2042 (2011).
Google Scholar
Fuchs, G., Boll, M. & Heider, J. Microbial degradation of aromatic compounds – from one strategy to four. Nat. Rev. Microbiol. 9, 803–816 (2011).
Google Scholar
Pingree, M. R. A. & DeLuca, T. H. Function of wildfire-deposited pyrogenic carbon in terrestrial ecosystems. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2017.00053 (2017).
Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3, 1–21 e00076-18 (2018).
Ahlgren, N. A., Ren, J., Lu, Y. Y., Fuhrman, J. A. & Sun, F. Alignment-free (d_2^ast) oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res. 45, 39–53 (2017).
Kuzyakov, Y. & Mason-Jones, K. Viruses in soil: nano-scale undead drivers of microbial life, biogeochemical turnover and ecosystem functions. Soil Biol. Biochem. 127, 305–317 (2018).
Google Scholar
Knowles, B. et al. Lytic to temperate switching of viral communities. Nature 531, 466–470 (2016).
Google Scholar
Hewelke, E. et al. Soil functional responses to natural ecosystem restoration of a pine forest peucedano-pinetum after a fire. Forests 11, 286 (2020).
Mahoney, D. P. & LaFavre, J. S. Coniochaeta extramundana, with a synopsis of other Coniochaeta species. Mycologia 73, 931–952 (1981).
Yang, T. et al. Distinct fungal successional trajectories following wildfire between soil horizons in a cold‐temperate forest. New Phytol. 227, 572–587 (2020).
Google Scholar
Steindorff, A. S. et al. Comparative genomics of pyrophilous fungi reveals a link between fire events and developmental genes. Environ. Microbiol. 23, 99–109 (2021).
Google Scholar
Viswanath, B., Rajesh, B., Janardhan, A., Kumar, A. P. & Narasimha, G. Fungal laccases and their applications in bioremediation. Enzyme Res. 2014, 1–21 163242 (2014).
Bouskill, N. J., Mekonnen, Z., Zhu, Q., Grant, R. & Riley, W. J. Microbial contribution to post-fire tundra ecosystem recovery over the 21st century. Commun. Earth Environ. 3, 26 (2022).
Yeager, C. M., Northup, D. E., Grow, C. C., Barns, S. M. & Kuske, C. R. Changes in nitrogen-fixing and ammonia-oxidizing bacterial communities in soil of a mixed conifer forest after wildfire. Appl. Environ. Microbiol. 71, 2713–2722 (2005).
Google Scholar
Ward, N. L. et al. Three genomes from the Phylum Acidobacteria provide insight into the lifestyles of these microorganisms in soils. Appl. Environ. Microbiol. 75, 2046–2056 (2009).
Google Scholar
García-Fraile, P., Benada, O., Cajthaml, T., Baldrian, P. & Lladó, S. Terracidiphilus gabretensis gen. nov., sp. nov., an abundant and active forest soil acidobacterium important in organic matter transformation. Appl. Environ. Microbiol. 82, 560–569 (2016).
Google Scholar
Eichorst, S. A., Kuske, C. R. & Schmidt, T. M. Influence of plant polymers on the distribution and cultivation of bacteria in the Phylum Acidobacteria. Appl. Environ. Microbiol. 77, 586–596 (2011).
Google Scholar
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).
Google Scholar
Costa, O. Y. A., Raaijmakers, J. M. & Kuramae, E. E. Microbial extracellular polymeric substances: ecological function and impact on soil aggregation. Front. Microbiol. 9, 1–14 (2018).
Smith, S. E. & Read, D. Mycorrhizal symbiosis. Soil Sci. 137, 204 (1984).
Douglas, R. B., Parker, V. T. & Cullings, K. W. Belowground ectomycorrhizal community structure of mature lodgepole pine and mixed conifer stands in Yellowstone National Park. For. Ecol. Manage. 208, 303–317 (2005).
Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. https://doi.org/10.1038/s41396-021-01159-7 (2022).
Marx, D. H., Bryan, W. C. & Cordell, C. E. Survival and growth of pine seedlings with Pisolithus ectomycorrhizae after two years on reforestation sites in North Carolina and Florida. For. Science. 23, 363–373 (1977).
Franco, A. R., Sousa, N. R., Ramos, M. A., Oliveira, R. S. & Castro, P. M. L. Diversity and persistence of ectomycorrhizal fungi and their effect on nursery-inoculated Pinus pinaster in a post-fire plantation in Northern Portugal. Microb. Ecol. 68, 761–772 (2014).
Google Scholar
Kipfmueller, K. F. & Baker, W. L. A fire history of a subalpine forest in south-eastern Wyoming, USA. J. Biogeogr. 27, 71–85 (2000).
Key, C. H. & Benson, N. C. Landscape Assessment (LA) Sampling and Analysis Methods General Techical Report (USDA Forest Service, 2006).
Parson, A., Robichaud, P. R., Lewis, S. A., Napper, C. & Clark, J. T. Field Guide for Mapping Post-fire Soil Burn Severity General Technical Report (USDA Forest Service, 2010); https://doi.org/10.2737/RMRS-GTR-243
Miesel, J. R., Hockaday, W. C., Kolka, R. K. & Townsend, P. A. Soil organic matter composition and quality across fire severity gradients in coniferous and deciduous forests of the southern boreal region. J. Geophys. Res. Biogeosci. 120, 1124–1141 (2015).
Google Scholar
Bundy, L. G. & Meisinger, J. J., Weaver, R. W., Angle, S., Bottomley, P., Bezdicek, D., Smith, S., Tabatabai, A., Wollum, A. (Eds.) in Methods of Soil Analysis: Part 2 Microbiological and Biochemical Properties 951–984 (Macmillan, 2018). https://doi.org/10.2136/sssabookser5.2.c41
McDowell, W. H. et al. A comparison of methods to determine the biodegradable dissolved organic carbon from different terrestrial sources. Soil Biol. Biochem. 38, 1933–1942 (2006).
Google Scholar
Thomas, G. W., Sparks, D. L., Page, A. L., Helmke, P. A., Loeppert, R. H., Soltanpour, P. N., Tabatabai, M. A., Johnston, C. T., Sumner, M. E. (Eds.) in Methods of Soil Analysis: Part 3 Chemical Methods, 5.3 475–490 (1996).
Dittmar, T., Koch, B., Hertkorn, N. & Kattner, G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnol. Oceanogr. Methods 6, 230–235 (2008).
Google Scholar
Tolić, N. et al. Formularity: software for automated formula assignment of natural and other organic matter from ultrahigh-resolution mass spectra. Anal. Chem. 89, 12659–12665 (2017).
Google Scholar
Bramer, L. M. et al. ftmsRanalysis: an R package for exploratory data analysis and interactive visualization of FT-MS data. PLoS Comput. Biol. 16, e1007654 (2020).
Google Scholar
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
Google Scholar
Kõljalg, U. et al. UNITE: a database providing web‐based methods for the molecular identification of ectomycorrhizal fungi. New Phytol. 166, 1063–1068 (2005).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Google Scholar
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Oksanen, J. et al. (2020). vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Google Scholar
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Google Scholar
Joshi, N. & Fass, J. Sickle: A Sliding-window, Adaptive, Quality-based Trimming Tool for Fastq Files, v1.33 (2011).
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 31, 1674–1676 (2015).
Google Scholar
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Google Scholar
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Google Scholar
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
Google Scholar
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Google Scholar
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).
Google Scholar
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
Google Scholar
Katoh, K. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
Google Scholar
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Google Scholar
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
Google Scholar
Seppey, M., Manni, M. & Zdobnov, E. M., Walker, J. M. (Ed.) BUSCO: assessing genome assembly and annotation completeness. Gene prediction 227–245 (Humana Press, 2019). https://doi.org/10.1007/978-1-4939-9173-0_14
Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).
Google Scholar
Bushmanova, E., Antipov, D., Lapidus, A. & Prjibelski, A. D. RnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience 8, 1–13 (2019).
Google Scholar
Grigoriev, I. V. et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 42, 699–704 (2014).
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).
Google Scholar
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
Google Scholar
Aramaki, T. et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
Google Scholar
Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Smid, M. et al. Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons. BMC Bioinformatics 19, 236 (2018).
Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Google Scholar
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).
Google Scholar
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).
Google Scholar
Guo, J., Vik, D., Pratama, A. A., Roux, S. & Sullivan, M. B. Viral Sequence Identification SOP with VirSorter2 (2021); protocols.io. https://doi.org/10.17504/protocols.io.btv8nn9w
Bland, C. et al. CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8, 209 (2007).
Google Scholar
Skennerton, C. T., Imelfort, M. & Tyson, G. W. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 41, e105 (2013).
Google Scholar
Source: Ecology - nature.com