1. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res 35, 7188–7196 (2007).
2. Glöckner, J. et al. Phylogenetic diversity and metagenomics of candidate division OP3. Environ Microbiol 12, 1218–1229 (2010).
3. Hugenholtz, P., Pitulle, C., Hershberger, K. L. & Pace, N. R. Novel Division Level Bacterial Diversity in a Yellowstone Hot Spring. Journal of Bacteriology 180, 366–376 (1998).
4. Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).
5. Williams, T. J., Allen, M. A., Berengut, J. F. & Cavicchioli, R. Shedding Light on Microbial “Dark Matter”: Insights Into Novel Cloacimonadota and Omnitrophota From an Antarctic Lake. Frontiers in Microbiology 12, 2947 (2021).
6. Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J 11, 2319–2333 (2017).
7. Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2, 1533–1542 (2017).
8. Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6, 1–18 (2018).
9. Vavourakis, C. D. et al. Metagenomes and metatranscriptomes shed new light on the microbial-mediated sulfur cycle in a Siberian soda lake. BMC Biol 17, 1–20 (2019).
10. Seitz, K. W., Lazar, C. S., Hinrichs, K.-U., Teske, A. P. & Baker, B. J. Genomic reconstruction of a novel, deeply branched sediment archaeal phylum with pathways for acetogenesis and sulfur reduction. ISME J 10, 1696–1705 (2016).
11. Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
12. Seitz, K. W. et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun 10, 1822 (2019).
13. Zhou, Z. et al. Genome- and Community-Level Interaction Insights into Carbon Utilization and Element Cycling Functions of Hydrothermarchaeota in Hydrothermal Sediment. mSystems 5, (2020).
14. Vigneron, A. et al. Genomic evidence for sulfur intermediates as new biogeochemical hubs in a model aquatic microbial ecosystem. Microbiome 9, 1–14 (2021).
15. Nobu, M. K. et al. Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses. Microbiome 8, 111 (2020).
16. Tian, R. et al. Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome 8, 51 (2020).
17. Pedron, R. et al. Genomic and metagenomic insights into the microbial community of a thermal spring. Microbiome 7, 8 (2019).
18. Coutinho, F. H. et al. New viral biogeochemical roles revealed through metagenomic analysis of Lake Baikal. Microbiome 8, 163 (2020).
19. Buck, M. et al. Comprehensive dataset of shotgun metagenomes from oxygen stratified freshwater lakes and ponds. Sci Data 8, 131 (2021).
20. Alteio, L. V. et al. Complementary Metagenomic Approaches Improve Reconstruction of Microbial Diversity in a Forest Soil. mSystems 5, e00768-19 (2020).
21. Suzuki, S., Nealson, K. H. & Ishii, S. Genomic and in-situ Transcriptomic Characterization of the Candidate Phylum NPL-UPL2 From Highly Alkaline Highly Reducing Serpentinized Groundwater. Front Microbiol 9, 3141 (2018).
22. Breister, A. M. et al. Soil microbiomes mediate degradation of vinyl ester-based polymer composites. Commun Mater 1, 1–15 (2020).
23. Campanaro, S. et al. New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters. Biotechnol Biofuels 13, 25 (2020).
24. Probst, A. J. et al. Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nature Microbiology 3, 328–336 (2018).
25. Kolinko, S. et al. Single-cell analysis reveals a novel uncultivated magnetotactic bacterium within the candidate division OP3. Environmental Microbiology 14, 1709–1721 (2012).
26. Kolinko, S., Richter, M., Glöckner, F.-O., Brachmann, A. & Schüler, D. Single-cell genomics of uncultivated deep-branching magnetotactic bacteria reveals a conserved set of magnetosome genes. Environmental Microbiology 18, 21–37 (2016).
27. Kizina, J. Insights into the biology of Candidate Division OP3 LiM populations. (2017).
28. Rotaru, A.-E., Schauer, R., Probian, C., Mussmann, M. & Harder, J. Visualization of candidate division OP3 cocci in limonene-degrading methanogenic cultures. J. Microbiol. Biotechnol. 22, 457–461 (2012).
29. Suominen, S., Dombrowski, N., Sinninghe Damsté, J. S. & Villanueva, L. A diverse uncultivated microbial community is responsible for organic matter degradation in the Black Sea sulphidic zone. Environ Microbiol 23, 2709–2728 (2021).
30. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9, 1–8 (2018).
31. 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).
32. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36, 996–1004 (2018).
33. Na, S.-I. et al. UBCG: Up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction. J Microbiol. 56, 280–285 (2018).
34. Ankenbrand, M. J. & Keller, A. bcgTree: automatized phylogenetic tree building from bacterial core genomes. Genome 59, 783–791 (2016).
35. Eloe-Fadrosh, E. A. et al. Global metagenomic survey reveals a new bacterial candidate phylum in geothermal springs. Nature Communications 7, 10476 (2016).
36. 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).
37. Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing Cultivation To Identify Bacterial Species: Culture-independent genomic approaches identify credibly distinct clusters, avoid cultivation bias, and provide true insights into microbial species. Microbe Magazine 9, 111–118 (2014).
38. Murray, A. E. et al. Roadmap for naming uncultivated Archaea and Bacteria. Nature Microbiology 5, 987–994 (2020).
39. Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857 (2019).
40. Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
41. Proctor, C. R. et al. Phylogenetic clustering of small low nucleic acid-content bacteria across diverse freshwater ecosystems. ISME J 12, 1344–1359 (2018).
42. Vigneron, A. et al. Ultra-small and abundant: Candidate phyla radiation bacteria are potential catalysts of carbon transformation in a thermokarst lake ecosystem. Limnology and Oceanography Letters 5, 212–220 (2020).
43. Beam, J. P. et al. Ancestral Absence of Electron Transport Chains in Patescibacteria and DPANN. Front. Microbiol. 11, (2020).
44. Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nature Communications 7, 13219 (2016).
45. Hug, L. A. et al. Aquifer environment selects for microbial species cohorts in sediment and groundwater. ISME J 9, 1846–1856 (2015).
46. Sabath, N., Ferrada, E., Barve, A. & Wagner, A. Growth Temperature and Genome Size in Bacteria Are Negatively Correlated, Suggesting Genomic Streamlining During Thermal Adaptation. Genome Biology and Evolution 5, 966–977 (2013).
47. Zeldovich, K. B., Berezovsky, I. N. & Shakhnovich, E. I. Protein and DNA Sequence Determinants of Thermophilic Adaptation. PLOS Computational Biology 3, e5 (2007).
48. Sato, Y., Okano, K., Kimura, H. & Honda, K. TEMPURA: Database of Growth TEMPeratures of Usual and RAre Prokaryotes. Microbes Environ 35, ME20074 (2020).
49. Dittrich, C. R., Bennett, G. N. & San, K.-Y. Characterization of the Acetate-Producing Pathways in Escherichia coli. Biotechnology Progress 21, 1062–1067 (2005).
50. Crowley, P. J., Gutierrez, J. A., Hillman, J. D. & Bleiweis, A. S. Genetic and physiologic analysis of a formyl-tetrahydrofolate synthetase mutant of Streptococcus mutans. Journal of Bacteriology 179, 1563–1572 (1997).
51. Hesslinger, C., Fairhurst, S. A. & Sawers, G. Novel keto acid formate-lyase and propionate kinase enzymes are components of an anaerobic pathway in Escherichia coli that degrades L-threonine to propionate. Mol Microbiol 27, 477–492 (1998).
52. Tang, Y.-Q., Shigematsu, T., Morimura, S. & Kida, K. Effect of dilution rate on the microbial structure of a mesophilic butyrate-degrading methanogenic community during continuous cultivation. Appl Microbiol Biotechnol 75, 451–465 (2007).
53. Ljungdahl, L., Irion, E. & Wood, H. G. Total Synthesis of Acetate from CO2. I. Co-Methylcobyric Acid and Co-(Methyl)-5-methoxybenzimidazolylcobamide as Intermediates with Clostridium thermoaceticum*. Biochemistry 4, 2771–2780 (1965).
54. Westphal, L., Wiechmann, A., Baker, J., Minton, N. P. & Müller, V. The Rnf Complex Is an Energy-Coupled Transhydrogenase Essential To Reversibly Link Cellular NADH and Ferredoxin Pools in the Acetogen Acetobacterium woodii. Journal of Bacteriology 200, (2018).
55. Kuhns, M., Trifunović, D., Huber, H. & Müller, V. The Rnf complex is a Na + coupled respiratory enzyme in a fermenting bacterium, Thermotoga maritima. Communications Biology 3, 1–10 (2020).
56. Loh, H. Q., Hervé, V. & Brune, A. Metabolic Potential for Reductive Acetogenesis and a Novel Energy-Converting [NiFe] Hydrogenase in Bathyarchaeia From Termite Guts – A Genome-Centric Analysis. Front. Microbiol. 11, (2021).
57. Greening, C. et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J 10, 761–777 (2016).
58. Weidner, U. et al. The Gene Locus of the Proton-translocating NADH : Ubiquinone Oxidoreductase in Escherichia coli: Organization of the 14 Genes and Relationship Between the Derived Proteins and Subunits of Mitochondrial Complex I. Journal of Molecular Biology 233, 109–122 (1993).
59. Kim, Y. J. et al. Formate-driven growth coupled with H2 production. Nature 467, 352–355 (2010).
60. Baughn, A. D. & Malamy, M. H. The strict anaerobe Bacteroides fragilis grows in and benefits from nanomolar concentrations of oxygen. Nature 427, 441–444 (2004).
61. Garber, A. I. et al. FeGenie: A Comprehensive Tool for the Identification of Iron Genes and Iron Gene Neighborhoods in Genome and Metagenome Assemblies. Front. Microbiol. 11, (2020).
62. Deng, X., Dohmae, N., Nealson, K. H., Hashimoto, K. & Okamoto, A. Multi-heme cytochromes provide a pathway for survival in energy-limited environments. Science Advances 4, eaao5682 (2018).
63. McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).
64. Walker, D. J. et al. Electrically conductive pili from pilin genes of phylogenetically diverse microorganisms. The ISME Journal 12, 48–58 (2018).
65. Poehlein, A. et al. An Ancient Pathway Combining Carbon Dioxide Fixation with the Generation and Utilization of a Sodium Ion Gradient for ATP Synthesis. PLoS One 7, (2012).
66. Moreira, D., Zivanovic, Y., López-Archilla, A. I., Iniesto, M. & López-García, P. Reductive evolution and unique predatory mode in the CPR bacterium Vampirococcus lugosii. Nat Commun 12, 2454 (2021).
67. Evans, K. J., Lambert, C. & Sockett, R. E. Predation by Bdellovibrio bacteriovorus HD100 Requires Type IV Pili. J Bacteriol 189, 4850–4859 (2007).
68. Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J 8, 625–635 (2014).
69. Tulum, I., Kimura, K. & Miyata, M. Identification and sequence analyses of the gliding machinery proteins from Mycoplasma mobile. Scientific Reports 10, 3792 (2020).
70. Tulum, I., Yabe, M., Uenoyama, A. & Miyata, M. Localization of P42 and F1-ATPase α-Subunit Homolog of the Gliding Machinery in Mycoplasma mobile Revealed by Newly Developed Gene Manipulation and Fluorescent Protein Tagging. Journal of Bacteriology 196, 1815–1824 (2014).
71. Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208–211 (2015).
72. Kindaichi, T. et al. Phylogenetic diversity and ecophysiology of Candidate phylum Saccharibacteria in activated sludge. FEMS Microbiology Ecology 92, (2016).
73. Miller, I. J., Weyna, T. R., Fong, S. S., Lim-Fong, G. E. & Kwan, J. C. Single sample resolution of rare microbial dark matter in a marine invertebrate metagenome. Scientific Reports 6, 34362 (2016).
74. Yeoh, Y. K., Sekiguchi, Y., Parks, D. H. & Hugenholtz, P. Comparative Genomics of Candidate Phylum TM6 Suggests That Parasitism Is Widespread and Ancestral in This Lineage. Mol Biol Evol 33, 915–927 (2016).
75. Schmitz-Esser, S. et al. ATP/ADP Translocases: a Common Feature of Obligate Intracellular Amoebal Symbionts Related to Chlamydiae and Rickettsiae. Journal of Bacteriology 186, 683–691 (2004).
76. Horn, M. et al. Members of the Cytophaga-Flavobacterium-Bacteroides phylum as intracellular bacteria of acanthamoebae: proposal of ‘Candidatus Amoebophilus asiaticus’. Environ Microbiol 3, 440–449 (2001).
77. Pagnier, I. et al. Babela massiliensis, a representative of a widespread bacterial phylum with unusual adaptations to parasitism in amoebae. Biol Direct 10, (2015).
78. Reva, O. & Tümmler, B. Think big – giant genes in bacteria. Environmental Microbiology 10, 768–777 (2008).
79. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
80. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
81. Sakka, M., Kunitake, E., Kimura, T. & Sakka, K. Function of a laminin_G_3 module as a carbohydrate-binding module in an arabinofuranosidase from Ruminiclostridium josui. FEBS Lett 593, 42–51 (2019).
82. Itoh, Y. Discoidin domain receptors: Microenvironment sensors that promote cellular migration and invasion. Cell Adhesion & Migration 12, 378–385 (2018).
83. Peer, A., Smith, S. P., Bayer, E. A., Lamed, R. & Borovok, I. Noncellulosomal cohesin- and dockerin-like modules in the three domains of life. FEMS Microbiology Letters 291, 1–16 (2009).
84. Hamm, J. N. et al. Unexpected host dependency of Antarctic Nanohaloarchaeota. PNAS 116, 14661–14670 (2019).
85. Vogl, K. et al. Identification and analysis of four candidate symbiosis genes from ‘Chlorochromatium aggregatum’, a highly developed bacterial symbiosis. Environmental Microbiology 10, 2842–2856 (2008).
86. Liu, Z. et al. Genomic analysis reveals key aspects of prokaryotic symbiosis in the phototrophic consortium “Chlorochromatium aggregatum”. Genome Biol 14, 1–17 (2013).
87. Paul, B. G. et al. Retroelement-guided protein diversification abounds in vast lineages of Bacteria and Archaea. Nat Microbiol 2, 17045 (2017).
88. Hungate, B. A. et al. The Functional Significance of Bacterial Predators. mBio 12, (2021).
89. Castelle, C. J. et al. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nature Reviews Microbiology 16, 629–645 (2018).
90. Jarett, J. K. et al. Single-cell genomics of co-sorted Nanoarchaeota suggests novel putative host associations and diversification of proteins involved in symbiosis. Microbiome 6, 1–14 (2018).
91. Munson-McGee, J. H. et al. Nanoarchaeota, Their Sulfolobales Host, and Nanoarchaeota Virus Distribution across Yellowstone National Park Hot Springs. Applied and Environmental Microbiology 81, 7860–7868 (2015).
92. Cross, K. L. et al. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat Biotechnol 37, 1314–1321 (2019).
93. He, X. et al. Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci U S A 112, 244–249 (2015).
94. Stepanauskas, R. et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat Commun 8, 84 (2017).
95. 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).
96. Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010).
97. Eddy, S. R. Accelerated Profile HMM Searches. PLOS Computational Biology 7, e1002195 (2011).
98. 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).
99. 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).
100. Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Molecular Biology and Evolution 30, 772–780 (2013).
101. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Haeseler, A. von & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14, 587–589 (2017).
102. Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast Approximation for Phylogenetic Bootstrap. Mol Biol Evol 30, 1188–1195 (2013).
103. Barbera, P. et al. EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences. Syst Biol 68, 365–369 (2019).
104. Rodriguez-R, L. M. & Konstantinidis, K. T. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. https://peerj.com/preprints/1900 (2016) doi:10.7287/peerj.preprints.1900v1.
105. Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289–290 (2004).
106. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution 3, 217–223 (2012).
107. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods in Ecology and Evolution 8, 28–36 (2017).
108. Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics btz859, (2019).
109. Abby, S. S. & Rocha, E. P. C. Identification of Protein Secretion Systems in Bacterial Genomes Using MacSyFinder. Methods Mol. Biol. 1615, 1–21 (2017).
110. Abby, S. S. et al. Identification of protein secretion systems in bacterial genomes. Sci Rep 6, 1–14 (2016).
111. Zhou, Z., Tran, P., Liu, Y., Kieft, K. & Anantharaman, K. METABOLIC: A scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. bioRxiv 761643 (2019) doi:10.1101/761643.
112. Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305, 567–580 (2001).
113. Bateman, A. et al. The Pfam protein families database. Nucleic Acids Research 32, D138–D141 (2004).
114. Sonnhammer, E. L. L., Eddy, S. R. & Durbin, R. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins: Structure, Function, and Bioinformatics 28, 405–420 (1997).
115. Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res 31, 371–373 (2003).
116. Bengtsson-Palme, J. et al. METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Mol Ecol Resour 15, 1403–1414 (2015).
117. Chan, P. P. & Lowe, T. M. tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences. Methods Mol Biol 1962, 1–14 (2019).
118. Kalvari, I. et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research 49, D192–D200 (2021).
119. Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
120. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
121. Lane, D. 16S/23S rRNA Sequencing. in E. Stackebrandt and M. Goodfellow (ed.), Nucleic acid techniques in bacterial systematics 115–175 (John Wiley and Sons, 1991).
122. Schloss, P. D. et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
123. Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
124. Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108, 4516–4522 (2011).
125. Robeson, M. S. et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses. bioRxiv 2020.10.05.326504 (2020) doi:10.1101/2020.10.05.326504.
126. Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquatic Microbial Ecology 75, 129–137 (2015).
127. Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environmental Microbiology 18, 1403–1414 (2016).
128. Hungate, B. A. et al. Quantitative Microbial Ecology through Stable Isotope Probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).
129. Purcell, A. M. et al. Quantitative stable isotope probing with H218O to measure taxon-specific microbial growth. Siol Science Society of America Journal 84, 1503–1518 (2020).