dataesr/scientific-paragraphs-categorization huggingface dataset:
The genomic DNA of the sediment samples was extracted using a FastDNA Spin Kit for Soil (MP Biomedicals) according to the manufacturer's instructions. The obtained genomic DNA was purified for library construction and sequenced on an Illumina HiSeq2500 platform (2× 150 bp). The raw reads were filtered to remove Illumina adapters, PhiX and other Illumina trace contaminants using BBTools (v. 38. 79), and low-quality bases and reads were removed using Sickle (v. 1. 33; https://github. com/najoshi/sickle). The filtered reads were assembled using metaSPAdes (v. 3. 10. 1) with a kmer set of "21, 33, 55, 77, 99, 127". The filtered reads were mapped to the corresponding assembled scaffolds using bowtie2 (v. 2. 3. 5. 1)  56. The coverage of a given scaffold was calculated using the command of jgi_summa-rize_bam_contig_depths in MetaBAT (v.
Trimmomatic (v. 0. 36)  61  was used to trim low-quality regions and adapter sequences from raw reads (parameters: ILLUMINACLIP:TruSeq3-PE-2. fa:2:30:10, LEADING:20, SLIDINGWINDOW:4:20, MINLEN:50). Clean paired reads were then interleaved using the khmer software package  62. Interleaved and unpaired reads were assembled using MEGAHIT (v. 1. 1. 1-2-g02102e1) (--k-min 31, --k-max 151, --k-step 20, --min-contig-len 1000)  63, 64. Trimmed reads were mapped back to the contigs to determine read coverage using Bowtie 2 (v. 2. 2. 9)  56, 65  and SAMtools (v. 1. 3. 1)  66. Binning was performed using MetaBAT (v. 0. 32. 4)  57  and tetranucleotide frequency and read coverage. Bin completion and contamination were estimated using CheckM (v. 1. 0. 7)  67. Aarhus Bay. For Lokiarchaeia ABR01, ABR02, ABR03, ABR04, ABR05, ABR06, ABR08, ABR11, ABR13 and ABR15, Thorarchaeia ABR09 and ABR10 and Heimdallarchaeia ABR14 and ABR16, MAGs were obtained as previously described  29.
White Oak River. For Sifarchaeia WORA1, Hermodarchaeia WORB2, Heimdallarchaeia WORE3, Lokiarchaeia WORB4 and WORC5, and Thorarchaeia WORH6, sampling, DNA extraction, sequencing library preparation and sequencing methods were performed as previously described  68. Published assemblies and raw reads for the samples WOR-1-36_30 (National Center for Biotechnology Information (NCBI) BioSample identifier SAMN06268458; Joint Genome Institute ( JGI) identifier Gp0056175), WOR-1-52-54 (SAMN06268416; Gp0059784), WOR-3-24_28 (SAMN06268417; Gp0059785) were downloaded from the JGI. Short reads were trimmed using Trimmomatic (v. 0. 33)  61  (PE ILLUMINACLIP:2:30:10 SLIDINGWINDOW:4:15 MILEN:100). Contigs shorter than 1,000 bp were excluded from the assembly using SeqTK (v. 1. 0r75) (https://github. com/lh3/seqtk).
Each assembly was binned using CONCOCT (v. 0. 4. 1)  69  and coverage information from the three datasets, and Asgard bins were subsequently identified based on phylogenies of concatenated ribosomal proteins  3. Identified Asgard MAGs were used together with publicly available Asgard genomes to recruit trimmed reads originated from Asgard genomes using CLARK (v. 1. 2. 3) with the -m 0 option  70. For each dataset, recruited Asgard reads were independently assembled using SPAdes  71  and IDBA-UD  72  and further binned using CONCOCT, using a minimum contig length of 1,000 bp. Bins with higher completeness and lower contamination values as predicted by miComplete (v. 1. 00)  73 were selected and manually curated using mmgenome (v. 0. 7. 1)  74, 75  using the coverage information, paired-reads linkage, composition and marker genes information.
The samples and assembly method used for each final MAG were as follows: Sifarchaeia WORA1 (WOR-1-52-54; spades); Hermodarchaeia WORB2 (WOR-1-52-54; IDBA-UD); Heimdallarchaeia WORE3 (WOR-3-24_28; spades); Lokiarchaeia WORB4 and WORC5 (WOR-1-36_30; IDBA-UD); and Thorarchaeia WORH6 (WOR-1-36_30; spades). Radiata Pool hot springs. For Jordarchaeia RPD1 and RPF2, and Odinarchaeia RPA3, information about the location of the hot spring sediments from Radiata Pool, sampling and DNA extraction procedures has been previously reported  3. Short paired-end Illumina reads were generated and preprocessed using Scythe (https://github. com/vsbuffalo/scythe) and Sickle (https://github. com/najoshi/sickle) to remove adapters and low-quality reads. Reads were subsequently assembled with IDBA-UD 1. 1. 3 (--maxk 124).
The Jordarchaeia RPF2 MAG was generated by binning contigs according to their tetranucleotide frequencies using esomWrapper. pl (https://github. com/tetramerFreqs/Binning) with a minimum contig length of 5,000 bp and a window size of 10 kbp. ESOM maps were manually delineated using the Databionic ESOM viewer (http://databionic-esom. sourceforge. net/). Jordarchaeia RPD1 and Odinarchaeia RPA3 were binned following the previously described  29  methodology, but re-assembling the recruited reads only with IDBA-UD (--maxk 124)  72. Guaymas Basin. For Asgardarchaeia GBS01, Baldrarchaeia GBS02, GBS03, and GBS04, Jordarchaeia GBS05, GBS06 and GBS07, Heimdallarchaeia GBS08, GBS09, GBS10, GBS11, GBS15, GBS16, GBS17, GBS18, GBS19, GBS20, GBS21, GBS22, GBS23, GBS24, GBS25, GBS26 and TNS08, Lokiarchaeia GBS14, and Thorarchaeia GBS28, GBS29, GBS33 and GBS34, MAGs were obtained as previously described  76.
A total of 10 g of each of the two sediment samples was used to extract DNA using a MoBio DNA PowerSoil Total kit. A total of 100 ng DNA was used to prepare sequencing libraries that were 150 bp paired-end sequenced at the Marine Biological Laboratory (Woods Hole, MA, USA) on an Illumina MiSeq sequencer. Adaptors and DNA spike-ins were removed from the forward and reverse reads using cutadapt (v. 1. 12)  81. Afterwards, reads were interleaved using interleave_fasta. py (https://github. com/jorvis/biocode/blob/ master/fasta/interleave_fasta. py) and further trimmed using Sickle with default settings (Fass JN) (https://github. com/najoshi/sickle). Metagenomic reads from both samples were co-assembled using IDBA-UD with the following parameters: --pre_correction, --mink 75, --maxk 105, --step 10, --seed_kmer 55 (ref.
Exploration of phylogenetic diversity in Asgard archaeal assemblies and MAGs To assess the presence of potential Asgard-related lineages in our assemblies, we reconstructed a phylogeny of ribosomal proteins encoded in a conserved RP15 gene cluster  82. As the in-group, we used all MAGs presented in this study, plus all genomes classified as Asgard archaea in the NCBI database as of 25 June 2021, plus those classified as 'archaeon' corresponding to Hermodarchaeia (GCA_016550385. 1, GCA_016550395. 1, GCA_016550405. 1, GCA_016550415. 1, GCA_016550425. 1, GCA_016550485. 1, GCA_016550495. 1 and GCA_016550505. 1), and all Asgard archaeal MAGs released in previous study  19. To obtain an adequate outgroup dataset, we downloaded all archaeal genomes from the Genome Taxonomy Database 83 , release 89, and selected one genome sequence per species-level cluster as previously defined (https://data.
Functional annotation of homologous protein families Protein families, excluding singletons, were aligned using mafft-linsi (v. 7. 402)  93  and converted into HHsearch format (. hhm) profiles using HHblits (v. 3. 0. 3)  94. Profile-profile searches were subsequently performed against a database containing profiles from EggNOG (v. 4. 5)  95  , arCOGs  96  and Pfam databases  97  that had been previously converted to the hhm format using HHblits (v. 3. 0. 3)  94.
Phylogenetic analyses of concatenated proteins for species tree inference Two sets of phylogenetic markers were used to infer the species tree. The first one (RP56) is based on a previously published dataset of 56 ribosomal proteins used to place the first assembled Asgard genomes  3. The second one (NM57, for new markers) corresponds to 57 proteins extracted from a set of 200 markers previously identified as core archaeal proteins that can be used to confidently infer the tree of archaea  100. These 57 markers were selected because they were found in at least one-third of representatives of each of the 11 Asgard clades, as well as in 10 out of 14 eukaryotes, and were inherited from archaea in eukaryotes. We initially assembled a RP56 dataset for a phylogenetically diverse set of 222 archaeal and 14 eukaryotic taxa.
These included all 11 Asgard archaea MAGs and genomes available at the NCBI as of 12 May 2017, as well as the 53 most diverse new MAGs from this work (out of 63). We gathered orthologues of these genes from all proteomes by using sequences from the previously published alignment  3, 100  as queries for BLASTp. For each marker, the best BLAST hit from each proteome was added to the dataset. For the first iteration, each dataset was aligned using mafft-linsi  101  and ambiguously aligned positions were trimmed using BMGE (-m BLOSUM30)  102. All 56 trimmed ribosomal protein alignments were concatenated into a RP56-A64 supermatrix (236 taxa including 64 Asgard archaea, 6,332 amino acid positions). Once this taxon set was gathered, we identified homologues of the NM57 gene set as described above, thus generating supermatrix NM57-A64 (236 taxa, 14,847 amino acid positions).
We carried out a large number of phylogenomic analyses on variations of these two RP56-A64 and NM57-A64 datasets with different phylogenetic algorithms. Notably, preparing these datasets must be done with great care and is therefore time-consuming, and subsequent phylogenomic analyses generally require an enormous amount of computational running time. However, the rapid expansion of available Asgard archaeal MAGs, notably in a previous publication  4  , urged us to update and re-run many of the computationally demanding analyses. As some of the work that was based on a more restrained taxon sampling is still deemed valuable, such as some of the Bayesian phylogenomic analyses and ancestral genome content reconstructions, we retained these in the current study. An updated Asgard archaeal genomic sequence dataset was constructed by including all 230 Asgard archaeal MAGs and genomes available at the NCBI database as of 12 May 2021, as well as 63 new MAGs described in the current work.
All 56 trimmed ribosomal protein alignments were concatenated into an RP56-A293 supermatrix (465 taxa including 293 Asgard archaea, 7,112 amino acid positions), which was used to infer a preliminary phylogeny using FastTree (v. 2)  103  (Supplementary Fig. 16 ). Given the high computational demands of the subsequent analyses, we then used this phylogeny to select a subsample of Asgard archaea representatives. For this, we first removed the most incomplete MAGs encoding fewer than 19 ribosomal proteins (that is, one-third of the markers) in the matrix. We also used the preliminary phylogeny to subselect among closely related taxa: among taxa that were separated by branch lengths of <0. 1, we only kept one representative. This led to a selection of 331 genomes, including 175 Asgard archaea, 41 DPANN, 43 Euryarchaea and 72 TACK representatives (RP56-A175 dataset).
Out of these 175 Asgard archaea, 41 correspond to MAGs newly reported here. Once this taxon set was gathered, we identified homologues of the NM57 gene set as described above, thus generating supermatrix NM57-A175 (15,733 amino acid positions). All datasets and their composition are summarized in Supplementary Table  2. To test for potential phylogenetic reconstruction artefacts, our datasets were subjected to several treatments. Supermatrices were recoded into four categories using the SR4 scheme  25. The corresponding phylogenies were reconstructed using IQ-TREE (using a user-defined previously described model referred to as C60SR4 based on the implemented C60 model and modified to analyse the recoded data 3 ) and Phylobayes (under the CAT+GTR model). We also used the estimated site rate output generated by IQ-TREE (-wsr) to classify sites into 10 categories, from the fastest to the slowest evolving, and we removed them in a stepwise fashion, removing from 10% to 90% of the data.
Analyses of individual proteins For individual proteins of interest, we gathered homologues using various approaches depending on the level of conservation across taxa. To detect putative Asgard homologues of eukaryotic proteins, we used a combination of tools, including BLASTp 104 and the HMMer toolkit (http://hmmer. org/) if HMM profiles were available, and queried a local database containing our 240 archaeal representatives (including all Asgard predicted proteomes). We then investigated the Asgard candidates as following: (1) using them as seed for BLASTp searches against the nr database; (2) 3D modelling using Phyre2 and SwissModel when sequence similarity was low; (3) annotating them using Interproscan (v. 5. 25-64. 0)  105 , EggNOG mapper (v. 0. 12. 7)  106  , against the NOG database 106 , and GhostKoala annotation server 107 ; (4) annotating the archaeal orthologous cluster they belonged to using profile-profile annotation as described above.
Eukaryotic homologues were gathered from the UniRef50 database  108. Depending on the divergence between homologues, they were aligned using mafft-linsi and trimmed using TrimAl 109 (--automated1) or BMGE  102  , or, in cases where we investigated a specific functional domain, we used the hmmalign tool from the HMMer package with the --trim flag to only keep and align the region corresponding to this domain. When divergence levels allowed, phylogenetic analyses were performed using IQ-TREE with model testing including the C-series mixture models (-mset option)  110. Statistical support was evaluated using 1,000 ultrafast bootstrap replicates (for IQ-TREE)  109.
The genome copy number was corrected to account for the extinction probability per cluster (https://github. com/maxemil/ALE/ commit/136b78e). The missing fraction of the genome was calculated as 1 minus the completeness values (in fraction) as estimated by CheckM (v. 1. 0. 5) for each of the 181 taxa  67. Protein families containing only one protein (singletons) were considered as originations at the corresponding leaf. The ancestral reconstruction of 5 protein families that included more than 2,000 proteins raised errors and could not be computed. The minimum threshold of the raw reconciliation frequencies for an event to be considered was set to 0. 3 as commonly done  [114] [115] [116] [117]  and recommended by the authors of ALE (G. Szölősi, personal communication).
Ancestral metabolic inferences Metabolic reconstruction of the Asgard ancestors was based on the inference, annotation and copy number of genes in ancestral nodes. The presence of a given gene was scored if its copy number in the ancestral nodes was above 0. 3. A protein family was scored as 'maybe present' if the inferred copy number was between 0. 1 and 0. 3. The protein annotation of each of the clusters containing the ancestral nodes was manually verified for each of the enzymatic steps involved in the pathways, as detailed in Supplementary Table  4. Extended Data Fig. 3  | Identification of previously undetected vesicular trafficking ESPs in Asgard archaea. Schematic representation of a eukaryotic cell in which ESPs involved in membrane trafficking and endosomal sorting that have been identified in Asgard archaea are highlighted.
Distribution of ESP homologues in Asgard archaea grouped by function. Shaded rectangles above the protein names indicate ESPs newly identified as part of this study. Predicted homologues are depicted by coloured circles: fully filled circles indicate that we detected homologues in at least half of the representative genomes of the clade; half-filled circles indicate that we detected homologues in fewer than half of the representative genomes of the clade. Hodarchaeales ESP homologues are highlighted against a grey background. Accession numbers are available in Supplementary Table 3.
Acknowledgements We thank  S. Köstlbacher ,  L. Hederstedt ,  A. Spang  and  A. J. Roger  for discussions; staff at the  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) at Uppsala University  and the  Swedish National Infrastructure for Computing (SNIC)  at the  PDC Center for High-Performance Computing  for providing computational resources; staff at the  Japan Agency for Marine-Earth Science and Technology (JAMSTEC)  for taking sediment samples from the Taketomi shallow submarine hydrothermal system; and the crew of the RV  Roger Revelle  for assisting with the sampling of the ABE and Mariner vent fields along the Eastern Lau Spreading Center during the RR1507 Expedition. The  Ngāti Tahu-Ngāti Whaoa Runanga Trust  is acknowledged as mana whenua of  Radiata Pool  and associated samples, and we thank them for their assistance in access and sampling of the Ngatamariki geothermal features.
C. 192. 016 ), Marie Skłodowska-Curie ITN  project  SINGEK  ( H2020-MSCA-ITN-2015-675752 ) and the  Wellcome Trust foundation  ( Collaborative award   203276/K/16/Z ) awarded to T. J. G. E. L. E. was supported by a  Marie Skłodowska-Curie IEF  (grant  704263 ) and by funding from the  European Research Council  ( ERC Starting  grant  803151 ). T. N. was supported by  JSPS   KAKENHI JP19H05684  within  JP19H05679. W. -J. L. was supported by the  National Natural Science Foundation of China  (grant number  91951205  and  92251302 ). D. T. was supported by the  Swedish Research Council  ( International Postdoc  grant  2018-06609 ). C. W. S. was supported by a  Science for Life Laboratory postdoctoral fellowship  (awarded to T. J. G. E. ) and funding from the  Swedish research council (Vetenskaprådet Starting  grant  2020-05071  to C.
Data availability The MAGs reported in this study have been deposited at the DNA Data Bank of Japan, the European Molecular Biology Laboratory and GenBank. BioProject identifiers, BioSample identifiers and GenBank assembly accession numbers are provided in Supplementary Table  1. All raw data underlying phylogenomic analyses (raw and processed alignments and corresponding phylogenetic trees), and all predicted proteomes have been deposited into Figshare (https://doi. org/10. 6084/ m9. figshare. 22678789).
Eukaryotic signature proteins in Asgard archaea. Distribution of ESP homologues in Asgard archaea grouped by function. Shaded rectangles above the protein names indicate ESPs newly identified as part of this study. Predicted homologues are depicted by coloured circles: fully filled circles indicate that we detected homologues in at least half of the representative genomes of the clade; half-filled circles indicate that we detected homologues in fewer than half of the representative genomes of the clade. Hodarchaeales ESP homologues are highlighted against a grey background. Accession numbers are available in Supplementary Table 3. DNA pol epsilon Topoisomerase IB RNA pol A (fused) RNA pol RPB8/RpbG Ribosimal protein L22e Ribosomal protein L28e E2F/DP transcription factor N-terminal histone tail PARP domain Eukaryotic-type EF2 Dph1 Dph5 Dph6 PAC4 TRAP alpha TRAP beta TRAP delta TRAP gamma OST5 Ribophorin I OST4 OST3/OST6-like OST2 WBP1/DDOST SWPI Lokiactin ARP 2/3 complex subunit 4 Pro lin Gelsolin Tubulin Longin/Mon1 protein Roadblock/LC7 arTRAPP Sec23/24 Arrestin AP: Adaptin N AP: Alpha adaptinC2 AP: Adap comp sub AP: Clat adaptor s AP: B2-adapt-app C SNARE HOOK domain Yip1 ESCRT-I:Vps28 ESCRT-I:Steadinessbox ESCRT-I:UEV ESCRT-II:Vps25 ESCRT-II:NZF ESCRT-II:GLUE ESCRT-II:Vps22/36 ESCRT-III:Vps2/24/46andVps20/32/60 Vps4 Vfa1 Vta1 Ist1 Bro1 Retromer:Vps5 Retromer:Vps26 Retromer:Vps29 Retromer:Vps35 Retromer cargo:Vps10-Sortilin Vps62 CORVET/HOPS: Vps11 CORVET/HOPS: Vps16 CORVET/HOPS: Vps18 HOPS:Vps39 Vps55 Vps68 GARP:Vps51 GARP:Vps52 GARP:Vps53 GARP:Vps54 Ub-domain protein Ub-activating protein E1 Ub-conjugating protein E2 Ub-ligase E3 candidates Eukarya Lokiarchaeales Helarchaeales Thorarchaeia Hermodarchaeia Heimdallarchaeaceae Kariarchaeaceae Gerdarchaeales Njordarchaeales Hodarchaeales Wukongarchaeia Sifarchaeia Baldrarchaeia Odinarchaeia Jordarchaeia Asgardarchaeia Information processing N-glycosylation Cytoskeleton Traf cking machinery Endosomal sorting Ubiquitin system Fig.
Metabarcoding data processing and statistical analysis The sequence quality was checked using FastQC  [27]. Raw sequence processing and swarm clustering were performed using the FROGS pipeline (v3. 2. 3)  [28, 29]. The unmerged reads were retained during this process. OTUs with an overall read number of < 200 were discarded, as were those present in fewer than 16 samples (Supplementary Table  S1 ). The rDNA ITS region was identified using ITSx and used for OTU affiliation using the UNITE database (v8. 2)  [30]. Fungal ecological guilds were assigned to fungal amplicons using the FungalTrait database  [31]. The guild affiliation of the most abundant OTUs has been manually curated owing to the potential mis-classification of trophic lifestyle annotated in the FungalTrait database. Normalization based on the lowest number of reads for all samples was performed using random sampling.
To characterize how microbial populations differed across forest associations and soil depths, we used R vegan (v2. 5-7) and phyloseq (v1. 28. 0) packages  [32, 33]. The mean species diversity of each sample (alpha diversity) was calculated based on species abundance and observed richness. The mean species diversity between samples (beta diversity) was estimated using the Bray-Curtis method. Data structures were visualized using non-metric multidimensional scaling (NMDS), and variations were quantified using permutational multivariate analysis of variance (PERMANOVA, 9999 permutations). Variations in alpha-diversity according to the soil physicochemical parameters were quantified using the linear model implemented in the R vegan algorithm for constrained ordination  [33]. To identify taxonomic and functional differences among tree species and among seasons, Kruskal-Wallis with FDR correction and post-hoc Wilcoxon tests were performed.
For each of the 72 samples, filtered reads were mapped onto the corresponding de novo metatranscriptome assemblies using Bowtie2 (v2. 3. 0)  [35]. Counts were determined using SAMtools (v1. 7)  [36]. Contigs supported by less than 10 mapped reads or present in less than four different samples were discarded. These contigs were annotated using Diamond (v2. 0. 15)  [37]  and the following parameters (more_sensitive -max-target-seq 1 -max-hsps 1evalue 1e -10 ) and two different databases: GenBank non-redundant (April 2021 release) and the Joint Genome Institute MycoCosm (January 2023 release)  [38]. Only published genomes were used. Conflicting taxonomic annotations were solved based on a higher Diamond bit score, with an advantage given to Myco-Cosm in the case of tied scores. Up to 38% of the reads were mapped to the reference fungal genomes.
Contigs that were not annotated as fungi were discarded. Read counts were reported as transcripts per million (TPM). Functional annotations were based on (1) eukaryotic orthologous groups (KOG) annotations available for each genome in the MycoCosm database  [38] , (2) the CAZy database (April 2023 release)  [39] , and (3) the MEROPS peptidase database (January 2023 release)  [40]. CAZyme annotation was performed on the longest coding sequence (CDS) predicted for each contig. CDS prediction was performed using TransDecoder (v. 5. 5. 0) (incl. CDS > 100 amino acids) (https:// github. com/ Trans Decod er/). Plant cell wall-degrading enzymes (PCWDE), fungal cell wall-degrading enzymes (FCWDE), and other microbial cell wall-degrading enzymes (neither PCW nor FCW) were annotated according to Miyauchi et al.
[21]  and Lebreton et al. [41]. Several fungal species found in Xulongxueshan forests are endemic to southwestern China and lack a reference genome. Thus, the RNA reads from their extracted soil mycelium mapped to the available reference genome for closely related species. For example, RNA reads from the ectomycorrhizal Amanita flavopantherina, very abundant in Yunnan forest soils, mapped to the reference genomes deposited at JGI MycoCosm of the phylogenetically-related A. muscaria from North America, but we considered these RNA reads belong to A. flavopantherina. A detailed description of scripts, including parameters and versions used in the workflow, is available at https:// github. com/ anleb reton/ Scrip ts_ from_ Zeng_ Lebre ton_ et_ al. _ 2023. git. Ordination plots were generated using the vegan R package v2.