Sharon I, Kertesz M, Hug LA, Pushkarev D, Blauwkamp TA, Castelle CJ, et al. Accurate, multi-kb reads resolve complex populations and detect rare microorganisms. Genome Res. 2015;25:534–43.
Google Scholar
Bankevich A, Pevzner PA. Joint analysis of long and short reads enables accurate estimates of microbiome complexity. Cell Syst. 2018;7:192–200.e3.
Google Scholar
Luo C, Tsementzi D, Kyrpides NC, Konstantinidis KT. Individual genome assembly from complex community short-read metagenomic datasets. ISME J. 2012;6:898–901.
Google Scholar
Lapidus AL, Korobeynikov AI. Metagenomic data assembly—the way of decoding unknown microorganisms. Front Microbiol. 2021;12:613791.
Google Scholar
Nielsen HB, Almeida M, Juncker AS, Rasmussen S, Li J, Sunagawa S, et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotech. 2014;32:822–8.
Google Scholar
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol. 2015;13:13–27.
Google Scholar
Crespo BG, Wallhead PJ, Logares R, Pedrós-Alió C. Probing the rare biosphere of the North-West Mediterranean Sea: an experiment with high sequencing effort. PLOS ONE. 2016;11:e0159195.
Google Scholar
Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.
Google Scholar
Pedrós-Alió C. Dipping into the rare biosphere. Science. 2007;315:192–3.
Google Scholar
Sauret C, Séverin T, Vétion G, Guigue C, Goutx M, Pujo-Pay M, et al. ‘Rare biosphere’ bacteria as key phenanthrene degraders in coastal seawaters. Environmental Pollution. 2014;194:246–53.
Google Scholar
Kalenitchenko D, Le Bris N, Peru E, Galand PE. Ultra-rare marine microbes contribute to key sulfur related ecosystem functions. Mol Ecol. 2018;27:1494–504.
Google Scholar
Capo E, Debroas D, Arnaud F, Guillemot T, Bichet V, Millet L, et al. Long-term dynamics in microbial eukaryotes communities: a palaeolimnological view based on sedimentary DNA. Mol Ecol. 2016;25:5925–43.
Google Scholar
Lynch MDJ, Neufeld JD. Ecology and exploration of the rare biosphere. Nat Rev Micro. 2015;13:217–29.
Google Scholar
Debroas D, Hugoni M, Domaizon I. Evidence for an active rare biosphere within freshwater protists community. Mol Ecol. 2015;24:1236–47.
Google Scholar
Banerjee S, Schlaeppi K, Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.
Google Scholar
Herren CM, McMahon KD. Keystone taxa predict compositional change in microbial communities. Environ Microbiol. 2018;20:2207–17.
Google Scholar
Hugoni M, Taib N, Debroas D, Domaizon I, Dufournel IJ, Bronner G, et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. PNAS. 2013;110:6004–9.
Google Scholar
Debroas D, Domaizon I, Humbert J-F, Jardillier L, Lepère C, Oudart A, et al. Overview of freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding data. FEMS Microbiol Ecol. 2017;93:1.
Google Scholar
Elshahed MS, Youssef NH, Spain AM, Sheik C, Najar FZ, Sukharnikov LO, et al. Novelty and uniqueness patterns of rare members of the soil biosphere. Appl Environ Microbiol. 2008;74:5422–8.
Google Scholar
Pascoal F, Magalhães C, Costa R. The Link Between the Ecology of the Prokaryotic Rare Biosphere and Its Biotechnological Potential. Front Microbiol. 2020;11:231.
Google Scholar
Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng J-F, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.
Google Scholar
Delmont TO, Eren AM, Maccario L, Prestat E, Esen ÖC, Pelletier E, et al. Reconstructing rare soil microbial genomes using in situ enrichments and metagenomics. Front Microbiol. 2015;6:358.
Google Scholar
Sachdeva R, Campbell BJ, Heidelberg JF Rare microbes from diverse Earth biomes dominate community activity. bioRxiv 2019; 636373. https://doi.org/10.1101/636373.
Galand PE, Pereira O, Hochart C, Auguet JC, Debroas D. A strong link between marine microbial community composition and function challenges the idea of functional redundancy. ISME J. 2018;12:2470–8.
Google Scholar
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Google Scholar
Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.
Google Scholar
Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26:589–95.
Google Scholar
Ulyantsev VI, Kazakov SV, Dubinkina VB, Tyakht AV, Alexeev DG. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data. Bioinformatics. 2016;32:2760–7.
Google Scholar
Dixon P. VEGAN, a package of R functions for community ecology. J Vegetation Sci. 2003;14:927–30.
Google Scholar
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.
Google Scholar
Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nature methods. 2015;12:902–3.
Google Scholar
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biology. 2011;12:R60.
Google Scholar
The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017;45:D158–D169.
Google Scholar
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–D462.
Google Scholar
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Meth. 2015;12:59–60.
Google Scholar
Fernandes AD, Reid JN, Macklaim JM, McMurrough TA, Edgell DR, Gloor GB. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014;2:15.
Google Scholar
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:2224.
Google Scholar
Luo W, Friedman MS, Shedden K, Hankenson KD, Woolf PJ. GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics. 2009;10:161.
Google Scholar
Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29:1830–1.
Google Scholar
Rohart F, Gautier B, Singh A, Cao K-AL. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLOS Computational Biology. 2017;13:e1005752.
Google Scholar
Palarea-Albaladejo J, Martín-Fernández JA. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemometrics Intell Lab Syst. 2015;143:85–96.
Google Scholar
Plaza Oñate F, Le Chatelier E, Almeida M, Cervino ACL, Gauthier F, Magoulès F, et al. MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data. Bioinformatics. 2019;35:1544–52.
Google Scholar
Csardi G, Nepusz T. The Igraph Software Package for Complex Network Research. InterJournal 2006, Complex Systems, 1695.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
Google Scholar
Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48:1–18.
Google Scholar
Lambert S, Tragin M, Lozano J-C, Ghiglione J-F, Vaulot D, Bouget F-Y, et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 2019;13:388–401.
Google Scholar
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
Google Scholar
Galand PE, Casamayor EO, Kirchman DL, Lovejoy C. Ecology of the rare microbial biosphere of the Arctic Ocean. PNAS. 2009;106:22427–32.
Google Scholar
Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. Activity of abundant and rare bacteria in a Coastal Ocean. Proc Natl Acad Sci USA. 2011;108:12776–81.
Google Scholar
Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature. 2002;420:806–10.
Google Scholar
Bouvier T, del Giorgio PA. Key role of selective viral-induced mortality in determining marine bacterial community composition. Environ Microbiol. 2007;9:287–97.
Google Scholar
Thingstad TF, Våge S, Storesund JE, Sandaa R-A, Giske J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. Proc Natl Acad Sci USA. 2014;111:7813–8.
Google Scholar
Pedrós-Alió C. Marine microbial diversity: can it be determined? Trends Microbiol. 2006;14:257–63.
Google Scholar
Gobet A, Böer SI, Huse SM, van Beusekom JEE, Quince C, Sogin ML, et al. Diversity and dynamics of rare and of resident bacterial populations in coastal sands. ISME J. 2012;6:542–53.
Google Scholar
Pascoal F, Costa R, Assmy P, Duarte P, Magalhães C. Exploration of the types of rarity in the arctic ocean from the perspective of multiple methodologies. Microb Ecol. 2021;84:59–72.
Google Scholar
Huete-Stauffer TM, Arandia-Gorostidi N, Díaz-Pérez L, Morán XAG. Temperature dependences of growth rates and carrying capacities of marine bacteria depart from metabolic theoretical predictions. FEMS Microbiol Ecol. 2015;91:fiv111.
Google Scholar
Arandia-Gorostidi N, Huete-Stauffer TM, Alonso-Sáez L, G. Morán XA. Testing the metabolic theory of ecology with marine bacteria: different temperature sensitivity of major phylogenetic groups during the spring phytoplankton bloom. Environ Microbiol. 2017;19:4493–505.
Google Scholar
Giovannoni SJ, Bibbs L, Cho J-C, Stapels MD, Desiderio R, Vergin KL, et al. Proteorhodopsin in the ubiquitous marine bacterium SAR11. Nature. 2005;438:82–85.
Google Scholar
Yilmaz P, Yarza P, Rapp JZ, Glöckner FO. Expanding the world of marine bacterial and archaeal clades. Front Microbiol. 2016;6:1524.
Google Scholar
Pedler BE, Aluwihare LI, Azam F. Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean. Proc Natl Acad Sci USA. 2014;111:7202–7.
Google Scholar
Pereira O, Hochart C, Boeuf D, Auguet JC, Debroas D, Galand PE. Seasonality of archaeal proteorhodopsin and associated Marine Group IIb ecotypes (Ca. Poseidoniales) in the North Western Mediterranean Sea. ISME J. 2020;15:1302–16.
Google Scholar
Iverson V, Morris RM, Frazar CD, Berthiaume CT, Morales RL, Armbrust EV. Untangling Genomes from Metagenomes: Revealing an Uncultured Class of Marine Euryarchaeota. Science. 2012;335:587–90.
Google Scholar
Pereira O, Hochart C, Auguet JC, Debroas D, Galand PE. Genomic ecology of Marine Group II, the most common marine planktonic Archaea across the surface ocean. MicrobiologyOpen. 2019;8:e00852.
Google Scholar
Tully BJ. Metabolic diversity within the globally abundant Marine Group II Euryarchaea offers insight into ecological patterns. Nat Commun. 2019;10:271.
Google Scholar
Xie W, Luo H, Murugapiran SK, Dodsworth JA, Chen S, Sun Y, et al. Localized high abundance of Marine Group II archaea in the subtropical Pearl River Estuary: implications for their niche adaptation. Environ Microbiol. 2018;20:734–54.
Google Scholar
Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.
Google Scholar
Bernard G, Pathmanathan JS, Lannes R, Lopez P, Bapteste E. Microbial dark matter investigations: how microbial studies transform biological knowledge and empirically sketch a logic of scientific discovery. Genome Biol Evol. 2018;10:707–15.
Google Scholar
Carradec Q, Pelletier E, Da Silva C, Alberti A, Seeleuthner Y, Blanc-Mathieu R, et al. A global ocean atlas of eukaryotic genes. Nature Communications. 2018;9:373.
Google Scholar
Thomas AM, Segata N. Multiple levels of the unknown in microbiome research. BMC Biology. 2019;17:48.
Google Scholar
Source: Ecology - nature.com