dataesr/scientific-paragraphs-categorization huggingface dataset:
com/C-Connor/ EnterobaseGenomeAssemblyDownload. Genomes less than 4. 5 Mbp or greater than 6. 5 Mbp were excluded. ABRicate 0. 9. 8 was used to screen for ColV related genes and the Liu criteria was applied to infer ColV carriage as described above. Metadata, accession numbers and additional information on the cohort of strains under analysis are available in Supplementary Data 4. Statistics. All statistical analysis were performed in R 4. 0. 5 and scripts are available as described in 'Code availability' below. The Kruskal-Wallis test was used to determine whether the mean of total ARGs or VAGs per sequence was different based on BAP group membership and between ColV+/-status. Pairwise Wilcoxon test with Benjamini-Hochberg p-value correction for multiple testing was then used to calculate the significance of the pairwise differences between BAP groups.
Two-sided Wilcoxon rank-sum test was used to calculate the significance of the difference between ColV+ and ColV-sequences. Data processing and visualisation. All processing, analyses and data visualisation were performed in RStudio 1. 4. 1106 with R 4. 0. 5 and can be reproduced with the scripts linked in 'Code availability'. Some manual editing of figures for consistency of presentation was performed in Microsoft PowerPoint. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability All ST58 raw sequence read data generated for the first time in this study have been deposited in NCBI BioProject PRJNA727368 with individual accession numbers available in Supplementary Data 1. All other ST58 raw sequence read data used in this study are available via either the NCBI Sequence Read Archive [https://www. ncbi. nlm. nih. gov/sra] or the EMBL-EBI European Nucleotide Archive [https://www. ebi. ac. uk/ena/browser/ home] via accession numbers listed in Supplementary Data 1. Genome assemblies from the Enterobase collection are available at https://enterobase. warwick. ac. uk/species/index/ ecoli via assembly barcodes listed in Supplementary Data 4. Source data are provided with this paper.
Acknowledgements We would like to thank:  Frank Hansen ,  Karin Sixhøj Pedersen  and  Hülya Kaya  for their excellent technical assistance;  Jonas Bonnedahl ,  Linköping University ;  Sara Byfors ,  Public Health Agency of Sweden ;  Catarina Flink  and  Mia Egervärn ,  Swedish Food Agency. Ivan Literak ,  Jaroslav Hrabak ,  Iva Kutilova  and  Ivana Jamborova  for providing strains and help in the laboratory. Kay Anantanawat for genome sequencing support at iThree Institute. Ian Charles  and  Garry Myers  for their helpful comments on the manuscript. Fiona MacIver  for editing. This study was partially funded by  Czech Science Foundation  Grant no. 18-23532 S awarded to M. D. It was also supported by an  Australian Government Medical Research Future Fund project  ( MRFF75873 ), the  Australian Centre for Genomic Epidemiological Microbiology (AusGEM) , a strategic research initiative between the  New South Wales Department of Primary Industries  and the  University of Technology Sydney and Australian Research Council Linkage  Project  LP150100912.
g. , in IPA and LE regions) in several cell fractions, namely whole cells (total), nucleus, and cytosol (Fig. 5C ). For this, MCF-7 cells were treated with Doxo or vehicle for 6 h, and ELAVL1 iCLIP experiments were carried out on two biological replicates for every condition (Doxo and vehicle) and cell fraction (Supplemental Fig. S5A-C ) (it should be noted that the iCLIP data in vehicle-treated total cells, which were presented above in Fig. 4 , are part of this large iCLIP data set). As this CCS-iCLIP data set is quite extensive and unique, we first made a broad analysis of ELAVL1-RNA interactions across compartments and conditions before focusing on the link between ELAVL1 binding and IPA:LE isoform regulation. In the absence of Doxo, there were about seven times more ELAVL1 binding sites in the nucleus than in the cytosol (Fig.
Regarding adenosine deaminase RNA specific B1 (ADARB1), it is an RNA editing enzyme related to ADAR1. ADAR1 was reported to edit the mRNA encoding the DNA repair protein NEIL1, thereby modifying its protein sequence and its repair activity  (Yeo et al. 2010) , and two recent studies in cancer cells showed that ADAR1 impacts the mRNA stability of several DDR genes, the activation of the DDR, and genome stability  (Sagredo et al. 2020; Shiromoto et al. 2021). Thus, our data on ADARB1 extend the emerging link between RNA-editing enzymes and DNA damage. In conclusion, although many studies have shown widespread regulation of IPA isoforms in various biological processes, little was known about their regulation beyond nuclear metabolism. In this study, our genome-wide analyses on subcellular compartments and fractions reveal that Doxo differentially regulates IPA isoforms in the transcriptome and translatome, and in the nucleus and cytosol.
Sequencing was carried out on the NovaSeq 6000 instrument from Illumina based on a 2×100 cycle mode (paired-end reads, 100 bases) using an S1 flow cell in order to obtain around 100 million clusters (200 million raw paired-end reads) per sample. FASTQ files were generated from raw sequencing data using bcl2fastq where demultiplexing was performed according to barcodes. 3 ′ -seq experiments 3 ′ -seq libraries were prepared with a QuantSeq 3 ′ mRNA-Seq Library Prep kit REV for Illumina (Lexogen) using 250 ng (for polysomal RNA) or 500 ng (for all other samples) of DNase I treated RNA (n = 3 for each condition) following the manufacturer's instructions. Purified libraries were quantified with a Quant-iT Picogreen dsDNA kit (Thermo Fisher Scientific) and run on an Experion automated electrophoresis system (Bio-Rad).
4. 5) (Love et al. 2014)  and the following statistical model: Y ij = μ + L i + C j + (LC) ij + E ij , where Y ij is the normalized counts of peak i in biological condition j, μ is the mean, L i is the peak localization (IPA or LE), C j is the biological condition, (LC) ij is the interaction between peak localization and biological condition, and E ij is the residual. P-values and adjusted P-values (Benjamini-Hochberg) were calculated. Data with P < 0. 05 are shown. The complete bioinformatics pipeline (3 ′ -SMART package) described above can be freely downloaded at GitHub (https://github. com/InstitutCurie/3-SMART) and can be run through a configuration file and a simple command line. Annotated polyadenylation sites were retrieved from the PolyA_DB 3 and PolyASite 2. 0 databases  (Wang et al.
4. 5) (Love et al. 2014 ) and two independent biological replicates per group. Of note, even if a peak is detected in only one condition, reads were counted in this genomic window in all four samples. Adjusted P-values (Benjamini-Hochberg) were calculated and a cutoff of 0. 05 was used. The complete bioinformatics pipeline described above can be freely downloaded at GitHub (https://github. com/bioinfo-pfcurie/clip-seq) and can be run through a configuration file and a simple command line. Motif analysis was done using MEME-ChIP (version 4. 12. 0, classic mode, RNA motifs, width: 6-15, number of motifs: 10, number of sites: 2-600)  (Machanick and Bailey 2011).
Other bioinformatic and statistical analyses Intersections involving peak lists were done using BEDTools Intersect (version 2. 17. 0)  (Quinlan and Hall 2010) , allowing a maximum distance of 150 bp when crossing two lists of 3 ′ -seq peaks. 3 ′ UTR-intron regions were defined as regions that overlap with both a 3 ′ UTR and an intron from RefSeq transcripts; 3 ′ UTR-LE regions were defined as regions that overlap with both a RefSeq 3 ′ UTR and the last exon of a gene. The 3 ′ -seq, RNA-seq, and iCLIP data sets were visualized in the UCSC Genome Browser and Integrative Genomics Viewer  (Kent et al. 2002; Robinson et al. 2011). Functional gene annotation analyses were done using the DAVID software  (Huang et al. 2009a,b)  using the human genome as a reference. For each experimental analysis, at least three independent experiments were performed.
Acknowledgments This work was supported by grants from the  Fondation pour la Recherche Médicale  ( DBI20141231314 ),  Institut National du Cancer  ( 2015-141 ), and  Institut Curie (ICGex)  to M. D. , a grant from  Ligue Nationale Contre le Cancer  (équipe labellisée) to S. V. , and a  PhD fellowship from Ministère de l'Enseignement Supérieur et de la Recherche  to A. D. We thank the  ICGex NGS platform of Institut Curie  that prepared total-RNA-seq libraries and managed all sequencing runs, and is supported by grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  Canceropôle Ile-de-France , and by the  SiRIC-Curie program-SiRIC grant INCa-DGOS-4654. Author contributions: Experiments were performed by A.
Data access The data sets generated in this study have been deposited in the UCSC Genome Browser (https://genome. ucsc. edu/s/clabbe/ visuDoxoPaper_Dutertre2021). All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus repository (GEO; https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE180850. The bioinformatics pipelines for 3 ′ -seq analysis of IPAs (3 ′ -SMART package) and iCLIP analysis are available at GitHub (https://github. com/InstitutCurie/3-SMART and https://github. com/bioinfo-pfcurie/clip-seq, respectively) and as Supplemental Code. Other bioinformatics scripts used in this study are provided in the Supplemental Material (Supplemental Code).
Data Collection Initially, the questionnaire has been designed and conducted to explore how and why RG visitors use RGMA. The questionnaire has been constructed as an exploratory in order to understand the functions and employability of RGMA in the framework of a connected stadium. No pilot has been conducted. Six accredited interviewers have collected the answers for the questionnaires inside the stadium throughout the tournament period. The respondents were selected at random (simple random sampling,  Taherdoost, 2016) , based on a commitment to take part in this study. All of them are agree that their answers are collected and used in an academic study and published, respecting confidentiality and privacy. Thus, we use the sample of 2018 RG spectators (N = 1,000). The event was attended by 480,500 spectators.
Data Analysis The questionnaire was analyzed with Modalisa R software 1. The statistical analysis relies on three types: flat sorting, cross sorting, and factorial correspondence analysis for profile construction based on more than two variables. More precisely, the typology was constructed based on three variables: geographical origin, age category, and involvement in tennis. Correlations in cross-sorting were established using PEM (french abbreviation: pourcentage de l'écart maximum)-percentage from maximum deviation: an index of a tie between modalities of a contingency table  (Cibois, 2009).
Genome reconstruction and collection We collected 2618 genomes spanning all the recognized phyla and superphyla of the Archaea domain from the NCBI genome database (Additional file 1: Table  S1 ). To enable our analyses, we augmented the relatively limited sampling of the DPANN by adding 569 newly available DPANN metagenome-assembled genomes (MAGs) from low oxygen marine ecosystems, an aquifer adjacent to the Colorado River, Rifle, Colorado, and from groundwater collected at the Genasci dairy farm, Modesto, California  [22, 23]. The 3197 genomes were clustered at ≥ 95% average nucleotide identity (ANI) to generate 1749 clusters. We removed genomes with <70% completeness or >10% contamination or if there was < 50% of the expected columns in the alignment of 14 concatenated ribosomal proteins (see the "Methods" section).
Genomic content of representative genomes correlates with the phylogeny of archaea We clustered the 2,336,157 protein sequences from the representative genomes in a two-step procedure to generate groups of homologous proteins (Additional file 2: Fig. S2 ). This resulted in 10,866 clusters (representing 2,075,863 sequences) that were present in at least five distinct genomes. These clusters are henceforth referred to as protein families. We assessed the quality of the protein clustering. The rationale was that we expected protein sequences with the same function to cluster into the same protein family. We annotated our protein dataset using the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations  [24]  and systematically verified that the protein family groupings approximate functional annotations.
The KEGG annotations in our dataset encompass 6482 unique annotations of various biological processes, including fast-evolving defense mechanisms. For each of these 6482 annotations, we reported the family that contains the highest percentage of protein members annotated with that KEGG annotation. Most clusters were of good quality. For 87% of the KEGG annotations (5627 out of 6482), one family always contained >80% of the proteins (Additional file 2: Fig. S3A ). The contamination of each protein family was assessed by computing the percentage of the proteins with KEGG annotations that differ from the dominant annotation (percentage annotation admixture). Most of the families contain only proteins with the same annotation, and 2654 out of 3746 families (71%) have <20% annotation admixture (Additional file 2: Fig.
S3B ). Although this metric is useful, we note that it is imperfect because two homologous proteins can have different KEGG annotations and thus cluster into the same protein family, increasing the apparent percentage of annotation admixture. Although we used sensitive Hidden Markov Model-based (HMM-based) sequence-comparison methods and assessed the quality of the protein clustering, we cannot completely rule out the possibility that our pipeline failed to retrieve distant homology for highly divergent proteins. Small proteins and fast-evolving proteins are more likely to be affected. This lack of sensitivity would result in the separation of homologous proteins into distinct families and would impact the results. To reduce the incidence of proteins without functional predictions for which annotations should have been achieved we augmented PFAM and KEGG-based annotations by comparing sequences to the Protein Data Bank (PDB) database  [25]  and by performing HMM-HMM comparison against the eggNog database  [26]  (see the "Methods" section).
Functions specific to Poseidoniales Modules 32 and 71, encompassing 199 families, were consistently associated with genomes of Poseidoniales, formerly Marine Group II (MGII)  [42]  (Additional file 1: Table  S3 ), which are implicated in protein and saccharide degradation  [43]  (for details, see Additional file 3). These modules contain protein degrading enzymes (several different classes of peptidases and one oligotransporter) previously found in Poseidoniales  [43]  and two new Poseidoniales-specific families of well-conserved peptidases. As reported by Tully  [43] , peptidase S15 (PF02129; fam03321) and peptidase M60-like (PF13402; fam05454) have a narrow distribution within Poseidoniales, and were not assigned to ones of the 96 modules. Interestingly, we identified modules specific to Poseidoniales subgroup Candidatus Poseidonaceae (formerly subgroup MGIIa) (module 135, containing 99 families) and Poseidoniales subgroup Candidatus Thalassarchaeaceae (formerly subgroup MGIIb) (module 45, containing 39 families) with calcium-binding domains (Additional file 2: Fig.
We were interested to find that this ubiquitous function is performed by specific protein families in Crenarchaeota, possibly reflecting adaptation to their high-temperature habitats. One of these, PolB1-binding protein 2 (PBP2) (fam03141, PDB accession 5n35)  [46] , is a subunit of DNA polymerases B1 (PolB1) that are responsible for initial RNA primer extension with DNA, lagging and leading strand synthesis. The second is a single-stranded DNA-binding protein (DBP) ThermoDBP, which we also found to be conserved in Crenarchaeota and in Thermococci (fam03176, PDB accession 4psl)  [47, 48]. Interestingly, however, the third is a Fe-S independent primase subunit PriX (fam03870, PDB accessions: 4wyh and 5of3) specific to Sulfolobales (Additional file 2: Fig. S10 ). PriX is essential for the growth of Table  1  A list of the fourteen modules that are lineage specific but also well conserved within eleven major archaeal lineages.
A family was counted as having a signal peptide if at least 25% of its protein sequences were predicted to have a signal peptide prediction according to the SignalP software  [32]. A family was counted as having a transmembrane helix if more than half of its protein sequences were predicted to have a transmembrane helix according to the TMHMM software  [33]. Families were considered hypothetical if they have neither PFAM (Domain of Unknown Function domains were excluded) nor KEGG annotations (see the supplementary dataset -Table  S3  for the full list of hypothetical families). Finally, a family was considered to have bacterial homologs if the family matched with protein sequences of at least ten distinct bacterial genomes (see the "Methods" section). The core module 1 is included as a comparison Sulfolobus cells  [49, 50].
Hypothetical proteins distinguish the major archaeal groups Even after augmenting functional predictions using PDB and EggNog databases, families with functional predictions represent a tiny proportion of the protein families that comprise the lineage-specific modules (Table  1  and Additional file 1: Table  S3 ). In total, 1053 out of 1411 hypothetical families remain unannotated (Additional file 1: Table  S4 ). A total of 358 hypothetical families have small domain matches but not enough information is available to predict functions. For example, many have domains with matches to zinc finger domains, but such domains occur in proteins with diverse functions (Additional file 1: Table  S4 ). We found that the hypothetical proteins are shorter than proteins from the core families of module 1 (Additional file 2: Fig.
S15 ) and are more   S8  likely to have a transmembrane helix prediction and a signal peptide prediction (Table  1 ). Previous studies highlighted the presence of numerous families of proteins with roles in metabolism that are of bacterial origin but occur only in specific archaeal phyla  [75, 76]. Consequently, we compared all archaeal protein families against a database of bacterial genomes sampled from across the bacterial tree of life to determine the extent to which proteins acquired from bacteria contribute to the archaeal group-specific modules (see the "Methods" section). From 3% (Thermoplasmata) to 34% (Halobacteria) of the protein families in modules that are archaeal group specific have homologs in ≥10 distinct bacterial genomes, with the exception of Methanomicrobia, where 63% of the protein families have bacterial homologs (Table  1 ).
Genome collection A total of 569 unpublished genomes at the time we started the project  [22]  were added to the 2618 genomes of Archaea downloaded from the National Center for Biotechnology Information (NCBI) genome database in September 2018 (Additional file 1: Table  S1 ). One hundred thirty-two genomes were obtained from metagenomes of sediment samples. Sediment samples were collected from the Guaymas Basin (27° N 0. 388, 111° W 24. 560, Gulf of California, Mexico) during three cruises at a depth of approximately 2000 m below the water surface. Sediment cores were collected during two Alvin dives, 4486 and 4573 in 2008 and 2009. Sites referred to as "Megamat" (genomes starting with "Meg") and "Aceto Balsamico" (genomes starting with "AB" in name), Core sections between 0 and 18 cm from 4486 and from 0 to 33 cm 4573 and were processed for these analyses.
Intact sediment cores were subsampled under N 2 gas and immediately frozen at -80 °C on board. The background of sampling sites was described previously  [82]. Samples were processed for DNA isolation from using the MoBio PowerMax soil kit (Qiagen) following the manufacturer's protocol. Illumina library preparation and sequencing were performed using Hiseq 4000 at Michigan State University. Paired-end reads were interleaved using interleav_fasta. py (https:// github. com/ jorvis/ bioco de/ blob/ master/ fasta/ inter leave_ fasta. py) and the interleaved sequences were trimmed using Sickle (https:// github. com/ najos hi/ sickle) with the default settings. Metagenomic reads from each subsample were individually assembled using IDBA-UD with the following parameters: --pre_correction --mink 65 --maxk 115 --step 10 --seed_kmer 55  [83].
Assembly was performed using MEGAHIT  [89]  with default parameters, and the scaffolding function from assembler IDBA-UD was used to scaffold contigs. Scaffolds were binned on the basis of GC content, coverage, presence of ribosomal proteins, presence of singlecopy genes, tetranucleotide frequency, and patterns of coverage across samples. Bins were obtained using manual binning on ggKbase  [90] , Maxbin2  [91] , CONCOCT  [85] , Abawaca1, and Abawaca2 (https:// github. com/ CK7/ abawa ca), with DAS Tool  [86]  used to choose the best set of bins from all programs. All bins were manually checked to remove incorrectly assigned scaffolds using ggKbase. Additionally, 168 genomes were obtained from an aquifer adjacent to the Colorado River near the town of Rifle, CO, USA, at the Rifle Integrated Field Research Challenge (IFRC) site  [92].
Protein clustering Protein clustering into families was achieved using a twostep procedure. A first protein clustering was done using the fast and sensitive protein sequence searching software MMseqs2 (version 9f493f538d28b1412a2d124614e-9d6ee27a55f45)  [102]. An all-vs-all search was performed using e-value: 0. 001, sensitivity: 7. 5, and cover: 0. 5. A sequence similarity network was built based on the pairwise similarities and the greedy set cover algorithm from MMseqs2 was performed to define protein subclusters. The resulting subclusters were defined as subfamilies. In order to test for distant homology, we grouped subfamilies into protein families using an HMM-HMM comparison procedure as follows: the proteins of each subfamily with at least two protein members were aligned using the result2msa parameter of mmseqs2, and, from the multiple sequence alignments, HMM profiles were built using the HHpred suite (version 3.
Functional annotation Protein sequences were functionally annotated based on the accession of their best Hmmsearch match (version 3. 1) (E-value cutoff 0. 001)  [106]  against an HMM database constructed based on ortholog groups defined by the KEGG  [24]  (downloaded on June 10, 2015). The same hmmsearch procedure was used to annotate the protein sequences with the Unifam (Package_20170307)  [28]  and arCOG (ftp:// ftp. ncbi. nih. gov/ pub/ wolf/ COGs/ arCOG/, accessed in February 2022)  [27]  databases. Domains were predicted using the same hmmsearch procedure against the Pfam database (version 31. 0) [107]. The domain architecture of each protein sequence was predicted using the DAMA software (version 1. 0) (default parameters)  [108]. SIGNALP (version 5. 0) (parameters: -format short -org arch)  [32]  was used to predict the putative cellular localization of the proteins.