Rusch, D. B. et al. The Sorcerer II Global Ocean Sampling Expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 5, e77 (2007).
Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).
Nayfach, S. et al. A genomic catalog of Earthâs microbiomes. Nat. Biotechnol. 39, 499â509 (2021).
Paoli, L. et al. Biosynthetic potential of the global ocean microbiome. Nature 607, 111â118 (2022).
Overmann, J. & Lepleux, C. in The Marine Microbiome (ed. Stal, L. J. & Cretoiu, M. S.) 21â55 (2016).
Nishimura, Y. & Yoshizawa, S. The OceanDNA MAG catalog contains over 50,000 prokaryotic genomes originated from various marine environments. Sci. Data 9, 305 (2022).
Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. USA 105, 7774â7778 (2008).
Auladell, A. et al. Seasonal niche differentiation among closely related marine bacteria. ISME J. 16, 178â189 (2022).
Ghiglione, J. F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl Acad. Sci. USA 109, 17633â17638 (2012).
Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. eLife 11, e78129 (2022).
McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. J. Open Source Softw. 3, 29 (2018).
Jönsson, B. F. & Watson, J. R. The timescales of global surface-ocean connectivity. Nat. Commun. 7, 11239 (2016).
Bentkowski, P., Van Oosterhout, C. & Mock, T. A model of genome size evolution for prokaryotes in stable and fluctuating environments. Genome Biol. Evol. 7, 2344â2351 (2015).
RodrÃguez-Gijón, A. et al. A genomic perspective across Earthâs microbiomes reveals that genome size in archaea and bacteria is linked to ecosystem type and trophic strategy. Front. Microbiol. 12, 761869 (2022).
Mara, P. et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 14, 3079â3092 (2020).
Cabello-Yeves, P. J. et al. The microbiome of the Black Sea water column analyzed by shotgun and genome centric metagenomics. Environ. Microbiome 16, 5 (2021).
Mende, D. R. et al. Environmental drivers of a microbial genomic transition zone in the oceanâs interior. Nat. Microbiol. 2, 1367â1373 (2017).
Musto, H. et al. Genomic GC level, optimal growth temperature, and genome size in prokaryotes. Biochem. Biophys. Res. Commun. 347, 1â3 (2006).
Almpanis, A., Swain, M., Gatherer, D. & McEwan, N. Correlation between bacterial G+C content, genome size and the G+C content of associated plasmids and bacteriophages. Microb. Genom. 4, e000168 (2018).
Sánchez-Romero, M. A. & Casadesús, J. The bacterial epigenome. Nat. Rev. Microbiol. 18, 7â20 (2020).
Hampton, H. G., Watson, B. N. & Fineran, P. C. The arms race between bacteria and their phage foes. Nature 577, 327â336 (2020).
Whittaker, C. A. & Hynes, R. O. Distribution and evolution of von Willebrand/integrin A domains: widely dispersed domains with roles in cell adhesion and elsewhere. Mol. Biol. Cell 13, 3369â3387 (2002).
Pasternak, Z. et al. By their genes ye shall know them: genomic signatures of predatory bacteria. ISME J. 7, 756â769 (2013).
Guo, M., Wang, J., Zhang, Y. & Zhang, L. Increased WD40 motifs in Planctomycete bacteria and their evolutionary relevance. Mol. Phylogenet. Evol. 155, 107018 (2021).
Hu, X. J. et al. Prokaryotic and highly-repetitive WD40 proteins: a systematic study. Sci. Rep. 7, 10585 (2017).
Neer, E. J., Schmidt, C. J., Nambudripad, R. & Smith, T. F. The ancient regulatory-protein family of WD-repeat proteins. Nature 371, 297â300 (1994).
Fuerst, J. A. & Sagulenko, E. Beyond the bacterium: planctomycetes challenge our concepts of microbial structure and function. Nat. Rev. Microbiol. 9, 403â413 (2011).
Meaden, S. et al. High viral abundance and low diversity are associated with increased CRISPRâCas prevalence across microbial ecosystems. Curr. Biol. 32, 220â227.e225 (2022).
Burstein, D. et al. Major bacterial lineages are essentially devoid of CRISPRâCas viral defence systems. Nat. Commun. 7, 10613 (2016).
Weissman, J. L., Laljani, R. M. R., Fagan, W. F. & Johnson, P. L. F. Visualization and prediction of CRISPR incidence in microbial trait-space to identify drivers of antiviral immune strategy. ISME J. 13, 2589â2602 (2019).
Gophna, U. et al. No evidence of inhibition of horizontal gene transfer by CRISPRâCas on evolutionary timescales. ISME J. 9, 2021â2027 (2015).
Zeng, X., Alain, K. & Shao, Z. Microorganisms from deep-sea hydrothermal vents. Mar. Life Sci. Tech. 3, 204â230 (2021).
Wheatley, R. M. & MacLean, R. C. CRISPRâCas systems restrict horizontal gene transfer in Pseudomonas aeruginosa. ISME J. 15, 1420â1433 (2021).
Shehreen, S., Chyou, T. Y., Fineran, P. C. & Brown, C. M. Genome-wide correlation analysis suggests different roles of CRISPRâCas systems in the acquisition of antibiotic resistance genes in diverse species. Philos. Trans. R Soc. B 374, 20180384 (2019).
Wilkinson, R. A., Martin, C., Nemudryi, A. A. & Wiedenheft, B. CRISPR RNA-guided autonomous delivery of Cas9. Nat. Struct. Mol. Biol. 26, 14â24 (2019).
Gavriilidou, A. et al. Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes. Nat. Microbiol. 7, 726â735 (2022).
Ayikpoe, R. S. et al. A scalable platform to discover antimicrobials of ribosomal origin. Nat. Commun. 13, 6135 (2022).
Wei, B. et al. Global analysis of the biosynthetic chemical space of marine prokaryotes. Microbiome 11, 144 (2023).
Fjell, C. D., Hiss, J. A., Hancock, R. E. & Schneider, G. Designing antimicrobial peptides: form follows function. Nat. Rev. Drug Discov. 11, 37â51 (2011).
Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068â1083 e1021 (2019).
Kawai, F., Kawabata, T. & Oda, M. Current state and perspectives related to the polyethylene terephthalate hydrolases available for biorecycling. ACS Sustain. Chem. Eng. 8, 8894â8908 (2020).
DeFrancesco, L. Closing the recycling circle. Nat. Biotechnol. 38, 665â668 (2020).
Zhu, B., Wang, D. & Wei, N. Enzyme discovery and engineering for sustainable plastic recycling. Trends Biotechnol. 40, 22â37 (2022).
Lu, H. et al. Machine learning-aided engineering of hydrolases for PET depolymerization. Nature 604, 662â667 (2022).
Tournier, V. et al. An engineered PET depolymerase to break down and recycle plastic bottles. Nature 580, 216â219 (2020).
Joo, S. et al. Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation. Nat. Commun. 9, 382 (2018).
Jin, M., Gai, Y., Guo, X., Hou, Y. & Zeng, R. Properties and applications of extremozymes from deep-sea extremophilic microorganisms: a mini review. Mar. Drugs 17, 656 (2019).
Schmidt, T. S. B. et al. SPIRE: a Searchable, Planetary-scale mIcrobiome REsource. Nucleic Acids Res. 52, D777âD783 (2023).
Collins, S., Boyd, P. W. & Doblin, M. A. Evolution, microbes, and changing ocean conditions. Annu. Rev. Mar. Sci. 12, 181â208 (2020).
Cordero, O. X. & Polz, M. F. Explaining microbial genomic diversity in light of evolutionary ecology. Nat. Rev. Microbiol. 12, 263â273 (2014).
Pursey, E., Dimitriu, T., Paganelli, F. L., Westra, E. R. & van Houte, S. CRISPRâCas is associated with fewer antibiotic resistance genes in bacterial pathogens. Philos. Trans. R Soc. B 377, 20200464 (2022).
Kaminski, M. M., Abudayyeh, O. O., Gootenberg, J. S., Zhang, F. & Collins, J. J. CRISPR-based diagnostics. Nat. Biomed. Eng. 5, 643â656 (2021).
Jacinto, F. V., Link, W. & Ferreira, B. I. CRISPR/Cas9-mediated genome editing: from basic research to translational medicine. J. Cell. Mol. Med. 24, 3766â3778 (2020).
Saati-Santamaria, Z., Selem-Mojica, N., Peral-Aranega, E., Rivas, R. & Garcia-Fraile, P. Unveiling the genomic potential of Pseudomonas type strains for discovering new natural products. Microb. Genom. 8, 000758 (2022).
Belknap, K. C., Park, C. J., Barth, B. M. & Andam, C. P. Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria. Sci. Rep. 10, 2003 (2020).
Yan, S., Zeng, M., Wang, H. & Zhang, H. Micromonospora: a prolific source of bioactive secondary netabolites with therapeutic potential. J. Med. Chem. 65, 8735â8771 (2022).
Szymczak, P. et al. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat. Commun. 14, 1453 (2023).
Tully, B. J., Wheat, C. G., Glazer, B. T. & Huber, J. A. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 12, 1â16 (2018).
Galambos, D., Anderson, R. E., Reveillaud, J. & Huber, J. A. Genome-resolved metagenomics and metatranscriptomics reveal niche differentiation in functionally redundant microbial communities at deep-sea hydrothermal vents. Environ. Microbiol. 21, 4395â4410 (2019).
Dombrowski, N., Seitz, K. W., Teske, A. P. & Baker, B. J. Genomic insights into potential interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal sediments. Microbiome 5, 106 (2017).
Reysenbach, A. L. et al. Complex subsurface hydrothermal fluid mixing at a submarine arc volcano supports distinct and highly diverse microbial communities. Proc. Natl Acad. Sci. USA 117, 32627â32638 (2020).
Konstantinidis, K. T., Braff, J., Karl, D. M. & DeLong, E. F. Comparative metagenomic analysis of a microbial community residing at a depth of 4,000 meters at station ALOHA in the North Pacific subtropical gyre. Appl. Environ. Microbiol. 75, 5345â5355 (2009).
Pelve, E. A., Fontanez, K. M. & DeLong, E. F. Bacterial succession on sinking particles in the oceanâs interior. Front. Microbiol. 8, 2269 (2017).
Kato, S., Hirai, M., Ohkuma, M. & Suzuki, K. Microbial metabolisms in an abyssal ferromanganese crust from the Takuyo-Daigo Seamount as revealed by metagenomics. PLoS ONE 14, e0224888 (2019).
Buongiorno, J., Sipes, K., Wasmund, K., Loy, A. & Lloyd, K. G. Woeseiales transcriptional response to shallow burial in Arctic fjord surface sediment. PLoS ONE 15, e0234839 (2020).
Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat. Microbiol. 4, 2090â2100 (2019).
De Corte, D. et al. Viral communities in the global deep ocean conveyor belt assessed by targeted viromics. Front. Microbiol. 10, 1801 (2019).
Rinke, C. et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 13, 663â675 (2019).
Martin-Cuadrado, A. B. et al. A new class of marine Euryarchaeota group II from the Mediterranean deep chlorophyll maximum. ISME J. 9, 1619â1634 (2015).
Fuchsman, C. A., Devol, A. H., Saunders, J. K., McKay, C. & Rocap, G. Niche partitioning of the N cycling microbial community of an offshore oxygen deficient zone. Front. Microbiol. 8, 2384 (2017).
Haro-Moreno, J. M., Rodriguez-Valera, F. & Lopez-Perez, M. Prokaryotic population dynamics and viral predation in a marine succession experiment using metagenomics. Front. Microbiol. 10, 2926 (2019).
Pascoal, F. et al. Inter-comparison of marine microbiome sampling protocols. ISME Commun. 3, 84 (2023).
Raes, E. J., Bodrossy, L., van de Kamp, J., Bissett, A. & Waite, A. M. Marine bacterial richness increases towards higher latitudes in the eastern Indian Ocean. Limnol. Oceanogr. Lett. 3, 10â19 (2017).
Schreiber, L. et al. Potential for microbially mediated natural attenuation of diluted bitumen on the coast of British Columbia (Canada). Appl. Environ. Microbiol. 85, e00086-19 (2019).
Biller, S. J. et al. Marine microbial metagenomes sampled across space and time. Sci. Data 5, 180176 (2018).
Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).
Tremblay, J. et al. Metagenomic and metatranscriptomic responses of natural oil degrading bacteria in the presence of dispersants. Environ. Microbiol. 21, 2307â2319 (2019).
Anstett, J. et al. A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters. Sci. Data 10, 332 (2023).
Diez, B. et al. Metagenomic analysis of the Indian Ocean picocyanobacterial community: structure, potential function and evolution. PLoS ONE 11, e0155757 (2016).
Orsi, W. D. et al. Climate oscillations reflected within the microbiome of Arabian Sea sediments. Sci. Rep. 7, 6040 (2017).
Murray, A. E. et al. Discovery of an Antarctic ascidian-associated uncultivated Verrucomicrobia with antimelanoma palmerolide biosynthetic potential. mSphere 6, e0075921 (2021).
Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc. Natl Acad. Sci. USA 116, 11824â11832 (2019).
Zheng, T. et al. Mining, analyzing, and integrating viral signals from metagenomic data. Microbiome 7, 42 (2019).
Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the oceanâs euphotic zone. Proc. Natl Acad. Sci. USA 114, 11446â11451 (2017).
Fernandes, S. et al. Enhanced carbon-sulfur cycling in the sediments of Arabian Sea oxygen minimum zone center. Sci. Rep. 8, 8665 (2018).
Markussen, T. et al. Coupling biogeochemical process rates and metagenomic blueprints of coastal bacterial assemblages in the context of environmental change. Environ. Microbiol. 20, 3083â3099 (2018).
Duarte, C. M. et al. Sequencing effort dictates gene discovery in marine microbial metagenomes. Environ. Microbiol. 22, 4589â4603 (2020).
Yoshitake, K. et al. Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan. Sci. Rep. 11, 12222 (2021).
Abdel-Ghaffar, F. et al. Morphological and molecular biological characterization of Pleistophora aegyptiaca sp. nov. infecting the Red Sea fish Saurida tumbil. Parasitol. Res. 110, 741â752 (2012).
Atlas, R. M. et al. Oil biodegradation and oil-degrading microbial populations in marsh sediments impacted by oil from the Deepwater Horizon well blowout. Environ. Sci. Technol. 49, 8356â8366 (2015).
Hauptmann, A. L. et al. Contamination of the Arctic reflected in microbial metagenomes from the Greenland ice sheet. Environ. Res. Lett. 12, 074019 (2017).
Botte, E. S. et al. Future ocean conditions induce necrosis, microbial dysbiosis and nutrient cycling imbalance in the reef sponge Stylissa flabelliformis. ISME Commun. 3, 53 (2023).
Thompson, L. R. et al. Metagenomic covariation along densely sampled environmental gradients in the Red Sea. ISME J. 11, 138â151 (2017).
Hilton, J. A., Satinsky, B. M., Doherty, M., Zielinski, B. & Zehr, J. P. Metatranscriptomics of N2-fixing cyanobacteria in the Amazon River plume. ISME J. 9, 1557â1569 (2015).
Nilsson, E. et al. Genomic and seasonal variations among aquatic phages infecting the Baltic Sea Gammaproteobacterium Rheinheimera sp. Strain BAL341. Appl. Environ. Microbiol. 85, e01003âe01019 (2019).
Glasl, B. et al. Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes. ISME J. 14, 1435â1450 (2020).
Abdou, Y. T. et al. Characterization of a novel peptide mined from the Red Sea brine pools and modified to enhance its anticancer activity. BMC Cancer 23, 699 (2023).
Romero Picazo, D. et al. Horizontally transmitted symbiont populations in deep-sea mussels are genetically isolated. ISME J. 13, 2954â2968 (2019).
Saw, J. H. W. et al. Pangenomics analysis reveals diversification of enzyme families and niche specialization in globally abundant SAR202 bacteria. mBio 11, e02975â19 (2020).
St John, E., Flores, G. E., Meneghin, J. & Reysenbach, A. L. Deep-sea hydrothermal vent metagenome-assembled genomes provide insight into the phylum Nanoarchaeota. Environ. Microbiol. Rep. 11, 262â270 (2019).
Anantharaman, K. et al. Sulfur oxidation genes in diverse deep-sea viruses. Science 344, 757â760 (2014).
Niemann, H. et al. Novel microbial communities of the Haakon Mosby mud volcano and their role as a methane sink. Nature 443, 854â858 (2006).
Jungbluth, S. P., Bowers, R. M., Lin, H. T., Cowen, J. P. & Rappe, M. S. Novel microbial assemblages inhabiting crustal fluids within mid-ocean ridge flank subsurface basalt. ISME J. 10, 2033â2047 (2016).
Lopez-Perez, M., Haro-Moreno, J. M., Gonzalez-Serrano, R., Parras-Molto, M. & Rodriguez-Valera, F. Genome diversity of marine phages recovered from Mediterranean metagenomes: Size matters. PLoS Genet. 13, e1007018 (2017).
Dombrowski, N., Teske, A. P. & Baker, B. J. Expansive microbial metabolic versatility and biodiversity in dynamic Guaymas Basin hydrothermal sediments. Nat. Commun. 9, 4999 (2018).
Liu, J. et al. Proliferation of hydrocarbon-degrading microbes at the bottom of the Mariana Trench. Microbiome 7, 47 (2019).
Yu, H. et al. Comparative genomics and proteomic analysis of assimilatory sulfate reduction pathways in anaerobic methanotrophic archaea. Front. Microbiol. 9, 2917 (2018).
Backstrom, D. et al. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism. mBio 10, e02497-18 (2019).
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, 1674â1676 (2015).
Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAPâa flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
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, 1043â1055 (2015).
Mattock, J. & Watson, M. A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination. Nat. Methods 20, 1170â1173 (2023).
Kitts, P. A. et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 44, D73âD80 (2016).
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, 2864â2868 (2017).
Parks, D. H. et al. A complete domain-to-species taxonomy for bacteria and archaea. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0501-8 (2020).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2âapproximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256âW259 (2019).
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068â2069 (2014).
Russel, J., Pinilla-Redondo, R., Mayo-Munoz, D., Shah, S. A. & Sorensen, S. J. CRISPRCasTyper: automated identification, annotation, and classification of CRISPRâCas loci. CRISPR J. 3, 462â469 (2020).
Yang, B., Zheng, J. & Yin, Y. AcaFinder: genome mining for anti-CRISPR-associated genes. mSystems 7, e0081722 (2022).
Sauer, D. B. & Wang, D. N. Predicting the optimal growth temperatures of prokaryotes using only genome derived features. Bioinformatics 35, 3224â3231 (2019).
Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. 40, 1341â1348 (2022).
Johansson, M. H. K. et al. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J. Antimicrob. Chemother. 76, 101â109 (2021).
Pinilla-Redondo, R. et al. Discovery of multiple anti-CRISPRs highlights anti-defense gene clustering in mobile genetic elements. Nat. Commun. 11, 5652 (2020).
Mahendra, C. et al. Broad-spectrum anti-CRISPR proteins facilitate horizontal gene transfer. Nat Microbiol 5, 620â629 (2020).
Mohanraju, P. et al. Alternative functions of CRISPRâCas systems in the evolutionary arms race. Nat. Rev. Microbiol. 20, 351â364 (2022).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583â589 (2021).
Li, Z. et al. DNB-based on-chip motif finding: a high-throughput method to profile different types of proteinâDNA interactions. Sci. Adv. 6, eabb3350 (2020).
Wagih, O. ggseqlogo: a versatile R package for drawing sequence logos. Bioinformatics 33, 3645â3647 (2017).
Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192â197 (2015).
Weber, L. et al. Editing a γ-globin repressor binding site restores fetal hemoglobin synthesis and corrects the sickle cell disease phenotype. Sci. Adv. 6, eaay9392 (2020).
Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224â226 (2019).
Medema, M. H. et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 39, W339âW346 (2011).
Kautsar, S. A., van der Hooft, J. J. J., de Ridder, D. & Medema, M. H. BiG-SLiCE: a highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters. Gigascience 10, giaa154 (2021).
Kautsar, S. A., Blin, K., Shaw, S., Weber, T. & Medema, M. H. BiG-FAM: the biosynthetic gene cluster families database. Nucleic Acids Res. 49, D490âD497 (2021).
Ma, Y. et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat. Biotechnol. 40, 921â931 (2022).
Humphries, R. M. et al. CLSI methods development and standardization working group best practices for evaluation of antimicrobial susceptibility tests. J. Clin. Microbiol. 56, e01934â17 (2018).
Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).
Steinegger, M. & Soding, J. Clustering huge protein sequence sets in linear time. Nat. Commun. 9, 2542 (2018).
Coelho, L. P. et al. Towards the biogeography of prokaryotic genes. Nature 601, 252â256 (2022).
Liao, S. et al. Deciphering the microbial taxonomy and functionality of two diverse mangrove ecosystems and their potential abilities to produce bioactive compounds. mSystems 5, e00851â19 (2020).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59â60 (2014).
Yoshida, S. et al. A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351, 1196â1199 (2016).
Han, X. et al. Structural insight into catalytic mechanism of PET hydrolase. Nat. Commun. 8, 2106 (2017).
Danso, D. et al. New insights into the function and global distribution of polyethylene terephthalate (PET)-degrading bacteria and enzymes in marine and terrestrial metagenomes. Appl. Environ. Microbiol. 84, e02773-17 (2018).
Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420â423 (2019).
Erickson, E. et al. Sourcing thermotolerant poly(ethylene terephthalate) hydrolase scaffolds from natural diversity. Nat. Commun. 13, 7850 (2022).
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547â1549 (2018).
Robert, X. & Gouet, P. Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res. 42, W320âW324 (2014).
Liu, K. et al. A dual fluorescence assay enables high-throughput screening for poly(ethylene terephthalate) hydrolases. ChemSusChem 16, e202202019 (2022).
Cui, Y. et al. Computational redesign of a PETase for plastic biodegradation under ambient condition by the GRAPE strategy. ACS Catal. 11, 1340â1350 (2021).