Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).
Tully, B. J., Sachdeva, R., Graham, E. D. & Heidelberg, J. F. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ 5, e3558 (2017).
Biller, S. J. et al. Marine microbial metagenomes sampled across space and time. Sci. Data 5, 180176 (2018).
Paoli, L. et al. Biosynthetic potential of the global ocean microbiome. Nature 607, 111–118 (2022).
Nishimura, Y. & Yoshizawa, S. The OceanDNA MAG catalog contains over 50,000 prokaryotic genomes originated from various marine environments. Sci. Data 9, 305 (2022).
Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).
Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).
Roberts, C. M. et al. Marine biodiversity hotspots and conservation priorities for tropical reefs. Science 295, 1280–1284 (2002).
Haro-Moreno, J. M. et al. Fine metagenomic profile of the Mediterranean stratified and mixed water columns revealed by assembly and recruitment. Microbiome 6, 128 (2018).
Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean. Cell Genom. 2, 100123 (2022).
Gaïa, M. et al. Mirusviruses link herpesviruses to giant viruses. Nature 616, 783–789 (2023).
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).
Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123.e14 (2019).
Duncan, A. et al. Metagenome-assembled genomes of phytoplankton microbiomes from the Arctic and Atlantic Oceans. Microbiome 10, 67 (2022).
Alexander, H. et al. Eukaryotic genomes from a global metagenomic data set illuminate trophic modes and biogeography of ocean plankton. mBio 14, e01676-23 (2023).
Laiolo, E. et al. Metagenomic probing toward an atlas of the taxonomic and metabolic foundations of the global ocean genome. Front. Sci. 1, 1038696 (2024).
Planes, S. et al. The Tara Pacific expedition—a pan-ecosystemic approach of the “-omics” complexity of coral reef holobionts across the Pacific Ocean. PLoS Biol. 17, e3000483 (2019).
Lombard, F. et al. Open science resources from the Tara Pacific expedition across coral reef and surface ocean ecosystems. Sci. Data 10, 324 (2023).
Sereika, M. et al. Oxford Nanopore R10.4 long-read sequencing enables the generation of near-finished bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. Nat. Methods 19, 823–826 (2022).
Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife 8, e46497 (2019).
Huber, H., Hohn, M. J., Stetter, K. O. & Rachel, R. The phylum Nanoarchaeota: present knowledge and future perspectives of a unique form of life. Res. Microbiol. 154, 165–171 (2003).
Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 39, 727–736 (2021).
Browne, P. D. et al. GC bias affects genomic and metagenomic reconstructions, underrepresenting GC-poor organisms. GigaScience 9, giaa008 (2020).
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).
Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).
Sowell, S. M. et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 3, 93–105 (2009).
Sosa, O. A., Repeta, D. J., DeLong, E. F., Ashkezari, M. D. & Karl, D. M. Phosphate-limited ocean regions select for bacterial populations enriched in the carbon–phosphorus lyase pathway for phosphonate degradation. Environ. Microbiol. 21, 2402–2414 (2019).
Moreno-Vivián, C., Cabello, P., Martínez-Luque, M., Blasco, R. & Castillo, F. Prokaryotic nitrate reduction: molecular properties and functional distinction among bacterial nitrate reductases. J. Bacteriol. 181, 6573–6584 (1999).
Benedetti-Cecchi, L. et al. Marine protected areas promote stability of reef fish communities under climate warming. Nat. Commun. 15, 1822 (2024).
Great Barrier Reef Marine Park Authority. Great Barrier Reef Marine Park Zoning Plan 2003 (Australian Government, 2004).
Terzin, M. et al. The road forward to incorporate seawater microbes in predictive reef monitoring. Environ. Microbiome 19, 5 (2024).
Apprill, A. & Salerno, J. L. Reef water microorganisms as diagnostic indicators for coral reef ecosystem management and sustainability. Cell Rep. Sustain. 2, 100403 (2025).
Viklund, J., Martijn, J., Ettema, T. J. G. & Andersson, S. G. E. Comparative and phylogenomic evidence that the Alphaproteobacterium HIMB59 is not a member of the oceanic SAR11 clade. PLoS ONE 8, e78858 (2013).
Roda-Garcia, J. J., Haro-Moreno, J. M., Rodriguez-Valera, F., Almagro-Moreno, S. & López-Pérez, M. Single-amplified genomes reveal most streamlined free-living marine bacteria. Environ. Microbiol. 25, 1136–1154 (2023).
Martinez-Gutierrez, C. A. & Aylward, F. O. Strong purifying selection is associated with genome streamlining in epipelagic Marinimicrobia. Genome Biol. Evol. 11, 2887–2894 (2019).
Grote, J. et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio 3, e00252-12 (2012).
Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).
Roach, T. N. F. et al. Microbial bioenergetics of coral–algal interactions. PeerJ 5, e3423 (2017).
Haas, A. F. et al. Global microbialization of coral reefs. Nat Microbiol 1, 16042 (2016).
Dinsdale, E. A. & Rohwer, F. in Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) 231–240 (Springer, 2011).
Lauro, F. M. et al. The genomic basis of trophic strategy in marine bacteria. Proc. Natl Acad. Sci. USA 106, 15527–15533 (2009).
Turner, D. et al. Abolishment of morphology-based taxa and change to binomial species names: 2022 taxonomy update of the ICTV bacterial viruses subcommittee. Arch. Virol. 168, 74 (2023).
Suttle, C. A. Marine viruses — major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).
Camargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat. Biotechnol. 42, 1303–1312 (2023).
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).
Tian, F. et al. Prokaryotic-virus-encoded auxiliary metabolic genes throughout the global oceans. Microbiome 12, 159 (2024).
Wang, T. et al. Characterization and genomic analysis of an oceanic cyanophage infecting marine Synechococcus reveal a novel genus. Front. Microbiol. 14, 1231279 (2023).
Du, S. et al. Genome sequences of the first Autographiviridae phages infecting marine Roseobacter. Microb. Genom. 10, 001240 (2024).
Sullivan, M. B., Coleman, M. L., Weigele, P., Rohwer, F. & Chisholm, S. W. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 3, e144 (2005).
Pope, W. H. et al. Genome sequence, structural proteins, and capsid organization of the cyanophage Syn5: a “horned” bacteriophage of marine Synechococcus. J. Mol. Biol. 368, 966–981 (2007).
Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).
Dutilh, B. E. et al. A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes. Nat. Commun. 5, 4498 (2014).
Guerin, E. et al. Biology and taxonomy of CrAss-like bacteriophages, the most abundant virus in the human gut. Cell Host Microbe 24, 653–664.e6 (2018).
Shkoporov, A. N. et al. ΦCrAss001 represents the most abundant bacteriophage family in the human gut and infects Bacteroides intestinalis. Nat. Commun. 9, 4781 (2018).
Yutin, N. et al. Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat. Microbiol. 3, 38–46 (2017).
Edwards, R. A. et al. Global phylogeography and ancient evolution of the widespread human gut virus crAssphage. Nat. Microbiol. 4, 1727–1736 (2019).
Yutin, N. et al. Analysis of metagenome-assembled viral genomes from the human gut reveals diverse putative CrAss-like phages with unique genomic features. Nat. Commun. 12, 1044 (2021).
Papudeshi, B. et al. Host interactions of novel Crassvirales species belonging to multiple families infecting bacterial host, Bacteroides cellulosilyticus WH2. Microb. Genom. 9, 001100 (2023).
Bartlau, N. et al. Highly diverse flavobacterial phages isolated from North Sea spring blooms. ISME J. 16, 555–568 (2022).
Piedade, G. J. et al. Seasonal dynamics and diversity of Antarctic marine viruses reveal a novel viral seascape. Nat. Commun. 15, 9192 (2024).
Li, W. K. W. Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: measurements from flow cytometric sorting. Limnol. Oceanogr. 39, 169–175 (1994).
Worden, A. Z., Nolan, J. K. & Palenik, B. Assessing the dynamics and ecology of marine picophytoplankton: the importance of the eukaryotic component. Limnol. Oceanogr. 49, 168–179 (2004).
Simmons, M. P. et al. Abundance and biogeography of picoprasinophyte ecotypes and other phytoplankton in the Eastern North Pacific Ocean. Appl. Environ. Microbiol. 82, 1693–1705 (2016).
Derelle, E. et al. Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc. Natl Acad. Sci. USA 103, 11647–11652 (2006).
Moreau, H. et al. Gene functionalities and genome structure in Bathycoccus prasinos reflect cellular specializations at the base of the green lineage. Genome Biol. 13, R74 (2012).
Palenik, B. et al. The tiny eukaryote Ostreococcus provides genomic insights into the paradox of plankton speciation. Proc. Natl Acad. Sci. USA 104, 7705–7710 (2007).
Joli, N., Monier, A., Logares, R. & Lovejoy, C. Seasonal patterns in Arctic prasinophytes and inferred ecology of Bathycoccus unveiled in an Arctic winter metagenome. ISME J. 11, 1372–1385 (2017).
Fernández-Gómez, B. et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 7, 1026–1037 (2013).
Hudson, J. & Egan, S. Opportunistic diseases in marine eukaryotes: could Bacteroidota be the next threat to ocean life? Environ. Microbiol. 24, 4505–4518 (2022).
Nayfach, S., Rodriguez-Mueller, B., Garud, N. & Pollard, K. S. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 26, 1612–1625 (2016).
Kazantseva, E., Donmez, A., Frolova, M., Pop, M. & Kolmogorov, M. Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing. Nat. Methods 21, 2034–2043 (2024).
Ma, B. et al. A genomic catalogue of soil microbiomes boosts mining of biodiversity and genetic resources. Nat. Commun. 14, 7318 (2023).
Gilbert, J. A. et al. Safeguarding microbial biodiversity: microbial conservation specialist group within the species survival commission of the International Union for Conservation of Nature. ISME J. 19, wraf239 (2025).
Terzin, M. et al. Gene content of seawater microbes is a strong predictor of water chemistry across the Great Barrier Reef. Microbiome 13, 11 (2025).
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
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).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Nissen, J. N. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotechnol. 39, 555–560 (2021).
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (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, 1043–1055 (2015).
Chklovski, A., Parks, D. H., Woodcroft, B. J. & Tyson, G. W. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat. Methods 20, 1203–1212 (2023).
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38, 5315–5316 (2022).
Lynch, T. P. et al. IMOS National Reference Stations: a continental-wide physical, chemical and biological coastal observing system. PLoS ONE 9, e113652 (2014).
Aroney, S. T. N. et al. CoverM: read alignment statistics for metagenomics. Bioinformatics 41, btaf147 (2025).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org/ (R Foundation for Statistical Computing, 2024).
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.6-8 https://CRAN.R-project.org/package=vegan (2024).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Nickols, W. A. et al. MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery. Nat. Methods 23, 554–564 (2026).
Larralde, M. Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. J. Open Source Softw. 7, 4296 (2022).
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).
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).
Rohart, F., Eslami, A., Matigian, N., Bougeard, S. & Lê Cao, K.-A. MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms. BMC Bioinform. 18, 128 (2017).
Lê Cao, K.-A., Boitard, S. & Besse, P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinform. 12, 253 (2011).
Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).
Lê Cao, K.-A., Martin, P. G., Robert-Granié, C. & Besse, P. Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinform. 10, 34 (2009).
Lê Cao, K.-A., Rossouw, D., Robert-Granié, C. & Besse, P. A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. Biol. 7, 35 (2008).
Antipov, D., Raiko, M., Lapidus, A. & Pevzner, P. A. MetaviralSPAdes: assembly of viruses from metagenomic data. Bioinformatics 36, 4126–4129 (2020).
Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).
Fang, Z. et al. PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. GigaScience 8, giz066 (2019).
Ren, J. et al. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8, 64–77 (2020).
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).
Pradier, L., Tissot, T., Fiston-Lavier, A.-S. & Bedhomme, S. PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets. BMC Bioinform. 22, 349 (2021).
Yu, M. K., Fogarty, E. C. & Eren, A. M. Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess. Nat. Microbiol. 9, 830–847 (2024).
Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).
Pronk, L. J. U. & Medema, M. H. Whokaryote: distinguishing eukaryotic and prokaryotic contigs in metagenomes based on gene structure. Microb. Genom. 8, mgen000823 (2022).
West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).
Brown, M. R., Manuel Gonzalez De La Rosa, P. & Blaxter, M. tidk: a toolkit to rapidly identify telomeric repeats from genomic datasets. Bioinformatics 41, btaf049 (2025).
Bouras, G. et al. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics 39, btac776 (2023).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).
Nishimura, Y. et al. ViPTree: the viral proteomic tree server. Bioinformatics 33, 2379–2380 (2017).
Letunic, I. & Bork, P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res. 52, W78–W82 (2024).
Roux, S. et al. iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria. PLoS Biol. 21, e3002083 (2023).
Robbins, S. et al. Great Barrier Reef Microbial Genomes Database (GBR-MGD). Zenodo https://doi.org/10.5281/zenodo.20503966 (2026).

