A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters

  • Revsbech, N. P. et al. Determination of ultra-low oxygen concentrations in oxygen minimum zones by the STOX sensor. Limnol. Oceanogr.: Methods. 7, 371-381. (2009).

  • Wright, J. J., Konwar, K. M. & Hallam, S. J. Microbial ecology of expanding oxygen minimum zones. Nat. Rev. Microbiol. 10, 381–394 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jürgens, K. & Taylor, G. T. Microbial ecology and biogeochemistry of oxygen-deficient water columns in Microbial Ecology of the Oceans (eds. Gasol, J. M. & Kirchman, D. L.) 231–288 (John Wiley & Sons, 2018).

  • Ulloa, O., Canfield, D. E., DeLong, E. F., Letelier, R. M. & Stewart, F. J. Microbial oceanography of anoxic oxygen minimum zones. Proc. Natl. Acad. Sci. USA 109, 15996–16003 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thamdrup, B., Dalsgaard, T. & Revsbech, N. P. Widespread functional anoxia in the oxygen minimum zone of the Eastern South Pacific. Deep-Sea Res. Pt. I. 65, 36–45 (2012).

  • Bristow, L. A. et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc. Natl. Acad. Sci. USA 113, 10601–10606 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hawley, A. K. et al. Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat. Commun. 8, 1507 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bertagnolli, A. D. & Stewart, F. J. Microbial niches in marine oxygen minimum zones. Nat. Rev. Microbiol. 16, 723–729 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Codispoti, L. A. et al. The oceanic fixed nitrogen and nitrous oxide budgets: Moving targets as we enter the anthropocene? Scientia Marina. 65, 85–105 (2001).

  • DeVries, T., Deutsch, C., Rafter, P. A. & Primeau, F. Marine denitrification rates determined from a global 3-D inverse model. Biogeosciences. 10, 2481–2496 (2013).

  • Naqvi, S. W. A. et al. Marine hypoxia/anoxia as a source of CH4 and N2O. Biogeosciences 7, 2159–2190 (2010).

  • Thamdrup, B. et al. Anaerobic methane oxidation is an important sink for methane in the ocean’s largest oxygen minimum zone. Limnol. Oceanogr. 64, 2569–2585 (2019).

  • Stramma, L., Johnson, G. C., Sprintall, J. & Mohrholz, V. Expanding oxygen-minimum zones in the tropical oceans. Science 320, 655–658 (2008).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Carstensen, J., Andersen, J. H., Gustafsson, B. G. & Conley, D. J. Deoxygenation of the Baltic Sea during the last century. Proc. Natl. Acad. Sci. USA 111, 5628–5633 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Malone, T. C. & Newton, A. The globalization of cultural eutrophication in the coastal ocean: causes and consequences. Front. Mar. Sci. 7, 670 (2020).

  • Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Louca, S. et al. Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone. Proc. Natl. Acad. Sci. USA. 113, E5925–E5933 (2016).

  • Reed, D. C., Algar, C. K., Huber, J. A. & Dick, G. J. Gene-centric approach to integrating environmental genomics and biogeochemical models. Proc. Natl. Acad. Sci. USA. 111, 1879–1884 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Iverson, V. et al. Untangling genomes from metagenomes: revealing an uncultured class of marine Euryarchaeota. Science 335, 587–590 (2012).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74 (2004).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Albertsen, M., Hansen, L. B. S., Saunders, A. M., Nielsen, P. H. & Nielsen, K. L. A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J. 6, 1094–1106 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • DeLong, E. F. et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311, 496–503 (2006).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Kashtan, N. et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science 344, 416–420 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Basher, A. R. M. A., McLaughlin, R. J. & Hallam, S. J. Metabolic pathway inference using multi-label classification with rich pathway features. PLoS Comput. Biol. 16, e1008174 (2020).

    Article 

    Google Scholar
     

  • Meziti, A. et al. The reliability of metagenome-assembled genomes (MAGs) in representing natural populations: insights from comparing MAGs against isolate genomes derived from the same fecal sample. Appl. Environ. Microbiol. 87, e02593-20 (2021).

  • Sczyrba, A. et al. Critical assessment of metagenome interpretation-a benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Saak, C. C., Dinh, C. B. & Dutton, R. J. Experimental approaches to tracking mobile genetic elements in microbial communities. FEMS Microbiol. Rev. 44, 606–630 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stepanauskas, R. Wiretapping into microbial interactions by single cell genomics. Front. Microbiol. 6, 258 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stepanauskas, R. Single cell genomics: an individual look at microbes. Curr. Opin. Microbiol. 15, 613–620 (2012).

  • Rinke, C. Single-Cell Genomics of Microbial Dark Matter. Methods Mol. Biol. 1849, 99–111 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ishoey, T., Woyke, T., Stepanauskas, R., Novotny, M. & Lasken, R. S. Genomic sequencing of single microbial cells from environmental samples. Curr. Opin. Microbiol. 11, 198–204 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bowers, R. M. et al. Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution. ISME J. 16, 1337–1347 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pachiadaki, M. G. et al. Major role of nitrite-oxidizing bacteria in dark ocean carbon fixation. Science 358, 1046–1051 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl. Acad. Sci. USA 110, 11463–11468 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kashtan, N. et al. Fundamental differences in diversity and genomic population structure between Atlantic and Pacific Prochlorococcus. ISME J. 11, 1997–2011 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roux, S. et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. Elife 3, e03125 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

  • Markowitz, V. M. et al. IMG 4 version of the integrated microbial genomes comparative analysis system. Nucleic Acids Res. 42, D560–D567 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Berube, P. M. et al. Single cell genomes of Prochlorococcus, Synechococcus, and sympatric microbes from diverse marine environments. Sci. Data 5, 180154 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Plominsky, A. M. et al. Metabolic potential and in situ transcriptomic profiles of previously uncharacterized key microbial groups involved in coupled carbon, nitrogen and sulfur cycling in anoxic marine zones. Environ. Microbiol. 20, 2727–2742 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Raghunathan, A. et al. Genomic DNA Amplification from a Single Bacterium. Appl. Environ. Microbiol. 71, 3342–3347 (2005).

  • Page, K. A., Connon, S. A. & Giovannoni, S. J. Representative freshwater bacterioplankton isolated from Crater Lake, Oregon. Appl. Environ. Microbiol. 70, 6542–6550 (2004).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stackebrandt, E. & Goodfellow, M. Nucleic Acid Techniques in Bacterial Systematics. (John Wiley & Son Limited, 1991).

  • Chapelle, F. H. et al. A hydrogen-based subsurface microbial community dominated by methanogens. Nature 415, 312–315 (2002).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Ohene-Adjei, S., Teather, R. M., Ivan, M. & Forster, R. J. Postinoculation protozoan establishment and association patterns of methanogenic archaea in the ovine rumen. Appl. Environ. Microbiol. 73, 4609–4618 (2007).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    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 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anstett, J. et al. A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters Figshare doi.org/10.6084/m9.figshare.c.6137379.v5 (2022).

  • O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 3, e1319 (2015).

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    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).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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, 5114 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).

  • Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).

    Article 
    ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat Microbiol 6, 946–959 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tennessen, K. et al. ProDeGe: a computational protocol for fully automated decontamination of genomes. ISME J. 10, 269–272 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, D., Song, L., Breitwieser, F. P. & Salzberg, S. L. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res. 26, 1721–1729 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hawley, A. K. et al. A compendium of multi-omic sequence information from the Saanich Inlet water column. Sci. Data 4, 170160 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Seemann, T. barrnap 0.9: Bacterial ribosomal RNA predictor. (Github).

  • Chan, P. P., Lin, B. Y., Mak, A. J. & Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 49, 9077–9096 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

  • Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26, 680–682 (2010).

  • Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Website. Oksanen, J. et al. vegan: Community Ecology Package R package version 2.5–6; CRAN.R-project.org/package=vegan (2019).

  • Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia. PLoS One 7, e35314 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ulloa, O. et al. The cyanobacterium Prochlorococcus has divergent light-harvesting antennae and may have evolved in a low-oxygen ocean. Proc. Natl. Acad. Sci. USA 118, e2025638118 (2021).

  • Doud, D. F. R. et al. Function-driven single-cell genomics uncovers cellulose-degrading bacteria from the rare biosphere. ISME J. 14, 659–675 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Woyke, T. et al. Decontamination of MDA reagents for single cell whole genome amplification. PLoS One 6, e26161 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Labonté, J. M. et al. Single-cell genomics-based analysis of virus–host interactions in marine surface bacterioplankton. ISME J. 9, 2386–2399 (2015).

  • Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4, e08490 (2015).

  • Martinez-Hernandez, F. et al. Single-cell genomics uncover Pelagibacter as the putative host of the extremely abundant uncultured 37-F6 viral population in the ocean. ISME J. 13, 232–236 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Becraft, E. D. et al. Rokubacteria: Genomic Giants among the Uncultured Bacterial Phyla. Front. Microbiol. 8, 2264 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nobu, M. K. et al. Phylogeny and physiology of candidate phylum “Atribacteria” (OP9/JS1) inferred from cultivation-independent genomics. ISME J. 10, 273–286 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mende, D. R., Aylward, F. O., Eppley, J. M., Nielsen, T. N. & DeLong, E. F. Improved Environmental Genomes via Integration of Metagenomic and Single-Cell Assemblies. Front. Microbiol. 7, 143 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kogawa, M., Hosokawa, M., Nishikawa, Y., Mori, K. & Takeyama, H. Obtaining high-quality draft genomes from uncultured microbes by cleaning and co-assembly of single-cell amplified genomes. Sci. Rep. 8, 2059 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Konwar, K. M., Hanson, N. W., Pagé, A. P. & Hallam, S. J. MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinformatics 14, 202 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hanson, N. W., Konwar, K. M., Wu, S.-J. & Hallam, S. J. MetaPathways v2.0: A master-worker model for environmental Pathway/Genome Database construction on grids and clouds. 2014 IEEE Conf. Comput. Intel. Bioinf. Comput. Biol. (2014).

  • Konwar, K. M. et al. MetaPathways v2.5: quantitative functional, taxonomic and usability improvements. Bioinformatics 31, 3345–3347 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Karp, P. D., Paley, S. & Romero, P. The pathway tools software. Bioinformatics 18, S225–S232 (2002).

  • Karp, P. D., Latendresse, M. & Caspi, R. The pathway tools pathway prediction algorithm. Stand. Genom. Sci. 5, 424–429 (2011).

  • Karp, P. D. The EcoCyc and MetaCyc databases. Nucleic Acids Res. 28, 56–59 (2000).

  • Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes – a 2019 update. Nucleic Acids Res. 48, D445–D453 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Basher, A. R. M. A. & Hallam, S. J. Leveraging heterogeneous network embedding for metabolic pathway prediction. Bioinformatics 37, 822–829 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Basher, A. R. M. A., McLaughlin, R. J. & Hallam, S. J. Metabolic pathway prediction using non-negative matrix factorization with improved precision. J. Comput. Biol. 28, 1075–1103 (2021).

  • Morgan-Lang, C. et al. TreeSAPP: the tree-based sensitive and accurate phylogenetic profiler. Bioinformatics 36, 4706–4713 (2020).

  • Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  • Ganesh, S. et al. Single cell genomic and transcriptomic evidence for the use of alternative nitrogen substrates by anammox bacteria. ISME J. 12, 2706–2722 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ganesh, S. et al. Size-fraction partitioning of community gene transcription and nitrogen metabolism in a marine oxygen minimum zone. ISME J. 9, 2682–2696 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tsementzi, D. et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature 536, 179–183 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Duret, M. T. et al. Size-fractionated diversity of eukaryotic microbial communities in the Eastern Tropical North Pacific oxygen minimum zone. FEMS Microbiol. Ecol. 91 (2015).

  • Padilla, C. C. et al. NC10 bacteria in marine oxygen minimum zones. ISME J. 10, 2067–2071 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Henríquez-Castillo, C. et al. Metaomics unveils the contribution of Alteromonas bacteria to carbon cycling in marine oxygen minimum zones. Front. Mar. Sci. 9, 993667 (2022).

  • Rii, Y. M. et al. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the South East Pacific Ocean. Limnol. Oceanogr. 61, 806–824 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Boeuf, D. et al. Metapangenomics reveals depth-dependent shifts in metabolic potential for the ubiquitous marine bacterial SAR324 lineage. Microbiome 9, 172 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Landry, Z., Swan, B. K., Herndl, G. J., Stepanauskas, R. & Giovannoni, S. J. SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter. MBio 8 (2017).

  • Torres-Beltrán, M. et al. A compendium of geochemical information from the Saanich Inlet water column. Sci. Data 4, 170159 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Garcia, H. E. et al. World Ocean Atlas 2018: Dissolved oxygen, apparent oxygen utilization, and oxygen saturation. NOAA Atlas NESDIS. 3, 83 (2019).

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