dataesr/scientific-paragraphs-categorization huggingface dataset:
CAF received support from the  European Research Council  (https://erc. europa. eu/)under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement n. 307532, the  United States National Institutes of Health  (https://www. nih. gov/)Grants  U01 HL135842  and  R01 HL105297 , the  American Heart Association  grant  19AIML34910010. CJA received support under the  Wellcome Trust  (https://wellcome. ac. uk/ )  Institutional Strategic Support Fund grant  to King's  College London  under grant number [ 204823/Z/16/Z ]. JM is funded by the  National Health and Medical Research Council of Australia  (https://www. nhmrc. gov. au/) and the  National Heart Foundation of Australia  (https://www. heartfoundation. org. au/). DN:  EP/R003866/1   Engineering and Physical Sciences Research Council  (EPSRC) https://epsrc.
The deep sea, the largest ocean's compartment, drives planetary-scale biogeochemical cycling. Yet, the functional exploration of its microbial communities lags far behind other environments. Here we analyze 58 metagenomes from tropical and subtropical deep oceans to generate the Malaspina Gene Database. Free-living or particle-attached lifestyles drive functional differences in bathypelagic prokaryotic communities, regardless of their biogeography. Ammonia and CO oxidation pathways are enriched in the free-living microbial communities and dissimilatory nitrate reduction to ammonium and H 2 oxidation pathways in the particle-attached, while the Calvin Benson-Bassham cycle is the most prevalent inorganic carbon fixation pathway in both size fractions. Reconstruction of the Malaspina Deep Metagenome-Assembled Genomes reveals unique non-cyanobacterial diazotrophic bacteria and chemolithoautotrophic prokaryotes.
The Malaspina 2010 Circumnavigation Expedition aimed to address such knowledge gaps by surveying bathypelagic microbes in the tropical and subtropical oceans  36. Here, we analyze 58 deep-sea microbial metagenomes from that expedition and release the Malaspina Gene DataBase (M-GeneDB), a valuable deep-ocean microbial community genomic dataset. In addition, we constructed the Malaspina Deep Metagenome-Assembled Genomes (MDeep-MAGs) catalog to explore the metabolic potential of the deep-sea microbiome.
Results and discussion Gene-centric taxonomic composition of the Malaspina Gene DataBase (M-GeneDB). The 58 microbial metagenomes were sampled between 35°N and 40°S from 32 stations (St) in the North and South Pacific and Atlantic Oceans, the Indian Ocean, and the South Australian Bight (Fig. 1a ) from an average water depth of 3731 m (Fig. 1b ). Two different plankton size fractions were analyzed in each station representing the FL (0. 2-0. 8 µm) and PA (0. 8-20 µm) microbial communities, the later fraction also including bathypelagic microbial eukaryotes. A total of 195 gigabases (Gb) (6. 49 × 10 8 read pairs) of the data with an average of 3. 36 Gb per sample were generated (Supplementary Data 1). The number of predicted genes was 4. 03 million (M) from the 58 assembled bathypelagic metagenomes.
M-GeneDB was first built by clustering nucleotide sequences at 95% sequence identity to remove redundancy and for consistency with the Tara Oceans gene set, yielding 1. 12 M non-redundant unique sequence clusters (referred hereafter as genes). The M-GeneDB was next integrated into the recently updated Ocean Microbial-Reference Gene Catalogue of Tara Oceans (OM-RGC. v2;  37 Supplementary Data 1) by further clustering both databases. The novelty of the M-GeneDB is represented by a total of 647,817 Malaspina-exclusive genes that account for 58% of the total genes (Fig. 1c ) that were absent from the Tara Oceans global survey  32  , representing a unique gene repertoire complementary to the epipelagic and mesopelagic genes reported by Tara Oceans  34, 37  (Fig. 1b, c ). The nature of the novelty of the M-GeneDB lies in its 63% of "unknown" genes without functional annotation.
The remaining 37% genes were linked to transporters, including two-component systems and ABC transporters (>9. 7%), followed by DNA repair and recombination proteins (2. 6%) and peptidases (1. 8%; Supplementary Fig. 2 ; Supplementary Data 2). Other genes >1% were secretion systems, aminoacidic related enzymes, or quorum sensing genes (Supplementary Fig. 2 ). These results agree with previous findings of the prevalence of these genes in the deep ocean  29, [38] [39] [40]. Although we acknowledge that this comparison represents a snapshot of the currently known databases, this number reflects the vertical functional stratification and dichotomy between photic and aphotic microbiomes, as well as the differences between the mesopelagic and the bathypelagic. Despite the role of sinking particles in delivering epipelagic microbes to the deepsea  27  , the presence of endemic bathypelagic microorganisms has been described in several of the Malaspina sampled stations  41.
On average 61 (± 14% SD) of the predicted genes in each sample were exclusively found in the present dataset, which highlights the unique gene content of the bathypelagic microbiome (Fig. 1c  and Supplementary Data 1). Each sample contained 14 ± 9% specific genes not found in any other Malaspina samples (Fig. 1c  and Supplementary Data 1). Station St62 in the Indian Ocean, sampled at 2400 m, showed the highest fraction of sample-specific genes with 43% of the total being also highly different in terms of taxonomic community composition  26  (Supplementary Fig. 3 ). This sample, together with other four stations located in Brazil (St32), North Atlantic American (St134), and Guatemala basins (St121), that harbored more than 30% of sample-specific genes, were all from the PA size fraction and associated with circumpolar deep water and North Atlantic Deep Water masses (Supplementary Data 1).
8-20 µm) fractions. potential relevant pathways in the deep ocean related to inorganic carbon fixation, nitrogen, sulfur, methane, and hydrogen metabolisms were searched in the bathypelagic metagenomic dataset (Fig. 3 ). Out of these key marker genes, 49 KOs were present in the M-GeneDB (Supplementary Data 8). Among them, the key genes of four different inorganic CO 2 -fixation pathways. The Calvin-Benson-Bassham (CBB) cycle, identified by the ribulose-bisphosphate carboxylase large subunit (RuBisCO, K01601: rbcL) was widely distributed in the dataset followed by the 3-HP (K14468: mcr, K08691: mcl), whereas the archaeal 3hydroxypropionate-4-hydroxybutyrate cycle (K15039), and the rrTCA cycle (K15230: aclA/ K15231: aclB) displayed a narrow  distribution restricted to a single station each: St62 and St53, respectively, both in the Indian Ocean (Fig.
3 ). RuBisCO occurred in 48% of the samples (Supplementary Data 8), and on 1. 3% of the potential cells on average (based on recA normalization) with two significant deviations observed in the PA fractions at St67 (South Australian Bight, Indian Ocean) and St134 (North American basin, Atlantic Ocean) where it peaked up to 12% of the cells (Fig. 3 ). However, there were no significant differences between both size fractions (Wilcoxon test, P value = 0. 135). Nitrification through ammonia  15, 50, 51  and nitrite oxidation 18 have been postulated as the main sources of energy for carbon fixation in the dark ocean. For ammonia oxidation, we looked for Fig. 4  Comparison of metabolic pathways in the free-living and particle-attached microbial communities from the bathypelagic ocean. Normalized gene abundance (reads/recA reads) of 49 marker genes indicative of different autotrophic carbon-fixation pathways, nitrogen, sulfur, methane, and hydrogen metabolisms in the Malaspina Gene DataBase.
Gene abundances from KOs belonging to the same pathway and KEGG module level have been plotted together (Supplementary Data 7). From top to the bottom: Reductive citrate cycle (rTCA): K15230, K15231); hydroxypropionate-hydroxybutyrate cycle: K15039; dicarboxylate-hydroxybutyrate cycle|hydroxypropionate-hydroxybutylate cycle: K15016; Calvin cycle: K00855; K01601 and K01602; 3hydroxypropionate bi-cycle|ethylmalonyl pathway|formaldehyde assimilation: K08691; 3-hydroxypropionate bi-cycle: K14468, K14470 and K09709; CO oxidation (C1 metabolism): K03520; K03519 and K03518; H 2 -oxidation: K00436; methane oxidation: K16158; coenzyme M biosynthesis: K05979, K06034, K08097 and K13039; C1 metabolism/methanogenesis: K00320, K00200, K00201, K00202, K00672, K03390, K14083, and K00577; nitrification|methane oxidation: K10944, K10945, and K10946; Nitrogen fixation: K02588; nitrification|denitrification|dissimilatory nitrate reduction: K00370 and K00371; dissimilatory nitrate reduction (DNRA): K00362 and K00363; denitrification|dissimilatory nitrate reduction: K00374, K02567, and K02568; denitrification: K00368, K15864, K04561, K02305, and K00376; assimilatory nitrate reduction: K00367 and K00372; dissimilatory sulfate reduction: K00394, K00395 and K11181.
3 ). Thus, exploring the co-occurrence of other key genes of each pathway in metagenomic bins is necessary for their validation as shown below. Other relevant nitrification genes, such as the hydroxylamine dehydrogenase (K10535: hao) or the key enzyme for the anaerobic ammonia oxidizers (K20932/ K20935: hdh; hydrazine dehydrogenase; anammox bacteria), normally present within the oxygen minimum zones in the mesopelagic ocean, were absent from our bathypelagic metagenomic dataset. This reflects different biogeochemical processes occurring in the anoxic mesopelagic OMZ and the oxic bathypelagic oceans. H 2 and CO oxidation were explored as potential alternative energy sources  52. H 2 -oxidation has been described in hydrothermal vents  53  or subsurface microbial communities  54  but we also found it in 24% of the samples (Fig.
Dissimilatory nitrate reduction to ammonium (DNRA) was also detected. Due to the absence of the periplasmic pentahaem cytochrome c nitrite reductase nrfA 58 in our dataset (Supplementary Data 7), nitrate reduction to ammonium seemed to be catalyzed by the cytoplasmic NADH-dependent nitrite reductase nirB or by its two-subunit variant nirBD (K00362: nirB -K00363: nirD)  59. The potential DNRA and other metabolisms such as denitrification (K00368: nirK; K02305: norC; K04561: norB; K00376: nosZ) and sulfate reduction (K00394: aprA-K00395: aprB) were also present. Up to 27% of the samples contained denitrification-related genes and 90% of the samples displayed marker genes of sulfate reduction (Fig. 3  and Supplementary Data 8). The prevalence of such metabolisms in well-oxygenated waters might be explained by the formation of microenvironments inside organic aggregates or particles, where intense respiration may result in local O 2 exhaustion  60.
Diversity and novelty of the Malaspina Deep MAGs catalog. Co-assembly of the 58 bathypelagic metagenomes allowed the reconstruction of 619 non-redundant bins with 57. 2% mean genome completeness, accounting for a total of 1. 4 Gbp. A total of 317 of these bins had ≥50% genome completeness values and <10% contamination and fulfilled the quality standards  61  to be considered as medium or high-quality Metagenome-Assembled Genomes (MAGs). These 317 MAGs, with 84. 2% average genome completeness, represented a total of 936 Mbp. This dataset is referred to here as the Malaspina Deep MAGs catalog (MDeep-MAGs). A total of 298 bacterial MAGs were taxonomically assigned to 22 phyla and 19 archaeal MAGs were assigned to 4 phyla based on the GTDB taxonomy (Fig. 5 ). In addition, two low-quality bins (0046 and 0224) were assigned to eukaryotes, potentially to fungal taxa.
Overall, these recruitments were higher than those reported for the photic layer of the global ocean Tara Oceans dataset (6. 84% of the metagenomic reads)  62  and specifically for the Mediterranean Sea, which showed average mapping rates of 14% of the metagenomic reads for different microbial size fractions  63. Such discrepancy may be due to probable differences in sequencing depth and methodological variations in the co-assembly, filtering, and mapping between studies. MAG completion estimates based on domain-specific singlecopy core genes  64  ranged from 50. 3 to 100% (Supplementary Data 9). The mean genome assembly size of the 317 MAGs was of 2. 9 Mbp, ranging from 0. 3 to 10. 9 Mbp (Supplementary Data 9). The smallest genome corresponded to an archaeon (MAG0485) of the phylum Nanoarchaeota, first described as obligate symbionts with reduced genomes in marine thermal vent environments  65  , but later found in a wide range of environments and temperatures.
The largest genome (MAG0539) belonged to a member of the family Sandaracinaceae in the Myxococcota phylum. So far, there is only one cultured member of this bacterial family, Sandaracinus amylolyticus DSM 53668, a starchdegrading myxobacterium, also with a large genome (10. 3 Mb)  66. The degree of taxonomic novelty of the MDeep-MAGs associated with Myxococcota was particularly high, with four MAGs representing new potential genera. Therefore, we may have uncovered new niches in the bathypelagic ocean for such largegenome microbes, some of which have been shown to produce antibacterial, antifungal, and other bioactive metabolites  67. Genome-resolved metabolic capabilities of the bathypelagic ocean microbiome. The MDeep-MAGs catalog represents the most extensive genome dataset from the bathypelagic ocean built to investigate the distribution and functional capability of deepocean microorganisms (Fig.
6 ). In the following section, we describe the most important features in the dataset. Non-cyanobacterial diazotrophs (NCDs). The ecological relevance of non-cyanobacterial diazotrophs (NCDs) in the deep ocean remains unclear as the availability of carbon substrates seems unlikely to support the costly energetic demands of N 2 fixation. However, NCDs are widely distributed across oxygenated oceans and are phylogenetically diverse  [68] [69] [70] [71]. New genotypes associated with Planctomycetota and Proteobacteria were recently found in the surface global ocean  62  , and some of them were actively transcribed even at mesopelagic depths  37. The diversity of NCDs in the deep ocean is mostly known from the detection of the nifH gene  72, 73. Three NCD MAGs were reconstructed in the Malaspina dataset that harbored the nifH gene and other structural genes of the nif operon such as nifKD (Supplementary Data 10): two were Alphaproteobacteria (MAG0509 and MAG0177) related to Salipiger thiooxidans and genus Novosphingobium, both with almost complete genomes (>94% completeness) that were detected exclusively in the PA fraction.
Chemolithoautotrophs Mixotrophs Heterotrophs Fig. 6  Metabolic potential in selected metagenome-assembled genomes (MAGs) from the bathypelagic ocean. A total of 25 MDeep-MAGs were selected based on the presence of metabolic pathways with potential for chemolithoautotrophy, mixotrophy, and nitrogen fixation (non-cyanobacterial diazotrophs, NCDs). AOA ammonia-oxidizing archaea, SOB sulfur-oxidizing bacteria, NCDs non-cyanobacterial diazotrophs, NOB nitrite-oxidizing bacteria, AOB ammonia-oxidizing bacteria. Their taxonomic assignment at the maximal possible resolution is shown at the top, followed by the occurrence of each MAG in samples of the Malaspina Gene DataBase, and the genome completeness (%) of each MAG. On the right, metabolic pathways involved in inorganic carbon fixation (green), sulfur (red), nitrogen (blue) are shown as well as the KOs that participate in these pathways.
The other genomes showed a rather limited distribution (Supplementary Data 10). Overall, MAGs containing RuBisCO Form I or II genes occurred on average in 22% of the samples, revealing that mixotrophy is a relatively common trait in the bathypelagic ocean. Some of these chemolithoautotrophic/mixotrophic MAGs are within the top 50 most abundant MAGs of our dataset, representing abundant taxa of the bathypelagic deep ocean (Supplementary Fig. 8 ). The MAG0176 (NOB) was in position 25, the MAG0052 ranked 29, and the MAG0166 was the 42 most abundant MAG in the dataset (Supplementary Data 14). Our results differed from the MAGs reconstructed from particleassociated bacteria collected in sediment traps at 4000 m depth in the North Pacific Subtropical Gyre, in which key enzymes involving autotrophic pathways were not detected  31.
Our findings, together with the enrichment of different metabolic pathways associated with Conclusions. The global metagenomic assessment of the deepocean microbiome with the creation of the Malaspina Gene DataBase and Malaspina Deep MAGs catalog uncovers potentially novel orders and classes of deep-ocean microorganisms and describes potentially relevant biogeochemical processes of the deep-ocean microbiome in the tropical and subtropical bathypelagic oceans. Our results show metabolic differentiation reflected in contrasting functional gene repertoires, between the FL and the PA prokaryotic assemblages with also a certain degree of functional patchiness across oceans and basins. Our study also provides evidence for diverse metabolic strategies in the deep ocean. The widespread distribution of different autotrophic pathways in the deep ocean and the prevalence of alternative energy sources such as CO oxidation, sulfur oxidation, and H 2oxidation, supports the role of a multitude of autotrophic processes in subsidizing the heterotrophic metabolism supported by export flux from the photic layer.
Data acquisition. The description and availability of the different datasets can be found in Supplementary Table  1. Our Malaspina bathypelagic microbial metagenomes were sequenced by the DOE´s Joint Genome Institute (JGI). Raw and clean sequences were therefore obtained from DOE's JGI Integrated Microbial Genomes and Microbiomes (IMG/MER), as well as several data analyzed (Metagenome Annotation Standard Operating Procedure for IMG, Nov 2012) available as JGI proposal ID 300784. Functional abundance tables contained the number of reads in each sample for every functional category within four different functional annotations: Cluster of Orthologous Groups 45 (COG), KEGG orthologs  44  (KOs), Protein families  46  (Pfam), and Enzyme Commission classification (EC). In all cases, abundance tables were downloaded directly from the IMG repository (accession numbers in Supplementary Table  1 ) using the "estimated gene copies" option  94.
These tables represented the read counts for every annotated function in every sample coming from assembled gene data, taking into account the mean contig coverage and corrected by gene length. Additional metadata were collected during the expedition including environmental variables (salinity, potential temperature, and oxygen concentration), sampling station coordinates (latitude, longitude, and depth), and auxiliary data for every sample (filter size, ocean basin, and water mass; Supplementary Data 1). Statistics and reproducibility. For every functional abundance table, a subsampled equivalent table was constructed in order to avoid biases due to the varying sequencing depth between samples. Subsampling was performed by generating a randomly rarefied table without replacement from the original one with the "rrarefy" function in vegan  95  package within R software  96.
Generation of the Malaspina Gene Database (M-GeneDB). All 3,872,410 predicted coding sequences larger than 100 bp from each assembled metagenome were pooled and clustered at 95% sequence similarity and 90% sequence overlap of the smaller sequence using cd-hit-est 97 v. 4. 6 using the following options: -c 0. 95 -T 0 -M 0 -G 0 -aS 0. 9 -g 1 -r 1 -d 0 to obtain 1,115,269 non-redundant gene clusters (from now on referred simply as genes). These gene clusters were aligned to UniRef100  98  (release 2019-10-16) with diamond blastx 99 (v0. 9. 22; e-value 0. 0001). The least common ancestor taxonomic assignation of UniRef100 best matches was obtained from NCBI's taxonomy database  100  (release 2020-01-30). In order to explore the novelty of the M-GeneDB, we clustered it with the 46,775,154 non-redundant sequences from the Tara Oceans Microbial Reference Gene Catalog version 2 (OM-RGC.
v2)  37 using cd-hit-est-2d 97 v. 4. 6 with the following options: -c 0. 95 -T 48 -M 256000 -G 0 -aS 0. 9 -g 1 -r 1 -d 0 to obtain a final catalog of 47,422,971 genes. Taxonomy of protist, prokaryotes, and viruses from metagenomic reads Protist. 18S miTags were extracted from the metagenomes and subsequently analyzed following Logares et al. 93. These miTags were mapped at 97% similarity using Uclust  101  to the PR2 database  102  that was pre-clustered at 97% similarity (Supplementary Data 3). Bacteria and archaea. Prokaryotic taxonomical tables were downloaded from IMG [Compare Genomes/Phylogenetic Dist. /Metagenomes vs. Genomes] using the "estimated gene copies" option (i. e. , estimated by multiplying by read depth when available instead of using raw gene count) and the 60+ Perc. Identity option.
001). Then the metagenomic hits were searched against UniRef100 98 using BLAST (e-value <1e-3). As a result, 365 metagenomic peptides had best hits to virophage sequences, of which 50 sequences exhibited >95% sequence identity to homologs from the Mavirus virophages infecting Cafeteria roenbergensis. Viral signal analysis. The marker gene terL (large subunit of the terminase) was used to assess the diversity of bacterial and archaeal viruses in the deep-sea microbial metagenomes. terL genes were identified in the proteins predicted from the 58 metagenomes through hits to the PFAM domains PF04466 (terminase_3), PF03237 (terminase_6), PF03354 (terminase_1), and PF05876 (terminase_GpA). Genes shorter than 100 bp were discarded, leading to a dataset of 485 terL genes. First, a family-level affiliation (i.
e. , Myoviridae, Podoviridae, or Siphoviridae) of these sequences was obtained from a best blastp hit to the RefseqVirus database (threshold of 50 on bit score, 0. 001 on e-value, and 50 on % of amino acid identity; Supplementary Table  S6 ). For each sample, viral community composition was then calculated based on this family-level affiliation and the normalized coverage of the contig (i. e. , contig coverage divided by contig length and sequencing depth of the sample, as in Brum et al. 105  ). Next, these 485 "deep-sea" terL genes were clustered with all terL from the RefseqVirus database (n = 899, v72, 09-2015), from "environmental phages" in Genbank (n = 456, downloaded on 07-2015), from the VirSorter Curated Dataset  106  (n = 6600), and from the Global Ocean Virome Dataset  106  (n = 2674, sequences from 91 Epi-and Mesopelagic viromes from Tara Oceans and Malaspina expedition) at 98% of nucleotide identity (threshold most consistent with genome-based population definition, i.
When possible, marker enzymes were assigned to a specific module within a map (e. g. , enzymes only participating in denitrification module within the nitrogen metabolism map). Corrected abundance estimation for marker enzymes was obtained by dividing the number of reads from the EC functional abundance table (without subsampling) by the number of reads assigned to the prokaryotic singlecopy gene recA (selected as COG0468). A table selection of 83 KOs was built (Supplementary Data 7) representing the main key marker genes for different metabolic pathways relevant in the deep ocean. Of those, a total of 49 KOs were found in the Malaspina Deep-Sea Gene Collection (59%; Supplementary Table  8 ). Nonmetric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA).