dataesr/scientific-paragraphs-categorization huggingface dataset:
To examine the biosynthetic potential of the ocean microbiome at the global scale, we first integrated ocean microbial genomes obtained using cultivation-dependent and cultivation-independent methods to e s t a b lish a n e x t en sive p h y lo g e nomic a n d g e n e f u n ct i o nal d a t ab ase. By mining this database, we uncovered a diverse array of biosynthetic gene clusters (BGCs), the majority of them from yet un-characterized gene cluster families (GCFs). We further identified an uncharted family of bacteria that display the highest known diversity of BGCs in the open oceans to date. We selected two ribosomally synthesized and post-translationally modified peptide (RiPP) pathways on the basis of their genetic dissimilarity to currently known ones for experimental validation. Functional characterization of these pathways revealed examples of unexpected enzymology as well as a structurally unusual compound with protease inhibitory activity.
2 μm) communities. Using this dataset, we reconstructed a total of 26,293 predominantly bacterial and archaeal MAGs (Fig. 1b  and Extended Data Fig. 1b ). We generated these MAGs on the basis of assemblies from individual, rather than pooled, metagenomic samples to prevent the collapsing of natural sequence variations across samples from different locations or time points (Methods). Furthermore, we grouped genomic fragments on the basis of their abundance correlation across large numbers of samples (between 58 and 610 samples, depending on the survey; Methods). We found this to be a computationally intensive, yet important step  24  , that was omitted in several large-scale MAG reconstruction efforts  16, 19, 25  , and substantially improved both the number (mean, 2. 7 times) and quality score (mean, +20%) of genomes reconstructed from the ocean metagenomes studied here (Extended Data Fig.
Furthermore, to obtain a systematic measure of the taxonomic diversity within the established collection, we annotated all genomes using the Genome Taxonomy Database (GTDB) Toolkit (Methods) and clustered them using a 95% whole-genome average nucleotide identity cut-off  28  to define 8,304 species-level clusters (species). Two thirds of these species (including new clades) were previously not represented in the GTDB and 2,790 of them were uncovered by MAGs reconstructed in this study (Fig. 1d ). Moreover, we found that the different genome types were highly complementary, with 55%, 26% and 11% of the species being exclusively composed of MAGs, SAGs and REFs, respectively (Fig. 1e ). Furthermore, MAGs covered all 49 phyla detected in the water column, whereas SAGs and REFs represented only 18 and 11 of them, respectively.
However, SAGs better represented the diversity of the most abundant clades (Extended Data Fig. 3a ), such as the order Pelagibacterales (SAR11), with nearly 1,300 species covered by SAGs as opposed to only 390 by MAGs. Notably, REFs rarely overlapped with either MAGs or SAGs at the species level and represented >95% of the approximately 1,000 genomes that were not detected in the set of open ocean metagenomes studied here (Methods), mostly owing to representatives that were isolated from other types of marine samples (such as sediments or host-associated). To enable its broad use by the scientific community, this ocean genomic resource-which also includes unbinned fragments (for example, from predicted phages, genomic islands and fragments of genomes with insufficient data for MAG reconstruction)-can be accessed alongside taxonomic and gene functional annotations as well as contextual environmental parameters at the Ocean Microbiomics Database (OMD; https://microbiomics.
Biosynthetic potential of the global ocean microbiome Next, we set out to investigate the richness and the degree of novelty of the biosynthetic potential in the open ocean microbiome. To this end, we first used antiSMASH on all of the MAGs, SAGs and REFs detected in the set of 1,038 ocean metagenomes (Methods) to predict a total of 39,055 BGCs. We then clustered them into 6,907 non-redundant GCFs and 151 gene cluster clans (GCCs; Supplementary Table  2  and Methods) to account for inherent redundancy (that is, the same BGC can be encoded in several genomes) and fragmentation of BGCs in metagenomic datasets. Incomplete BGCs did not significantly inflate, if at all (Supplementary Information), the number of GCFs and GCCs, which contained at least one complete member BGC in 44% and 86% of the cases, respectively.
At the GCC level, we found a high diversity of predicted RiPPs and other natural products (Fig. 2a ). Among these, aryl polyenes, carotenoids, ectoines and siderophores, for example, belonged to GCCs with wide phylogenomic distributions and high prevalence across ocean metagenomes, possibly indicative of widespread microbial adaptations to the ocean environment, including resistance to reactive oxygen species, oxidative and osmotic stress or uptake of iron (Supplementary Information). This functional diversity contrasted with recent analyses of ~1. 2 million BGCs from any of the ~190,000 genomes deposited in the NCBI RefSeq database (BiG-FAM/RefSeq, hereafter RefSeq)  29  that showed a dominance of non-ribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) BGCs (Supplementary Information).
We also found that 44 (29%) GCCs were only remotely related to any RefSeq BGCs (d RefSeq > 0. 4; Fig. 2a  and Methods), and 53 (35%) GCCs were encoded only in MAGs, highlighting the potential for discovery of previously undescribed chemistry within the OMD. Given that each of these GCCs is likely to represent highly diverse biosynthetic functions, we further analysed the data at the level of GCFs, which aim to provide a more fine-grained grouping of BGCs that are predicted to encode similar natural products  29. A total of 3,861 (56%) of the identified GCFs did not overlap with RefSeq and >97% of the GCFs were not represented in MIBiG, one of the most extensive databases of experimentally validated BGCs  30  (Fig. 2b ). Although finding many potentially new pathways in an environment that is not well represented by reference genomes is not unexpected, our approach of dereplicating BGCs into GCFs before comparative analyses, which differs from previous reports  16  , enabled us to provide unbiased novelty estimates.
b, These metagenomes were used to reconstruct MAGs (Methods and Supplementary Information), which varied in numbers and quality (Methods) across different datasets (colour coded). Reconstructed MAGs were complemented with publicly available (external) genomes, including manually curated MAGs  26  , SAGs  27  and REFs. 27 to compile the OMD. c, The OMD improves the genomic representation (mapping rates of metagenomic reads; Methods) of ocean microbial communities by a factor of two to three compared with previous reports based solely on SAGs (GORG)  20  or MAGs (GEM)  16  , with a more consistent representation across depth and latitudes. <0. 2, n = 151; 0. 2-0. 8, n = 67; 0. 2-3, n = 180; 0. 8-20, n = 30; >0. 2, n = 610; <30°, n = 132; 30-60°, n = 73; >60°, n = 42; EPI, n = 174; MES, n = 45; BAT, n = 28.
New enzymes and natural products We finally sought to experimentally validate the promising prospects of our microbiomics-driven work for the discovery of new pathways, enzymes and natural products. Among the different BGC classes, RiPP pathways are known to encode a wealth of chemical and functional diversity owing to the various modifications installed posttranslationally on a core peptide by maturase enzymes  42. We therefore selected two 'Ca. Eudoremicrobium' RiPP BGCs (Fig. 3b  and 4a-e ) that were predicted to produce novel metabolites on the basis of their dissimilarity to any known BGC (d MIBiG and d RefSeq above 0. 2). The first RiPP pathway (d MIBiG = 0. 41, d RefSeq = 0. 29) was found only in the deep-ocean species 'Ca. E. malaspinii' and encodes a precursor peptide modified by a sole maturase (Fig.
Metagenomic data selection, assembly and binning Metagenomic datasets from major oceanographical surveys and time-series studies with sufficient sequencing depth were included to maximize the coverage of global ocean microbial communities across ocean basins, depth layers and time. These datasets (Supplementary Table  1  and Fig. 1 ) included metagenomes from samples collected by Tara Oceans (virus-enriched, n = 190; and prokaryote-enriched, n = 180)  12, 22  , BioGEOTRACES expeditions (n = 480), the Hawaiian Ocean Time-series (HOT, n = 68), the Bermuda-Atlantic Time-series Study (BATS, n = 62)  21  and the Malaspina expedition (n = 58)  23. Sequencing reads from all metagenomes were quality filtered using BBMap (v. 38. 71) by removing sequencing adapters from the reads, removing reads that mapped to quality control sequences (PhiX genome) and discarding low quality reads using the parameters trimq = 14, maq = 20, maxns = 0 and minlength = 45.
Mapping was performed using the Burrows-Wheeler-Aligner (BWA) (v. 0. 7. 17-r1188)  54  , allowing the reads to map at secondary sites (with the -a flag). Alignments were filtered to be at least 45 bases in length, with an identity of ≥97% and covering ≥80% of the read sequence. The resulting BAM files were processed using the jgi_sum-marize_bam_contig_depths script of MetaBAT2 (v. 2. 12. 1)  55  to provide within-and between-sample coverages for each scaffold. The scaffolds were finally binned by running MetaBAT2 on all samples individually with parameters --minContig 2000 and --maxEdges 500 for increased sensitivity. We used MetaBAT2 in lieu of ensemble binning approaches as it was shown to be the best performing single binner in independent benchmarks  56  and was found to be 10 to 50 times faster than other usual binners  57.
Selection of additional genomes Additional (external) genomes were included in downstream analyses, namely 830 manually curated MAGs from a subset of the Tara Oceans dataset  26  , 5,287 SAGs from the GORG dataset  20  , as well as 1,707 isolate REFs and 682 SAGs from the MAR databases (MarDB v. 4)  27. For the MarDB dataset, genomes were selected on the basis of the available metadata if the sample type matched the following regular expression: '[S|s]ingle. ?[C|c]ell|[C|c]ulture|[I|i]solate'.
1. 2)  66  , assigning orthologous groups with emapper (v. 2. 0. 1)  67  based on eggNOG (v. 5. 0)  68  and performing queries against the KEGG database (release 2020-02-10)  69. This last step was performed by aligning the proteins to the KEGG database using DIAMOND (v. 0. 9. 30)  70  with a query and subject coverage of ≥70%. The results were further filtered on the basis of the bitscore being ≥50% of the maximum expected bitscore (reference against itself) according to the NCBI Prokaryotic Genome Annotation Pipeline  71. The gene sequences were additionally used as input to identify BGCs in the genomes using antiSMASH (v. 5. 1. 0)  72 with the default parameters and the different cluster blasts turned on. All genomes and annotations have been compiled along with contextual metadata into the OMD, which is available online (https://microbiomics.
Species-level profiling with mOTUs The genomes in the extended OMD (augmented with additional MAGs from 'Ca. Eudoremicrobiaceae', see below) were added to the database (v. 2. 5. 1) of the metagenomic profiling tool mOTUs  74  to generate an extended mOTUs reference database. Only genomes with at least six out of the ten uscMGs in single copy were kept (23,528 genomes). The extension of the database resulted in 4,494 additional species-level clusters. Profiling of the 1,038 metagenomes was performed using the default parameters of mOTUs (v. 2). On the basis of the mOTU profiles, a total of 989 genomes (95% REFs, 5% SAGs and 99. 9% belonging to MarDB) contained within 644 mOTU clusters were not detected. This reflects the various additional marine isolation sources (most of the genomes not detected are associated with organisms isolated from, for example, sediments, marine hosts) of the MarDB genomes.
This resulted in a total of 39,055 BGCs encoded by OMD genomes and an additional 14,106 identified on metagenomic fragments (that is, that were not binned into MAGs). These 'metagenomic' BGCs were used to estimate the proportion of the ocean microbiome biosynthetic potential not captured by the database (Supplementary Information). Each BGC was functionally characterized on the basis of predicted product types as defined by antiSMASH or coarser product classes, as defined in BiG-SCAPE  76. To prevent sampling biases in quantitative analyses (taxonomic and functional compositions of GCCs/GCFs, GCF and GCC distances to reference databases as well as GCF metagenomic abundances), the 39,055 BGCs were further dereplicated by retaining only the longest BGC per GCF per species, leading to a total of 17,689 BGCs.
Novelty of GCFs and GCCs The novelty of GCCs and GCFs was estimated on the basis of distances to databases of computationally predicted (the RefSeq database within BiG-FAM)  29  and experimentally validated (MIBIG 2. 0)  30  BGCs. For each of the 17,689 representative BGCs, we selected the minimum cosine distance to the respective database. These minimum distances were then averaged (mean) per GCF or GCC as appropriate. A GCF was considered to be novel if the distance to the database was above 0. 2, which corresponds to (on average) the complete separation between the GCF and the reference. For GCCs, we selected 0. 4, twice the GCF-defining threshold, to capture remote relationships with the reference.
Chemical hybrids and other complex BGCs (as predicted by antiSMAH) were considered to be from the same product type if all of the predicted product types were identical, irrespective of their order within the cluster (for example, a proteusin-bacteriocin hybrid is identical to bacteriocin-proteusin hybrid). Long-read sequencing of 'Ca. Eudoremicrobium' Leftover DNA (an estimated 6 ng) from the sample Malaspina MP1648, corresponding to the biosample SAMN05421555 and matching the short-read Illumina metagenomic readset SRR3962772, was processed for an ultralow input PacBio sequencing protocol to produce a >20 Gb Hifi Pacbio metagenome using the PacBio kits SMRTbell gDNA Sample amplification kit (100-980-000) and the SMRTbell Express Template Prep kit 2. 0 (100-938-900). In brief, the remaining DNA was sheared using a Covaris (g-TUBE, 52104), repaired and purified (ProNex beads).
Using the same parameters as described above, these samples were binned and 10 additional 'Ca. Eremiobacterota' MAGs were recovered. These 16 MAGs (which excludes the two that were already in the database) bring the total number of genomes in the extended OMD to 34,815. The MAGs were assigned to taxonomic ranks on the basis of their genomic similarity and GTDB placement. The 18 MAGs were dereplicated using dRep into 5 species (within-species ANIs were >99%) and 3 genera (within-genus ANIs ranged between 85% and 94%)  79  within the same family. Species representatives were manually selected on the basis of completeness, contamination and N 50. Proposed naming is available in Supplementary Information.
Trait and lifestyle prediction of 'Ca. Eudoremicrobiaceae' We used Traitar (v. 1. 1. 2) with the default parameters (phenotype, from nucleotides)  83  to predict general microbial traits. We investigated the potential predatory lifestyle on the basis of a previously developed predatory index  84  , which relies on the protein-coding gene content of a genome. Specifically, we used DIAMOND to compare the proteins from a genome to the OrthoMCL database (v. 4)  85  using the parameters --more-sensitive --id 25 --query-cover 70 --subject-cover 70 --top 20 and counted genes that matched predatory and non-predatory marker genes. The index is the difference between the number of predatory and non-predatory markers. As an additional control, we also analysed the genome of 'Ca. Entotheonella' factor TSY1 18 on the basis of its similar characteristics to 'Ca.
The medium components Bacto Tryptone and Bacto Yeast Extract were purchased from BD Biosciences. Sequencing-grade trypsin was purchased from Promega. Cloning of embA, embM and orf3 (embI) for protein expression. Gene sequences were extracted from BGC 75. 1 predicted by antiSMASH on the type material of 'Ca. E. malaspinii' (Supplementary Information). The genes embA (locus, MALA_SAMN05422137_METAG-scaffold_127-gene_5), embM (locus, MALA_SAMN05422137_METAG-scaffold_127-gene_4) and embAM (including intergenic region) were ordered as synthetic constructs in pUC57(Amp R ), with and without codon-optimization for expression in Escherichia coli. The gene embA was subcloned into the first multiple cloning site (MCS1) of pACYCDuet-1(Cm R ) and pCDFDuet-1(Sm R ) with BamHI and HindIII cut sites. The genes embM and embMopt (codon optimized) were subcloned into MCS1 of pCDFDuet-1(Sm R ) with BamHI and HindIII and in the second multiple cloning site (MCS2) of pCDFDuet-1(Sm R ) and of pRSFDuet-1(Kan R ) with NdeI/XhoI.
The embAM cassette was subcloned into pCDFDuet1(Sm R ) with the BamHI and HindIII cut sites. The gene orf3/embI (locus, MALA_SAMN05422137_METAG-scaffold_127-gene_3) was constructed by overlap extension PCR with primers EmbI_OE_F_ NdeI and EmbI_OE_R_XhoI, digested with NdeI/XhoI and ligated to pCDFDuet-1-EmbM(MCS1), which was digested using the same restriction enzymes (Supplementary Table  6 ). Restriction enzyme digestions and ligations were performed according to the manufacturer's (New England Biolabs) procedures. All constructs generated above were introduced into chemically competent E. coli DH5α and plated onto LB agar with appropriate antibiotic selection. Plasmids were purified from single colonies and sequenced using sequencing primers to verify proper insertion of genes (Supplementary Table  6 ).
Experimental validation of a novel proteusin pathway ('Ca. E. malaspinii', HLLJDLBE BGC 3. 1) Cloning (E. coli and M. aerodenitrificans). Genes of the type material of 'Ca. E. malaspinii' (Supplementary Information) in the antiSMASH-predicted BGC 3. 1 (region 1) on MALA_SAMN05422137_ METAG-scaffold_4, including Nhis-ereA (gene_139), ereI (gene_140), ereM (gene_141), ereD (gene_142) and ereB (gene_143) were codon-optimized and ordered as synthetic constructs as described in Supplementary Table  5  for expression in E. coli. Genes were further subcloned by Gibson assembly using the primers listed in Supplementary Table  5. All generated constructs were introduced into chemically competent E. coli DH5α and plated on LB agar with appropriate antibiotic selection. Plasmids were purified from single colonies and Sanger sequencing was used to verify proper insertion of genes (Supplementary Table  5 ).
Phylogenetic analysis of FkbM-family proteins. PfamScan classified EreM from 'Ca. E. malaspinii' as belonging solely to the FkbM methyltransferase (PF05050) family. To identify other FkbM-family proteins involved in natural product biosynthesis, the FkbM-family methyltransferase HMM (Methyltransf_21. HMM in Pfam_A) was used to query all protein-coding sequences in MIBiG (v. 2. 0) 30 using hmmsearch in HMMER v. 3. 1b2 (http://hmmer. org/) with the default parameters and the --cut_nc PFAM noise cut-off. Hits within 37 characterized BGCs in MIBiG were identified (Supplementary Table  5 ) and associated literature was manually assessed for experimental evidence of FkbM-family methyltransferase activity. Eight proteins were excluded on the basis of the FkbM hit falling outside the defined BGC cluster boundaries and having no apparent role in biosynthesis based on the final natural product structure (Supplementary Table  5 ).
Four FkbM family members had experimental evidence for O-methyltransferase activity in the form of heterologous expression or genetic studies. A total of 25 FkbM-family proteins were documented in publications by authors to have likely O-methyltransferase activity on the basis of the final natural product structure, biosynthetic logic and bioinformatic evidence. The summed 29 FkbM-family proteins were aligned using MUSCLE (v. 3. 8. 1551)  81  with two outgroups involved in proteusin biosynthesis, PoyE (AFS60641. 1) and AerE (AFS60641. 1) from a different methyltransferase protein family (PF05175). The protein alignment was assessed and all columns containing 50% or more gaps were removed using Trimal v. 1. 2. The trimmed alignment was used for phylogenetic model selection using IQ-TREE (v.
2. 0. 3)  96  and the V5+F+R5 model was selected based on best-fit using the Bayesian and Akaike information criteria. IQ-TREE was then used to estimate a maximum-likelihood phylogeny with 5,000 resamplings using the ultrafast bootstrap approximation  97. The scripts used for bioinformatic analysis are available at GitHub (https://github. com/serina-robinson/fkbm-bioinformatics). The HPLC was coupled to a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer using heated electrospray ionization in positive-ion mode (spray voltage, 3,500 V; capillary temperature, 268. 75 °C; probe heater temperature, 350 °C; S-lens level, 70). Full MS was detected at a resolution of 70,000 (AGC target: 1 × 10 6 ; maximum IT, 100 ms). MS 2 fragmentation was performed at a resolution of 35,000 (AGC target, 2 × 10 5 ; maximum IT, 100 ms, isolation window, 4.
1c , owing to the large number of points and space constraints. Fig. 1e  was plotted using the R package UpSetR (v. 1. 4. 0)  98. The trees in Fig. 2  were plotted using the R package ggtree (v. 3. 3. 0. 901)  99. 4 ). (b) We investigated the metagenomic detection of the 8,500 genes encoded by the Ca. E. taraoceanii representative, using methodology identical to the transcriptomic analyses (Methods). In samples where the 10 marker genes were detected, we counted the number of genes with one or more insert(s). We found that the 8,500 genes were detected in several ocean basins and different size fractions, with variation in detection rates likely due to variable sequencing depths across samples and datasets. This indicates, at least for the gene set covered by the reconstructed genome, that niche partitioning may be driven by gene expression changes rather than gene content variation.
Acknowledgements We thank the members of  Tara Oceans , which has been driven by a collaborative engagement of the  Tara Ocean Foundation , countless scientific project members and associated institutional partners. We are grateful for the availability of the data generated in the context of the  BioGEOTRACES, Malaspina, HOT, BATS  and  GORG  projects as well as the data compiled as part of the Mar Databases project, which formed an essential basis of this work. We thank the staff at the  ETH Zurich IT Services ,  HPC  facilities and  Biomolecular NMR Spectroscopy Platform  as well as the  Functional Genomics Center Zurich , in particular  A. Patrignani , for their support;  S. Booker  for the gift of the pBAD42-BtuCEDFB plasmid;  J. Vorholt ,  E. Rocha  and  R. Neher  for their feedback throughout this project;  P.
Hugenholtz  and  T. J. Williams  for their comments on the preprint version of this Article; and  A. Jermy  for his advice during the writing of this manuscript. This Article is contribution number 130 of  Tara Oceans. This work was supported by funding from the  ETH  and the  Helmut Horten Foundation ; the  Swiss National Science Foundation (SNSF)  through project grants  205321_184955  to S. S. ,  205320_185077  to J. P. and the  NCCR Microbiomes  ( 51NF40_180575 ) to S. S. ; by the  Gordon and Betty Moore Foundation  (https://doi. org/10. 37807/GBMF9204 )and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 101000392  (MARBLES) to J. P. ; by an  ETH  research grant  ETH-21 18-2  to J. P. ; and by the  Peter and Traudl Engelhorn Foundation  and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no.