dataesr/scientific-paragraphs-categorization huggingface dataset:
tRNA prediction was performed using tRNAscan-SE  [41]  and ribosomal rRNA genes were identified with meta_rna  [42]. Functional gene annotation was performed by comparisons against COG hmms  [43]  using and e-value cutoff of 1e-5, and the TIGRfams models  [44]  (using trusted score cutoffs --cut_tc) using the hmmer package  [45]. The assembled genomes were also annotated using the RAST server  [46]  and BlastKOALA  [47]. Additionally, all predicted proteins were also compared to the NCBI-NR database using BLASTP at e-value 1e-5.
Genome reconstruction Only contigs longer than 10 kb were used in the genome reconstructions. A contig was considered to belong to the phylum Chloroflexi if a majority of its genes gave best BLAST hits to this phylum. Contigs within each dataset were grouped using taxonomic affiliation, principal component analysis of tetranucleotide frequencies, GC% and coverage in different metagenomes as described previously  [48] [49] [50]. Tetranucleotide frequencies were computed using the compseq program (-word 4 -reverse) in the EMBOSS package  (Rice et al. 2000). Principal components analysis was performed using the FactoMineR package (function res. pca()) in R  [104]. Enzyme EC numbers were predicted using PRIAM  [51]  and metabolic pathway reconstruction was performed using Pathway Tools  [52].
Metagenomic fragment recruitment To avoid bias in recruitment results owing to the presence of highly related rRNA sequences, rRNA sequences in genomes were masked. After masking, recruitments were performed using BLASTN  [53] , and a hit was considered only when it was at least 50 nucleotides long, had a nucleotide identity of > 95% and an e-value of ≤ 1e-5. These cutoffs approximate species-level divergence  [54]. These hits were used to compute the RPKG (reads recruited per kilobase of genome per gigabase of metagenome) values that reflect abundance values and are normalized and comparable across different genomes and metagenomes (RPKG = Number of reads matching a contig or genome per Kb/Total number of bases in the metagenome expressed in Gb). In order to confirm that the reads assigned to SAR202 genomes using these cutoffs did not contribute to abundance of other microbes, we also compared the SAR202 assigned reads from three bathypelagic MALA-SPINA datasets (10 million reads each from SRR3961935, SRR3963457, SRR3965592) to the complete RefSeq collection (ca.
Phylogenomics and genome size estimation A reference phylogenomic tree was made using Phy-loPhlAN  [55] , the complete genomes of representatives from all known Chloroflexi classes and reconstructed MAGs of this study were added to the built-in tree of life in PhyloPhlAN (Supplementary Fig. S5 ). The two single cell amplified genomes SCGC-AB-629-P13 (Mid-Altlantic Ocean, 553 m depth) and SCGC-AAA240-N13 (Pacific Ocean, ALOHA station, Hawaii, 770 m depth) were also added as confirmed SAR202 representatives verified by 16S rRNA analysis (Supplementary Fig. S3 ). PhyloPhlAN uses USEARCH  [34]  to identify the conserved proteins and subsequent alignments against the built-in database are performed using MUSCLE  [56]. Finally, an approximate maximum-likelihood tree is generated using FastTree  [57]  with local support values using Shimodaira-Hasegawa test  [58].
This analysis confirmed that all MAGs belong to the phylum Chloroflexi and are phylogenetically most related to the SAR202 cluster branching together with the two confirmed SAR202 SAGs. For creating the sub-tree (Fig. 1 ) of MAGs with higher than 50% completeness, only Dehalococcoidia genomes were used as outgroups (as they are the closest neighbors of SAR202 (Supplementary Fig. S5 ) to reveal better the internal phylogenetic relationships within the incomplete genomes by maximizing the number of common proteins. For creating the sub-tree phylogeny shown in Fig. 1 , 50 conserved proteins in the reconstructed genomes and the reference genomes were identified using the COG database  [43]. These proteins were concatenated and aligned using Kalign (default parameters)  [59]  and the alignment was trimmed using trimAL (--gappyout)  [60].
Accession numbers The assembled genomic bins have been deposited to DDBJ/ EMBL/GenBank and can be accessed under the Bioproject PRJNA356693 and the accession numbers MUCH00000000, MUCI00000000, MUCJ00000000, MUCK00000000 (for Casp-Chloro-G1-4), MUCL00000000, MUCM00000000, MUCN00000000 (for Ae2-Chloro-G1-3), MUCO00000000, MUCP00000000, MUCQ00000000, MUCR00000000, MUCS00000000, MUCT00000000, MUCU00000000, MUCV00000000, MUCW00000000 (for Io17-Chloro-G1-9), MUCX00000000, MUCY00000000 (for MP-SAtl-SRR3965592-G1 and -G2), MUCZ00000000, MUDA00000000 (for MP-SInd-SRR3963457-G1 and -G2), MUDB00000000 (for MP-NPac-SRR3961935-G1).
SAR202 abundance in marine and brackish environments A broad overview of presence of phylum Chloroflexi representatives in the water column of marine and brackish environments using 16S rRNA metagenomic reads is shown in Supplementary Fig. S1A  (See also Supplementary Table  S4 ). While SAR202 were barely detectable in the photic zone and deep chlorophyll maximum (DCM) datasets (Western and Eastern Mediterranean, Caspian and Red Sea, HOTs and BATs), they comprised a substantial fraction of the microbial community in the datasets from the deep aphotic zones of eastern Mediterranean (Aegean ca. 5% and Ionian ca. 10%). The Caspian datasets also showed a progressive increase in the percentage of reads assigned to Chloroflexi with increasing depth, reaching a maximum of ca. 4% at 150 m, suggesting the existence of novel brackish SAR202 representatives.
In comparison, the Red Sea depth profile did not show any significant amounts of Chloroflexi members (maximum sample depth 500 m), suggesting that SAR202 does not show an ecological preference for the particular physico-chemical conditions characterizing the dark Red Sea  [64, 65]. An integrated view of the distribution of 16S rRNA reads assigned to Chloroflexi in metagenomic datasets from the surface, DCM, mesopelagic and bathypelagic ocean layers shows clearly an increasing abundance with depth (Supplementary Fig. S1B ). There are statistically significant differences between the surfacemesopelagic, surface-bathypelagic datasets, DCM-mesopelagic and DCM-bathypelagic datasets (Kruskal-Wallis test, P = <0. 001). Apart from the deep Mediterranean, the high abundance of Chloroflexi across different geographical locations in the global ocean and depths in the Pacific, Atlantic and Indian Oceans reaffirms the deep dark waters of global oceans as their preferred niche (Supplementary Fig.
S1  and Supplementary Table  S3 ). Chloroflexi even comprised up to 14% of microbial community in some deep ocean datasets of the MALASPINA expedition. They appear to be present in variable proportions at all deep aphotic zones for which data are available (largely -40 N to 44 S) without obvious preferences. Only one deep ocean dataset is available from the Antarctic Province that is outside these latitudinal bounds (TARA expedition, 790 m depth, -61. 98 S, -49. 36 W) and it also contained 3. 35 percent of 16S rRNA reads of Chloroflexi (Supplementary Fig. S2 ). Overall, these analyzes recapitulate previous findings indicating that the SAR202 abundance increases with depth  [7, [10] [11] [12] 14]  and has a global distribution in the marine water column.
Genome reconstruction and phylogenomics In order to obtain a first insight into the genomic repertoire and diversity of the SAR202 group, we chose a number of marine and brackish metagenomic datasets for assembly, annotation and taxonomic binning (Supplementary Table  S1 , see also Methods). These include two deep datasets from the Eastern Mediterranean (Aegean and Ionian Sea) and three representative deep datasets from the MALASPINA expedition (one each from South Atlantic Ocean, Indian Ocean and North Pacific Ocean). To identify brackish SAR202 representatives, existing assemblies from the Caspian Sea were used  [32]. We also included for assembly five deep chlorophyll maximum datasets from the Mediterranean. After the assembly, we selected long contigs ( > 10 Kb) in which the majority of predicted proteins gave best hits to Chloroflexi genomes.
As expected from the 16S rRNA results, very few Chloroflexi contigs could be identified in the DCM datasets as Chloroflexi are only minor constituents of the photic zone communities (Supplementary Fig. S1  and Supplementary Table  S5 ) and they were not considered further. The Ionian Sea (Io17) datasets from 3500 m depth had the highest number of assembled Chloroflexi affiliated genomic fragments (709 contigs) with total size of 17. 8 Mb (Supplementary Table  S5 ). Using differences in GC content, coverage in different datasets, and principal components analysis of tetranucleotide frequencies, (see Methods) these contigs were further segregated to 21 genomic bins (nine from the Ionian, three from Aegean, four from the Caspian, two each from South Atlantic and South Indian Ocean and one from the North Pacific Ocean, Supplementary Table  S5 ).
S3 ) to which the original SAR202 clones also belong  [10]. A nearly complete 16S rRNA gene was also found in the SAG Pac-SCGC-AAA240-N13 (1508 bp), which also affiliated to subcluster III. Only a single genome (SAG Atl-SCGC-AB-629-P13, 16S rRNA 1202 bp) was found to belong to subcluster V. The average nucleotide identity (ANI) comparison of all the MAGs and SAGs of SAR202 cluster shows that the five MAGs from MALASPINA datasets belong to two different species (Supplementary Fig. S4 ). The MAGs, SAGs and the previously available Chloroflexi genomes were inserted into a reference tree of life (3174 microbial genomes) using PhyloPhlAn  [55]. All MAGs and SAGs formed a large "Class" level group with two distinct sub-clusters within the phylum Chloroflexi (Supplementary Fig. S5  and Fig. 1 ). This was possible for all genomes except Caspian-Chloro-G1 because of the small size (only 0.
In what follows, we focused our analyzes largely on the twelve most complete MAGs (seven genomes from deep Ionian Sea, two genomes from the Caspian Sea, one genome each from assembled deep MALASPINA datasets, MP-NPac-SRR3961935, MP-SInd-SRR3963457, and MP-SAtl-SRR3965592) and the two SAR202 cluster SAGs.
SAR202 global distribution and vertical stratification All MAGs reconstructed in this study showed highest recruitment values in their environment of origin along with higher abundances in the deeper strata of the water column (Fig. 1  and Supplementary Fig. S6 ). Similar trends were observed in the global ocean, where the surface and deep chlorophyll maximum datasets were largely devoid of SAR202 representatives, but the deep aphotic datasets showed a far higher abundance for reconstructed MAGs and SAGs of the SAR202 cluster (Supplementary Fig. S7A ). Nine SAR202 genomes, including three from the Ionian (Io17-G1, Io17-G5, Io17-G8, 3500 m), two each from the South Atlantic (MP-SAtl-SRR3965592-G1 and -G2, 3199 m) and Indian Ocean (MP-SInd-SRR3963457-G1 and -G2, 4000 m), one from North Pacific (MP-NPac-SRR3961935-G14004 m depth) and Pac-SCGC-AAA240-N13 (770 m depth, Pacific Ocean) appear to be widely distributed in several deep aphotic datasets (Supplementary Figs.
S6  and S7B ). However, on segregating datasets into mesopelagic (up to 1000 m deep, 19 datasets) and bathypelagic (1500-6000 m, 31 datasets) a different recruitment pattern is evident. Fig. 3  Mosaic schematic view of the metabolic traits in all available genomes from SAR202 cluster. The parts affiliated with sulfur compound metabolism are highlighted in gray While Ae2-Chloro-G1, Ae2-Chloro-G3, Io17-Chloro-G5 recruited more reads in the mesopelagic datasets, Io17-Chloro-G1, Io17-Chloro-G8, all MALASPINA genomes and Pac-SCGC-AAA240-N13 recruited even more from the bathypelagic datasets (Fig. 2 ). This differential recruitment in meso-and bathypelagic layers of the ocean suggests a vertical stratification of SAR202 in the aphotic zone. It has been suggested that the mesopelagic layer is more influenced by the upper layers and the bathypelagic realm has a comparatively more stable status regarding different physicochemical features  [72] [73] [74] [75].
Despite the distant origin of the genomes from the MALASPINA datasets, they show high recruitment values in almost all available deep bathypelagic datasets suggesting the presence of the same species in widely different geographic areas (Southern Indian Ocean, South Atlantic, North Pacific Ocean). Remarkably, a differential vertical stratification was also seen for currently available deep specific SAR11 SAGs, (isolated from the mesopelagic zone, 770 m depth), that are more abundant in the mesopelagic than in bathypelagic datasets (Fig. 2 ). These deep specific SAR11 SAGs appear also to be much more abundant in the mesopelagic samples than the cultured isolates in the photic zone (Supplementary Fig. S8 ). However, another pattern is also evident for some SAR202 genomes, with high abundances at the place where the original metagenome was obtained from, and lower at others.
These observations likely reflect biogeographic patterns resulting from local environmental selection. Nevertheless, these results suggest that at least some SAR202 genomes recovered here are primarily bathypelagic, extremely abundant, much more than the currently available SAR11 representative genomes are (either from deep or surface), being among the most abundant microbial genomes retrieved from the aphotic zone at large. The brackish SAR202 MAGs reconstructed from metagenomes of the Caspian Sea also display a clear vertical stratification similar to the marine habitat along the Caspian depth profile datasets (Supplementary fig. S6 ) although at shallower depths than their marine bathypelagic counterparts.
Conclusions The dark ocean is several times larger than the photic zone in sheer size and the majority of its microbial inhabitants are unknown. In this work, we have reconstructed genomes of the uncultivated SAR202 cluster (phylum Chloroflexi), which appears particularly abundant in the aphotic zone of global oceans. Moreover, the distribution of SAR202 sequences in the currently available deep metagenomic datasets (n = 50) suggests that SAR202 populations are vertically stratified, with distinct mesopelagic and bathypelagic groups. In particular, at least five genomes (belonging to only two distinct species) appear to be widely distributed in geographically distant regions in the bathypelagic zone of the world's oceans. Based on the higher abundance of the SAR202 cluster in the metagenomes of 0.
-0. 8 µm size fraction in the MALASPINA datasets, it is expected that these are free living microbes. Genome size estimations suggested sizes in the range of 2. 5-3 Mb, that is in line with the large cell sizes observed by FISH  [10]. Their larger cell-sizes coupled with higher abundances in the deep ocean suggest they alone make up for a significant fraction of the total biomass in this oligotrophic habitat. It must be considered however that total cell numbers are an order of magnitude lower in the deeper oceans than at the surface  [99]. The genomes of the SAR202 group described here contain several pathways for metabolizing multiple organosulfur compounds as carbon, nitrogen and sulfur sources, which in turn implies that at least some of these compounds (e. g. , DMS, DMSP, taurine, hypotaurine) may be more widely available in the deep ocean than suspected before  [100, 101].
Acknowledgements MM was supported by the  Czech Academy of Sciences  ( Postdoc program  PPPLZ application number  L200961651 ). RG was supported by the research grants  13-00243 S  and  17-04828  S from the  Grant Agency of the Czech Republic. MAA was supported by the grant  MI-1391-20  from the  Iranian Biological Resource Centre (IBRC). FRV was supported by projects "VIREVO"  CGL2016-76273-P [AEI/FEDER , UE], from  Agencia Estatal de Investigación (AEI) , " REDES DE EXCELENCIA "  CONSOLIDER-CGL2015-71523-REDC  from the  Spanish Ministerio de Economía, Industria y Competitividad  and  PROMETEO II/2014/012  " AQUAMET" from Conselleria d'Educació, Cultura i Esport, Generalitat Valenciana. Work in PLG's lab was funded by the  French National Agency for Research  ( ANR-08-GENM-024-002 ) and the  European Research Council (ERC)  under the  European Commission   7th Framework Program  ( ERC Grant Agreement 322669 ).
e. without volcanic aerosols. A simulation (hereafter called Sat-sim) has been set up by prescribing time-dependent variations in the stratospheric sulfate aerosol content from 1 km vertical resolution extinction measurements by the Optical Spectrograph and Infrared Imaging System (OSIRIS) instrument onboard the Odin satellite. OSIRIS aerosol extinction data used in this study are the validated version 5 retrieved at 750 nm  (Bourassa et al. , 2012). They compare well with the profiles inferred from the STAC balloon-borne aerosol counter  (Jégou et al. , 2013) , thus providing confidence in the use of the data as a basis for considering time-dependent changes in aerosol content. OSIRIS data were averaged daily and zonally over 10 • latitude bins. A standard Mie scattering model (  Van de Hulst, 1957; Wiscombe, 1980; Steele and Turco, 1997)  has been run to convert extinction values to H 2 SO 4 mixing ratios from parameters of log-normal unimodal size distributions provided by the STAC instrument and used in the work of  Jégou et al.
(2013)  in the Sarychev aerosol conditions. The derived H 2 SO 4 mixing ratios have then been incorporated into the model over the period of the presence of the Sarychev aerosols in the Northern Hemisphere lower stratosphere, i. e. from the beginning of July 2009 onwards. The simulation has been conducted until October 2009 because OSIRIS data at high latitudes are lacking beyond this period due to decreasing solar illumination. We have conducted another type of simulation (hereafter called Bal-sim) consisting in adjusting the input H 2 SO 4 mixing ratios so that the model output matches SADs observed by the STAC aerosol counter. Although similar aerosol SAD values were observed by  Kravitz et al. (2011)  in November 2009, i. e. ∼ 2 months after the STAC measurements as mentioned by  Jégou et al.
Figures  5  and 6  present the measured profiles of NO 2 obtained by the SPIRALE, SALOMON and DOAS instruments, together with REPROBUS model outputs for altitudes below 20 km where the Sarychev aerosols were present. In contrast to the reference simulations, the Bal-sim simulations constrained by the range of aerosol SADs observed by STAC show significant improvement in comparison with the non-volcanic calculations with, for instance, average differences of 3 ± 20 % for SPIRALE-07082009. Results from the Sat-sim simulations driven by OSIRIS satellite data are very close to the Bal-sim results and are only shown for the SPI-RALE flights. It may be noted that the REPROBUS calculations do not reproduce some of the vertical structures detected by the SPIRALE instrument, i. e. between 17. 5 and 19.
, 2013). The model is a CTM driven by ECMWF offline meteorological data and does not describe radiative processes. In other words, volcanic aerosol radiative effects do not directly interact with the circulation computed by the model. Radiative processes from the injection of volcanic aerosols into the tropics have been shown to have an impact on mean meridional circulation and ozone transport  (Brasseur and Granier, 1992; Pitari et Rizi, 1993). In our study, effects of the Sarychev aerosols on midlatitude stratospheric dynamics, if any, are at least of the first order intrinsically taken into account in the ECMWF analyses used for all simulations. REPROBUS does not take into account the aerosol impact on calculated photolysis rates, which is likely to result in some differences between models when this process is computed or ignored  (Pitari and Rizi, 1993; Pitari et al.
5 % around 16 km) because the saturation of the NO x / NO y response limits the increase in HO x and in active chlorine (ClO) by enhanced HO x , precluding important ozone loss rates. Moreover, stratospheric temperatures remained too high (i. e. mainly above 215 K) for efficient heterogeneous conversion of ClONO 2 to active chlorine, which could have led to significant ozone depletion. For these temperature conditions, Reaction (R2) is not expected to compete with N 2 O 5 hydrolysis in the NO y partitioning  (Fahey et al. , 1993; Cox et al. , 1994). However, limitations in our model simulations also contribute to some model-measurement discrepancies. A first major difficulty is to drive the model simulations with representative and consistent inputs in term of volcanic aerosol loading. To address this issue, two different model runs for aerosol forcing have been performed, one using OSIRIS satellite data converted to aerosol SAD fields and the other one from in situ balloon-borne observations.
The OSIRIS satellite data represent zonally and daily averaged values of SAD, which may vary from a 3-D construction based on the local surface areas. The possible presence of aerosol streamers (geographical variations in the aerosol content) resulting from the transport of the volcanic aerosols over the Northern Hemisphere present from mid-July to September 2009 is likely to affect the N 2 O 5 abundances locally and regionally and, to a lesser extent, NO 2 and HNO 3  (Jucks et al. 1999; Küll et al. , 2002). If our aerosol SAD dataset had been obtained when the local concentrations were higher than the zonal mean values, then the calculated rate of the heterogeneous reactions would be biased low and calculated NO x and HNO 3 abundances would be systematically biased high and low respectively.
This is not, however, evident in all our comparisons from simulations based on OSIRIS aerosols. The second type of aerosol-constrained simulation uses SADs from balloon-borne observed profiles. By definition, such in situ observations deal with a particular location. Extrapolating in situ derived SADs to drive a 3-D model on a large scale may induce inaccurate simulations of the chemical impact of the aerosols  (Kondo et al. , 2000). To account for this SADrelated uncertainty, our simulations based on in situ data encompass the range of SADs derived from the STAC balloonborne observations over the August-September 2009 period. Both satellite-and balloon-driven simulations give similar results in terms of NO 2 and HNO 3 amounts, possibly because the in situ observations represent the aerosol loading at the northern midlatitudes well.
Another explanation is that the saturation effect (roughly when SADs become larger than 3 µm 2 cm -3 ) of the NO x / NO y ratio is more relevant for the range of observed SADs than spatiotemporal inhomogeneities. Secondly, adequate modelling of transport is also crucial for the partitioning of NO y. Processes that control the vertical profiles of NO 2 and HNO 3 in the stratosphere are based on a complex interplay between dynamics and chemistry with the key issue being to accurately simulate total NO y , which may be not systematically achieved with 3-D CTM calculations. Improved simulations of transport can be obtained by combing operational analyses with forecasts to construct 3-hourly meteorological data to drive the CTM  (Berthet et al. , 2006). We have applied this strategy in the present study.
Data availability Balloon data can be accessed on the ESPRI database (ESPRI data Centre, http://ether. ipsl. jussieu. fr/etherTypo/index. php? id=1538&L=1) and are sorted by instrument names  (ESPRI data Centre, 2016). Appendix A: Technical description A1 The STAC aerosol counter Aerosol size distributions are provided in the 0. 4-5 µm diameter size range  (Ovarlez and Ovarlez, 1995; Renard et al. , 2008). Since 2008, the number of available size classes has been increased from 7 to 14 within this size range  (Renard et al. , 2010). The counting uncertainty is obtained from the statistical probability given by Poisson counting statistics  (Willeke and Liu, 1976). This uncertainty, defined as the relative standard deviation, is 60 % for aerosol concentrations of 10 -3 cm -3 , 20 % for 10 -2 cm -3 and 6 % for concentrations higher than 10 -1 cm -3.