Blog

Jun 01, 2023

A compendium of bacterial and archaeal single

Scientific Data volume 10,

Scientific Data volume 10, Article number: 332 (2023) Cite this article

1266 Accesses

22 Altmetric

Metrics details

Oxygen-deficient marine waters referred to as oxygen minimum zones (OMZs) or anoxic marine zones (AMZs) are common oceanographic features. They host both cosmopolitan and endemic microorganisms adapted to low oxygen conditions. Microbial metabolic interactions within OMZs and AMZs drive coupled biogeochemical cycles resulting in nitrogen loss and climate active trace gas production and consumption. Global warming is causing oxygen-deficient waters to expand and intensify. Therefore, studies focused on microbial communities inhabiting oxygen-deficient regions are necessary to both monitor and model the impacts of climate change on marine ecosystem functions and services. Here we present a compendium of 5,129 single-cell amplified genomes (SAGs) from marine environments encompassing representative OMZ and AMZ geochemical profiles. Of these, 3,570 SAGs have been sequenced to different levels of completion, providing a strain-resolved perspective on the genomic content and potential metabolic interactions within OMZ and AMZ microbiomes. Hierarchical clustering confirmed that samples from similar oxygen concentrations and geographic regions also had analogous taxonomic compositions, providing a coherent framework for comparative community analysis.

Oxygen deficient zones are common oceanographic features (Fig. 1) arising when microbial respiratory oxygen demand during breakdown of organic matter exceeds oxygen availability. These waters are operationally defined based on oxygen conditions ranging from dysoxic (20–90 μM), suboxic (1–20 μM), anoxic (less than 1 μM) or anoxic sulfidic (no detectable oxygen)1,2. Oceanic midwater oxygen minimum zones (OMZs) such as the North Pacific subtropical gyre present dysoxic conditions capable of supporting anaerobic metabolism through microbial remineralization of sinking particulate organic matter3 (Fig. 1a). Low oxygen coastal and open ocean OMZs such as the Northeastern Subarctic Pacific (NESAP) present suboxic conditions encompassing the redox transition for nitrate (NO3−) reduction (Fig. 1a). Anoxic marine zones (AMZs) are further differentiated by nitrite (NO2−) accumulation with or without sulfide accumulation (sulfidic bottom waters and open ocean or low-oxygen minimum zones (OMZs), respectively)4,5,6. For example, AMZs in the Eastern Tropical North Pacific (ETNP) and Eastern Tropical South Pacific (ETSP) present nanomolar oxygen conditions supporting NO3− reduction to NO2− and further reduced nitrogen products without hydrogen sulfide (H2S) accumulation (Fig. 1a). In contrast, coastal upwelling systems such as Benguela upwelling off the coast of Namibia present episodic shifts in oxygen deficiency, supporting the emergence of transient sulfidic plumes (Fig. 1a). Anoxic sulfidic conditions are also present in coastal fjords, such as the Saanich Inlet (SI), where glacial sills restrict water mass circulation. Sulfidic bottom conditions are also observed in marginal seas, such as the Baltic Sea (Fig. 1a).

Oxygen minimum zone (OMZ) and anoxic marine zone (AMZ) geochemical profiles and global map of sampling locations. (a) The different geochemical profiles of oxygen-deficient marine waters are schematized (modified from Ulloa et al., 2012)4. Solid lines represent observed data, while the dashed line represent a sporadic accumulation event. (b) OMZ and AMZ sampling locations for single-cell amplified genomes (SAGs) are indicated. The total number (white) and sequenced (black) SAGs obtained from each location are denoted with a circle proportional to the corresponding number of samples in the dataset. The Ocean is coloured according to the lowest mean statistical value for the oxygen concentration reported for each 1° and 5° grid in the 2018 annual NOAA World Ocean Atlas119, with white grids indicating locations where oxygen concentration data was unavailable. Sampling sites from oceanic midwaters include the North Pacific Subtropical Gyre (NPSG) and the South Atlantic Subtropical Gyre (SASG). Sample sites from low oxygen OMZs include the Northeastern Subarctic Pacific (NESAP). Sample sites from AMZs include the Eastern Tropical North Pacific Gyre (ETNP) and Eastern Tropical South Pacific Gyre (ETSP). Sites from coastal upwelling systems with ephemerally sulfidic bottoms include the Eastern South Pacific Coastal Upwelling (ESPCU) and Benguela coastal upwelling (Benguela). Sampling sites from sulfidic bottom basins include Saanich Inlet (SI) and the Baltic Sea. Geolocalization coordinates and the number of samples for each location are detailed in Table 1.

Different geochemical profiles within OMZs and AMZs create ecothermodynamic gradients7 driving coupled biogeochemical cycling of carbon, nitrogen and sulphur by cosmopolitan and endemic microorganisms adapted for life under low oxygen conditions (reviewed in2,3,4,8). Understanding how these metabolic interactions contribute to nitrogen loss and climate active trace gas production is a critical challenge9,10,11,12. Global warming exacerbates water column oxygen deficiency through thermal stratification and changes in water mass circulation, resulting in OMZ and AMZ expansion and intensification13,14,15. Other factors, including excessive nutrient inputs (eutrophication), also contribute to coastal and marginal sea oxygen deficiency15,16,17,18. Efforts to model coupled biogeochemical cycles within OMZs and AMZs using both gene-centric and genome-resolved metagenomic approaches have identified key microbial populations that would benefit directly from availability of improved genome assemblies with increased taxonomic resolution19,20.

Cultivation-independent whole genome shotgun sequencing provides direct insights into microbial community structure and function in natural and engineered environments21,22,23,24,25,26,27. As sequencing technologies improve, it becomes possible to assemble genomes from metagenomes with increasing taxonomic resolution20. However, despite an expanding reliance on metagenome-assembled genomes (MAGs), several challenges remain, including resolving population microheterogeneity28, incomplete or chimeric genome assemblies (resulting from either assembly or binning), coverage bias, and limited availability of taxonomically characterized reference genomes for cross-validation29,30,31. Advances in fluorescence-activated cell sorting (FACS) and sequencing technologies enable study of uncultivated microorganisms at the individual cell level, providing more accurate taxonomic labels and associated mobile genetic elements (MGEs)32,33,34,35,36,37,38. Resulting single-cell amplified genomes (SAGs) and MGEs have been used to illuminate coding potential of "microbial dark matter"39, provide accurate linkages between taxonomy and function underlying biogeochemical cycles20,21,30,40, and to evaluate genome streamlining41, fine scale population structure28,37,42 and virus-host dynamics43. Recent release of the Global Ocean Reference Genomes Tropics, or GORG-Tropics provides a valuable compendium of taxonomically defined SAGs containing >12,000 partial genome sequences from tropical and subtropical euphotic ocean waters44. Although a small subset of GORG-Tropics SAGs were collected from ‘oceanic midwater low oxygen’ waters (2,136 of 20,288 sequenced SAGs)44, oxygen-deficient marine waters remain conspicuously underrepresented, considering their substantial biogeochemical impact on marine ecosystem functions and services.

Here, we present a global compendium of bacterial and archaeal SAGs from OMZs and AMZs. This compendium contains 5,129 taxonomically identified SAGs derived using a combination of targeted and untargeted cell sorting methods, and isolated from environments covering the full range of geochemical profiles associated with extant, oxygen-deficient marine waters4 (Fig. 1). Currently, 3,570 of these SAGs have been sequenced, assembled and decontaminated, based on established genomic standards45 (Fig. 2, S1a-c). Sequenced and assembled SAGs were processed through the Microbial Genome Annotation Pipeline46 for gene prediction and functional annotation, and are available through the Integrated Microbial Genome platform (IMG; https://img.jgi.doe.gov/)47 or IMG/ProPortal (https://img.jgi.doe.gov/proportal). The collection of SAG sequences provides an invaluable resource to infer metabolic traits, resolve population structures, and assess spatial and temporal trends of relevant taxonomic lineages within OMZ and AMZ microbiomes.

Overview of the workflow for processing and generating microbial Single-cell Amplified Genomes (SAGs). A more detailed scheme is presented in the supplementary information (Supplemental Figure S1-S3) (modified from Rinke et al., 2013)50.

Approximately 1–2 mL seawater samples were collected in duplicate or triplicate during various oceanographic cruises within different OMZs and anoxic waters (Fig. 1 and Table 1). Samples were placed in sterile cryovials and amended with one of the following cryoprotectants: glycine betaine (6% [v/v] final concentration39,48), glycerol (10% [v/v] final concentration28,49), or glycerol-TE buffer39,50. Environmental seawater collection was performed using a Niskin-bottle rosette, or a Pump Profiling System for the NBP13-05 cruise (ETSP; R/V Nathaniel B. Palmer, July 5–7th, 2013), equipped with a conductivity-temperature-depth profiler, dissolved O2 sensor, fluorometer and transmissometer. A modified sample collection protocol was used during the BiG RAPA cruise (ETSP, off the coast of Chile, November 19th 2010, 55 m depth) which was first enriched on-deck selecting for chlorophyll-containing microorganisms51. Triplicate samples were passed through a 60 μm size mesh and sorted through an InfluxTM (BD Biosciences) flow cytometer system. Approximately 4,000 cells were sorted into 1 mL of sterile glycerol-TE buffer. Sorting was triggered based on the pigment content of particles in the red emission channel (excited by the 488 laser), using forward scattered light as a proxy for particle size. All samples were cryopreserved in liquid nitrogen and then stored at −80 °C, before being processed for single-cell amplified genome generation.

Samples were thawed and microbial cells sorted at the Bigelow Laboratory for Ocean Sciences’ Single Cell Genomics Center (SCGC) or the Joint Genome Institute (JGI). Samples were passed through a sterile 40 μm size mesh before microorganisms were sorted by either a non-targeted isolation procedure or specific selection for cyanobacteria. For non-target isolation, the microbial particles were labelled with a 5 μM final concentration of the DNA stain SYTO-9 (Thermo Fisher Scientific). Microbial cells were individually sorted using a MoFloTM (Beckman Coulter) or an InFluxTM (BD Biosciences) flow cytometer system equipped with a 488 nm laser for excitation and a 70 μm nozzle orifice52. The gates for the untargeted isolation of microbial cells stained with SYTO-9 were defined based on the green fluorescence of particles as a proxy for nucleic acid content, and side scattered light as a proxy for particle size. For isolation of cyanobacterial cells, gates were defined based on autofluorescence in the red emission channel. An improved discrimination of cyanobacterial cells from detrital particles was performed based on the ratio of green (SYTO-9 DNA label) versus red (chlorophyll content) fluorescence. The cytometer was triggered on the side-scatter using the "single-1 drop" mode. All microbial single-cells were sorted into 384-well plates containing 600 nL of 1X TE buffer per well and then stored at −80 °C until further processed. A subset of microbial cells, that generated the SAGs identified with the ‘AAA001’ prefix (part of the SAGs collected at the SASG, 800 m depth), were sorted into ‘prepGEMTM Bacteria reaction mix’ (ZyGEM)48. For samples processed in the Bigelow Single Cell Genomics Center, 64 of the 384-wells on each plate were used as negative controls (no droplet deposition), and 3 wells received 10 cells each to serve as positive controls.

The microbial single-cells sorted into TE buffer were lysed as described previously by adding either cold KOH53, or 700 nl of a lysis buffer consisting of 0.4 mM KOH, 10 mM EDTA and 100 mM dithiothreitol52. Samples were incubated for 10 min at either 4 or 20 °C for samples lysed with cold KOH or lysis buffer, respectively. Microbial cells sorted into ‘prepGEMTM Bacteria reaction mix’ were first lysed following the manufacturer's instructions and then processed through the cold KOH lysis procedure48. The microbial nucleic acids were then whole genome amplified in individual wells through either through traditional Phi29-mediated "Legacy Multiple Displacement Amplification" (L-MDA39,53) or using a more thermostable Phi29 polymerase via "Whole Genome Amplification-X" (WGA-X52). The products of this procedure are here referred to as SAGs.

While WGA-X generated single-cell genome amplification products were not taxonomically pre-screened, nearly all SAGs processed using L-MDA with the non thermostable polymerase were taxonomically identified by sequencing small subunit ribosomal RNA (SSU or 16 S rRNA) gene amplicons. Both bacterial (27-F: 5′- AGAGTTTGATCMTGGCTCAG -3′54, 907-R: 5′- CCGTCAATTCMTTTRAGTTT -3′55) and archaeal primers (Arc_344F: 5′- ACGGGGYGCAGCAGGCGCGA -3′56, Arch_915R: 5′- GTGCTCCCCCGCCAATTCCT -3′57) were used. Real-time PCR and sequencing of the resulting amplicons were performed as previously described39,52. Resulting SSU rRNA gene amplicon sequences were queried against the SILVA database v138.158 with blastn, from BLAST + v2.9.059. The top blastn hit (i.e. highest coverage, bit-score, and identity, as well as lowest e-value) was used as the primary taxonomic classification for each pre-screened SAG (Table S1)60. Additionally, SSU rRNA gene amplicon sequences were queried against the NCBI-RefSEQ v2021-08-14 database61. Top hits were determined using the same criteria described above, and denoted here as the secondary taxonomic assignments of the SAGs (Table S1)60. Sequences denoted as "Unclassified" had no significant sequence homology to any of the references within these databases (Table S1)60.

Two methods were used to assign taxonomy to the SAGs. Initially, taxonomic assignments for SAGs generated through L-MDA were conducted by extracting SSU rRNA gene sequences directly from the whole genome assemblies, or from the amplicons described above. For all SAGs generated through the WGA-X procedure that were not screened for any phylogenetic marker prior to genome sequencing, a search was conducted to identify SSU rRNA gene sequences > 500 bp within the genome assembly (Supplemental Figure S2). This search was performed through the Integrated Microbial Genomes & Microbiome system (IMG/M, https://img.jgi.doe.gov/m/)based on its gene prediction and annotation pipeline (see below)45,46. Additionally, SSU rRNA sequences were recovered from a subset of SAG assemblies with the Recovering ribosomal RNA gene sequences workflow with Anvi’o v7.062. These SSU rRNA gene sequences were processed as described above to assign taxonomy (Table S1)60. Because 1,281 SAGs did not provide sufficient SSU rRNA gene sequence information (Table S2)60, all SAG assemblies were also processed through the Genome Taxonomy Database Tool Kit GTDB-Tk v2.1.063,64,65,66,67,68,69,70 with GTDB R07-R207_v271,72,73 reference data for multi-locus taxonomic assignment. This allowed for taxonomic identification of SAGs missing SSU rRNA gene sequences, and offered an additional reference compared to those assigned by partial or complete phylogenetic marker sequences. The number of taxonomic assignments that were generated using both methods are detailed in Table S360, with the assignments being available in Table S160.

SAGs were sequenced as described previously39,52, and their reads assembled into contigs using SPAdes v2.2.10 to v3.10.074. Contigs of <2,000 bp were removed from SAG assemblies. Completeness and contamination levels of SAG assemblies were then determined using CheckM v1.2.175. To comply with established genomic standards45, assemblies exceeding 5% estimated contamination were run through ProDeGe v2.2 to v2.376 to eliminate the conflicting contigs until there was no improvement in their contamination estimates. The contamination and completeness levels for these SAGs were then re-evaluated using CheckM v1.2.175 and those that still exceeded 5% contamination were manually decontaminated through the Metagenomics Workflow and Refining MAG bin workflows available in Anvi’o v562.

A total of 14 SAGs exceeded 5% contamination after being processed through the ProDeGe decontamination pipeline76 and short-contig trimming (Table S5)60. These SAGs were manually decontaminated with Anvi’o v5 using the Metagenomics Workflow and Refining MAG bin workflows62. A contig database and the corresponding Hidden Markov Model for each database was generated for each of these SAGs. The taxonomy for each gene was then assigned using the Centrifuge Database77. Additional manual curation of these SAGs was carried out using differential coverage of each SAG based on metagenomic reads from Saanich Inlet metagenomes (August 2011 100 m, 150 m, and 2012 100 m, 150 m. Biosamples SAMN05224439, SAMN05224444, SAMN05224441, SAMN05224518, BioProject PRJNA247822)78. Raw metagenomic reads were mapped with bwa v0.7.17-r118879 and samtools v1.6-19-g1c03df680. Anvi profile databases were generated for each SAG by utilizing the contig databases and the read mapping files. Individual contigs were manually removed through the interactive interface based on taxonomic identity, average tetranucleotide identity, and low differential coverage. The new assemblies were exported as fasta files and re-assessed with CheckM.

After CheckM was run on all decontaminated SAG assemblies, the quality of each SAG was determined based on Bowers et al. 201745. SAGs that were <50% estimated completeness were considered low quality SAGs. SAGs that had ≥50% estimated completeness and <10% estimated contamination were considered to be at least medium quality. To determine if a SAG was high quality, in addition to having >90% estimated completeness and <5% estimated contamination, SAGs need to have 23 S, 16 S, and 5 S rRNA genes and at least 18 tRNAs present in the final assembly. To identify and quantify the rRNAs and tRNAs, SAGs were passed through Barrnap v0.9 (https://github.com/tseemann/Barrnap)81 and tRNA-SE v2.0.1182 respectively. Any SAGs having >90% estimated completeness and <5% estimated contamination but missing one or more rRNA genes with at least 18 tRNAs were classified as medium quality. The rRNA and tRNA counts, as well as Quality classifications for each SAG can be found Table S160.

All genome assemblies were annotated through the Joint Genome Institute's IMG platform and annotated using the JGI Microbial Genome Annotation Pipeline46. The IMG (https://img.jgi.doe.gov/) or IMG/ProPortal (https://img.jgi.doe.gov/proportal) systems host all final assembled and decontaminated SAG sequences, with gene calls and functional annotations publicly available through these portals. All IMG accession numbers for sequenced SAGs are provided (Table S1)60.

The recovered SSU rRNA gene amplicon sequences covering the V4-V5 variable region were clustered at 97% identity using CD-Hit83,84,85, and assigned identifiers based on a representative sequence from each cluster. Based on the taxonomic identity of these representative sequences, the proportion of SAGs associated with each cluster was determined on a per sample basis. These proportions were used to calculate Bray-Curtis Dissimilarity indices using the vegdist() command in the vegan R package v2.5-786. The samples were clustered based on Bray-Curtis dissimilarity, using an average linking method for hierarchical clustering using the hclust command in base R and visualized (Fig. 3).

A SAG-based assessment of microbial composition across OMZs. The dot-plot presents the taxonomic designation and proportion of anonymously sorted SAGs sequenced (colored dot) in each taxa at the phylum level and Proteobacteria at the class level from each location. Underlying grey dots represent SAGs collected and taxonomically screened, but not currently sequenced. Taxonomy was determined by SSU rRNA gene amplicon sequences as defined by SILVA v138.1. Dot colour represents environmental oxygen concentrations at time of sampling. Sampling locations were clustered according to the similarity of the SAG taxonomic composition collected at each location. Clustering scale represents the Bray-Curtis dissimilarity among the microbial diversity from each location based on SAG sequence information. Annotation bars denote DNA amplification mentod and OMZ type. Location information is colour encoded as shown for DNA amplification method, OMZ or AMZ type, and oxygen concentration at time of sampling. Sampling location names, on the tips of the dendrogram, are denoted as ‘location_depth (m)_collection month and/or year’. Location acronyms correspond to: Saanich Inlet (SI), Northeastern Subarctic Pacific (NESAP), North Pacific Subtropical Gyre (NPSG), Eastern Tropical North Pacific (ETNP), Eastern Tropical South Pacific (ETSP), Eastern South Pacific Coastal Upwelling (ESPCU), Benguela coastal upwelling (Benguela), South Atlantic Subtropical Gyre (SASG), and the Baltic Sea (Baltic).

File 1: Table S1. OMZ SAG biosamples with associated cruise and geolocation metadata. This file contains all Bioproject and Biosample accessions, IMG genome IDs, SRA accessions, Genbank accessions, CheckM outputs, GTDB-tk outputs, and SSU rRNA BLAST results can be found in: Table S1_Metadata-template-Bio-Med-SAGdescriptor-OMZ_April_06_2023.xlsx (10.6084/m9.figshare.20481603)60.

File 2: Table S2. Number of SAGs generated with each DNA amplification method, and how many recoverable SSU rRNA gene sequences were recovered from each dataset. Note that there are some samples that had both amplicon and whole genome derived SSU rRNA gene sequences. This information can be found in (10.6084/m9.figshare.20539005)60 and: https://github.com/hallamlab/OMZ_SAG_Compendium_Figures/blob/main/Outputs/Table_S2_Summary_Table_WGA_Approach_Mar_21_2023.csv

File 3: Table S3. Number of SAGs that were assigned a taxonomy with SILVA v138.1 and GTDB-tk v2.1.0 and their summary CheckM % completeness and % contamination estimates. This information can be found in (10.6084/m9.figshare.20539056)60 and: https://github.com/hallamlab/OMZ_SAG_Compendium_Figures/blob/main/Outputs/Table_S3_QA_QC_Summary_Mar_21_2023.csv

File 4: Table S4. Primary and secondary contact for each 384 microwell plate that contains the SAGs used in this compendium. This information can be found in Table S4_PI_Contact_Info.xlsx (10.6084/m9.figshare.20483595)60.

File 5: A zip-file compressed tar archive containing the genomic assemblies (10.6084/m9.figshare.20459526)60.fna – Nucleic acid file in multi-fasta format

File 6: A zip-file compressed tar archive containing the genomic SSU rRNA gene sequences, and the partial SSU rRNA gene amplicon sequences used for taxonomic assignment can be found in (10.6084/m9.figshare.20537919)60.fna – Nucleic acid file in multi-fasta format

File 7: Table S5. An xlsx file containing the list of SAGs that underwent manual decontamination from Saanich Inlet, as well as the depth they originated from. The depths were used to select the metagenome reads used for the manual decontamination process. This table can be found in (10.6084/m9.figshare.20538936)60.

Early implementation of SAG workflows involved MDA of anonymously sorted single cells in 384-well plate format followed by PCR amplification of selected phylogenetic markers e.g., SSU rRNA gene, to identify SAGs of interest for sequencing39. Recent development of WGA-X coupled with low-coverage genome sequencing (LoCoS) provides a more economical workflow to identify hundreds of SAGs per sample without potential PCR bias52. Targeted methods of sorting based on spectral properties of cells or substrates have also been applied to SAG selection and sequencing, including cyanobacteria and cells binding to fluorescently labelled substrates, such as cellulose87,88,89. Although the SSU rRNA gene remains one of the most extensively used phylogenetic markers and has well-established and curated databases (e.g. SILVA58), multi-locus phylogenetic assignment tools, such as GTDB-Tk63,64,65,66,67,68,69,70 generate equally valid results using more information. For this compendium, microbial diversity was assessed using taxonomic labels and abundance information for SAGs sequenced using non-targeted cell-sorting approaches. However, not all SAGs had a match for their SSU rRNA gene taxonomy due to either their amplicon sequences being too short (188 L-MDA SAGs with < 500 bp amplicons; Table S2) or no SSU rRNA gene was recovered from the random genome amplification (i.e. 1,093 WGA-X SAGs; Table S2). Thus, an additional taxonomic classifier was run for all SAGs, based on whole-genome assignment using GTDB-Tk63,64,65,66,67,68,69,70. Both sets of classifications are in Table S1. Hierarchical clustering (of 3,217 SAGs that contained an assignable V4-V5 SSU rRNA gene amplicon sequence) revealed a higher similarity among those from depths and geographic locations with similar oxygen conditions (Fig. 3), a result consistent with prior observations2,3,4,8. It should also be noted that many of the SAGs amplified with the WGS-X method originated from highly oxygenated samples, which had similar taxonomic compositions and therefore clustered together. Based on this information, the OMZ and AMZ SAG sequences presented here should serve to complement previous SAG collections obtained from (oxygenated) euphotic ocean waters44,49.

This compendium is intended to fill a critical gap in taxonomically labelled reference genomes from marine oxygen-deficient waters. Included SAG sequences were processed using well-established assembly and decontamination workflows. However, links to the raw data are also available for users interested in using future software versions or implementing alternative workflows. Any approach should aim to discern contaminating sequences associated with FACS (co-sorting two or more cells into a single well, environmental DNA contamination) and WGA (reagent contamination)90. It is important to emphasize that SAG sequences often contain MGEs, including plasmids and viruses43,91,92,93. These sequences are typically filtered out during the decontamination process, although differentiating between endogenous chromosomal intervals such as islands or prophage from MGEs requires careful manual curation. Users interested in MGEs are encouraged to work with the raw data or initial assemblies prior to decontamination. Note that genome assembly contamination estimates obtained by CheckM should be handled with caution, as this tool is prone to both over and under estimating completeness and contamination94. As described above, recent advances in MDA using WGA-X have led to improved SAG completion and the adoption of LoCoS has obviated the need for SSU rRNA gene amplicon screening to select SAGs of interest for sequencing52. The sequences included in this compendium include both older and more contemporary SAG sequencing approaches. The results are integrated by presenting SSU rRNA gene and multi-locus taxonomic assignments based on SILVA, NCBI, and GTDB.

Despite improvements introduced with WGA-X52, single-cell genomics invariably results in incomplete genome assemblies (Fig. 4, Supplemental Figure S4). This limitation can be overcome in part when multiple SAGs sharing extremely high levels of nucleotide identity are obtained from the same sample. Such closely related sequences can be analysed together, enabling more complete metabolic reconstruction7,33,42,51, or used to generate combined assemblies50,95. In addition, population-level genomes can be obtained through hybrid assemblies combining SAG sequences and metagenomic sequences96,97. In all cases, SAG contigs should be quality filtered to eliminate the presence of contaminating sequences and comply with established genomic standards45. All SAG assemblies reported here were thoroughly decontaminated, reaching <5% contamination for all except four SAGs (that only had between 5–10% contamination; Fig. 4, Supplemental Figure S4, Table S2).

CheckM completeness and contamination estimates of sequenced SAGs for all sequenced SAGs with the point size representing the assembly length in Megabase Pairs (MBP). Of these, the solid line represents the estimated completeness and contamination threshold for medium quality SAGs (> = 50% Completeness, <10% Contamination) and the dashed line represents the threshold for high quality SAGs (>90% Completeness, <5% Contamination)45. Plots are coloured based on (a) region, (b) OMZ ecotype, (c) depth, (d) environmental oxygen concentration level, (e) DNA amplification method, and (f) taxonomic group (class level for Proteobacteria, phylum level for other taxa) as defined by SILVA v138.1. Note that SAGs >5% estimated contamination have been excluded from this figure.

Many SAGs included in this compendium have not been sequenced, and the DNA remains in storage. Users are encouraged to identify underrepresented microorganisms from OMZ and AMZ microbiomes based on the provided taxonomic information that can be prioritized for sequencing and shared with the user community. At the same time, we recognize that there are also underrepresented OMZ and AMZ environments not included in this compendium. Sequences from the Black Sea, South China Sea, Arabian Sea and Bay of Bengal, among others, would provide a more robust representation of oxygen-deficient marine waters for use in comparative studies and modelling efforts. Finally, the SAG sequences included in this compendium can be used as taxonomically characterized reference genomes to recruit metagenomic data sets from marine environments, improve pathway prediction methods29,98,99,100,101,102,103,104,105,106 or expand reference packages for gene-centric analysis of functional markers107.

The scripts used to calculate the number of SAGs, the Bray-Curtis Dissimilarity Matrix, conduct the hierarchical cluster, and generate the Figs. 1b, 3, 4, Supplemental Figures S4–S8 written under R version 4.1.3. These scripts utilize the following R packages: tidyverse, egg, vegan, dendexted, sf, rnaturalearth, and rnaturalearthdata will produce the tables and figures presented in this paper. Direct link to relevant software and specifications can be found online at the Hallam Lab Github repository https://github.com/hallamlab/OMZ_SAG_Compendium_Figures.

Additional software used, including version numbers, adjustable variables and other parameters include the following:

Trimmomatic 0.35108: -phred33 LEADING:0 TRAILING:5 SLIDINGWINDOW:4:15 MINLEN:36

ILLUMINACLIP:Trimmomatic-0.35108: /adapters/TruSeq. 3-PE.fa:2:3:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

SPAdes 3.0.0-3.10 74 : --careful--sc--phred-offset 33

ProDeGe v2.3.0 76

CheckM v1.2.175: checkm lineage_wf --tab_table -x.fna --threads 8 --pplacer_threads 8

CheckM v1.2.175: checkm qa -o 2 --tab_table

GTDB-Tk v2.1.063,64,65,66,67,68,69,70: gtdbtk classify_wf --genome_dir --out_dir -x.fna --cpus 8

Nucleotide-Nucleotide BLAST 2.9.0+59: blastn -query -db -outfmt "6 qacc sacc stitle staxid pident bitscore" -max_target_seqs. 1 -num_threads 4 -out

Nucleotide-Nucleotide BLAST 2.9.0+59: blastn -query -db -outfmt "6 qacc stitle pident bitscore" -max_target_seqs. 1 -num_threads 4 -out

Anvi’o v562: anvi-gen-contigs-database -f -o -n

Anvi’o v562: anvi-run-hmms -c

Anvi’o v562: anvi-get-sequences-for-gene-calls -c -o

Anvi’o v562: $CENTRIFUGE_BASE/p + h + v/p + h + v gene-calls.fa -S centrifuge_hits.tsv

Anvi’o v562: anvi-import-taxonomy-for-genes -c -p

BWA v 0.7.17-r118879:bwa index

BWA v 0.7.17-r118879:bwa mem

Samtools v 1.6-19-g1c03df6 (using htslib 1.6-55-gb065a60)80: samtools view -b F 4

Samtools v 1.6-19-g1c03df6 (using htslib 1.6-55-gb065a60)80: samtools index file.sorted.bam

Anvi’o v562: anvi-profile -i -c --min-contig-length 2000 --output.dir --cluster-contigs

Anvi’o v562: anvi-merge path_to_profile1/PROFILE.db path_to_profile2/PROFILE.db -o --skip-concoct-binning

Anvi’o v562: anvi-interactive -p

Anvi’o v562: anvi-summarize -c -p -C

Anvi’o v762: anvi-gen-contigs-database -f -o

Anvi’o v762: anvi-run-hmms -c --num-threads 8

Anvi’o v762: anvi-get-sequences-for-hmm-hits

barrnap81: barrnap --kingdom bac --threads {threads} --outseq {working_dir}/barrnap/*rRNA.fasta {input.fasta_dir}/$g.fasta > {working_dir}/barrnap/$g.rRNA.gff

barrnap v0.982: barrnap --kingdom arc --threads {threads} --outseq {working_dir}/barrnap/*rRNA.fasta {input.fasta_dir}/$g.fasta >{working_dir}/barrnap/$g.rRNA.gff

tRNAscan-SE v 2.0.1182: tRNAscan-SE -B -o {working_dir}/trnascan/$g.output.txt -m {working_dir}/trnascan/$g.stats.txt -b {working_dir}/trnascan/$g.bed -j {working_dir}/trnascan/$g.gff -a {working_dir}/trnascan/$g.trna.fasta -l {working_dir}/trnascan/$g.log --thread {threads} {input.fasta_dir}/$g.fasta

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 https://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; https://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).

Download references

We would like to thank the captain, crew and scientists onboard the RV John Strickland and CCGS John P. Tully for their extraordinary efforts in the field over many years, and Miranda Harmon-Smith and Tijana Glavina del Rio at the DOE Joint Genome Institute (JGI) for project management support. We also thank the many undergraduate helpers in the Hallam lab and ocean-going technical support staff including Jade Shiller and Chris Payne for their support in sample collection and processing over the years. Special thanks also to members of the DeLong lab at University of Hawai’i at Manoa Department of Oceanography & CMORE, the Stewart lab at Georgia Institute of Technology School of Biological Sciences, the Ulloa lab at Departamento de Oceanografía Universidad de Concepción, the Jürgens lab at the Leibniz Institute for Baltic Sea Research, and the Bigelow SCGC for contributing the SAGs used in this dataset. The work (Comparative community genome analysis of the Subarctic Pacific Ocean: 10.46936/10.25585/60007478, Microbial Systems Ecology of Expanding Oxygen Minimum Zones in the Eastern Subtropical North Pacific Ocean: 10.46936/10.25585/60000795, Opening a single-cell genomic window on microbial ecotype selection in expanding marine oxygen minimum zones: 10.46936/10.25585/60000761, Going long and going deep: Comprehensive open ocean community single cell genome sequencing at the model open ocean time series study site, station ALOHA: 10.46936/10.25585/60000920, Microbial and viral regulation of community carbon cycling across diverse low-oxygen zones: 10.46936/10.25585/60000893, Dark ocean microbial single cell genomics: 10.46936/10.25585/60000688, GEBA Single Cell Uncultured Microbes: 10.46936/10.25585/60007913, Generating reference genomes for marine ecosystem research: Single cell sequencing of ubiquitous, uncultured bacterioplankton clades: 10.46936/10.25585/60007356, Single cell genome sequencing of the mesopelagic bacterioplankton: 10.46936/10.25585/60007269) conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility, was supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. This work was also performed under the auspices of the Scientific Committee on Oceanographic Research (SCOR), the G. Unger Vetlesen and Ambrose Monell Foundations, the Natural Sciences and Engineering Research Council of Canada, Genome British Columbia, the Canada Foundation for Innovation, and the Canadian Institute for Advanced Research through grants awarded to S.J.H. SCGC analyses and RS were supported by NSF grants 1826734, 1441717, 821374, 1335810, 1232982, 0826924; and the Simons Foundation grants 510023 and 827839. OU was supported by Chilean National Agency for Research and Development (ANID) grants ICN12_019 and 1161483.

Alvaro M. Plominsky

Present address: Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, 92037, USA

These authors contributed equally: Julia Anstett, Alvaro M. Plominsky.

Graduate Program in Genome Sciences and Technology, Genome Sciences Centre, University of British Columbia, Vancouver, British Columbia, Canada

Julia Anstett & Steven J. Hallam

Department of Microbiology and Immunology, University of British Columbia, Vancouver, British Columbia, V6T 1Z3, Canada

Julia Anstett, Alvaro M. Plominsky & Steven J. Hallam

Daniel K. Inouye Center for Microbial Oceanography: Research and Education, University of Hawaii, Manoa, Honolulu, HI, 96822, USA

Edward F. DeLong

School of Engineering, The University of British Columbia, Kelowna, BC, Canada

Alyse Kiesser

Leibniz Institute for Baltic Sea Research, Warnemünde, Germany

Klaus Jürgens

Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

Connor Morgan-Lang & Steven J. Hallam

Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

Ramunas Stepanauskas

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

Frank J. Stewart

Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA

Frank J. Stewart

Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT, USA

Frank J. Stewart

Departamento de Oceanografía, Universidad de Concepción, Casilla 160-C, 4070386, Concepción, Chile

Osvaldo Ulloa

Instituto Milenio de Oceanografía, Casilla 1313, 4070386, Concepción, Chile

Osvaldo Ulloa

Department of Energy Joint Genome Institute, Berkeley, CA, USA

Tanja Woyke & Rex Malmstrom

Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Tanja Woyke & Rex Malmstrom

Life Sciences Institute, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada

Steven J. Hallam

ECOSCOPE Training Program, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada

Steven J. Hallam

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

J.A., A.M.P. and S.J.H. conceived the study. E.F.D.L., K.J., R.S., F.J.S., O.U. and S.J.H. collected samples. J.A., A.M.P., A.K., C.M.L., R.S., T.W. and S.J.H. analyzed the data and/or provided graphical interpretation of data. J.A., A.M.P., A.K., and S.J.H. compiled data, wrote the manuscript, and generated the figures. All authors have read and contributed to the text. SAGs were generated at the Bigelow Single Cell Genomics Center (SCGC). R.S., T.W. and R.M. provided the FACS. and kinetics plots generated for the cell-sorting and DNA amplification. The sorted cells were sequenced at the SCGC, the D.O.E. Joint Genome Institute (JGI), Canada's Michael Smith Genome Sciences Centre, the Georgia Institute of Technology, BioMicroCenter at MIT, Oregon State University, and the Marine Biological Laboratory. J.A., C.M.L., A.M.P trimmed and assembled the Illumina short reads into contigs to generate the S.A.G. assemblies. J.A. (Other Co-Authors) decontaminated the assemblies. J.A. clustered the amplicon sequences. J.A. and A.M.P. compiled data and generated the figures shown in this study.

Correspondence to Steven J. Hallam.

S.J.H is a co-founder of Koonkie Inc., a bioinformatics consulting company that designs and provides scalable algorithmic and data analytics solutions in the cloud.

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

Anstett, J., Plominsky, A.M., DeLong, E.F. et al. A compendium of bacterial and archaeal single-cell amplified genomes from oxygen deficient marine waters. Sci Data 10, 332 (2023). https://doi.org/10.1038/s41597-023-02222-y

Download citation

Received: 26 August 2022

Accepted: 10 May 2023

Published: 27 May 2023

DOI: https://doi.org/10.1038/s41597-023-02222-y

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative