dataesr/scientific-paragraphs-categorization huggingface dataset:
Most contamination cases found correspond to contamination from Stramenopiles in the proteome of Syssomonas multiformis, probably from Spumella sp. 12. However, we also detected Pirum gemmata contamination in the proteome of Abeoforma whisleri, and few from Ichthyophonus hoferi in Sphaerothecum destruens, indicating cross-contamination problems between these ichthyosporeans datasets. Still, these cases of contamination neither affected the phylogenetic inference, as they were removed during the screening, nor the downstream analyses, as these species were only used for species tree reconstruction purposes. We created two distinct versions of the MCs70 dataset: the first dataset including all sequences from Holozoa (ingroup) and from three Holomycota taxa (outgroup) (Holozoa MCs70), and the second dataset including all sequences from Holomyoca (ingroup) and from three Holozoa taxa (outgroup) (Holomycota MCs70).
An alignment supermatrix was created for each dataset, first aligning and trimming each OG per separate [MAFFT -einsi, trimAl -gappyout], and later concatenating the alignments into a supermatrix (Holozoa MCs70: 37 taxa, 17,475 sites and 9. 27% of missing data; Holomycota MCs70: 28 taxa, 17,409 sites and 7. 81% of missing data). We constructed a phylogenetic tree for both MCs70 datasets using ML and Bayesian inference. ML inferences were done with IQ-TREE, and the models chosen for Holozoa and Holomycota MCs70 datasets were LG+C50+F+R7 and LG+C30+F+R6, respectively. Despite ModelFinder suggesting the usage of C60 (ref. 44 ) for Holomycota MCs70, we used mixture models with fewer profiles to avoid potential model overfitting, as some optimized mixture weights were estimated close to zero. Nodal supports for the ML trees consisted of 1,000 IQ-TREE ultrafast bootstrap replicates (UFBoot) and 100 standard non-parametric bootstrap replicates.
Incorporation of prokaryotic homologues into the OGs We incorporated prokaryotic homologues into the clusters before the MAPBOS processing step. For the incorporation of prokaryotic (and viral) homologues into the clusters, we first used DIAMOND 47 v0. 8. 22. 84 [--more-sensitive, -e 1e-05] to align all eukaryotic sequences from euk_db (a subset of draft_euk_db, which includes the species labelled in bold in Supplementary Table  4 ) to a database including 8,231,104 bacterial, 331,476 archaeal and 20,955 viral from Uniprot reference proteomes (release 2016_02; prok_db) (forward alignment approach). The aligned sequences from prok_db were aligned back against euk_db sequences (reverse alignment approach). Hits with a query and target alignment coverages lower than 75% were discarded, as well as hits in which the best-scoring euk_db target of a given prok_db query was a member of a distinct cluster than the best-scoring euk_db query for that prok_db sequence in the forward alignment.
Gene tree inference and gene tree-species tree reconciliation analyses We submitted every post-MAPBOS OGs (or clusters) to a gene tree inference pipeline, consisting of using MAFFT-linsi for the alignment step, trimAl [-gappyout] for alignment trimming and IQ-TREE for the phylogenetic inference. In particular, IQ-TREE was run using the LG+G4 model and sampling 1,000 optimized [-bnni] UFBoot replicates for every gene tree. For the gene tree-species tree reconciliation analyses, we used ALEml_undated from ALE v0. 4 (https://github. com/ssolo/ALE). ALEml_undated requires a distribution of phylogenetic trees for every gene family (the UFBoot replicates in our case) and a species tree. The Opisthokonta fraction of the species tree consisted of the most favoured topology according to our analyses, which only included Opisthokonta taxa (Fig.
Inference of gene fusion events We used CompositeSearch  57  to identify composite gene families, that is, families of genes whose protein sequence is composed by fractions-for example, protein domains-that are separately found in other, component, gene families. CompositeSearch requires as input all-against-all sequence alignments, for which we used the same BLASTp results used for OrthoFinder (see above), although alignment hits corresponding to draft_euk_db species not represented in euk_db were removed. Before being used as input for CompositeSearch, BLASTp results were preprocessed with cleanBlastp (included in CompositeSearch) to retain only the hit with the highest score among all hits involving the same querytarget pair. CompositeSearch was run with the default parameters and forcing the software [-f] to work on the clusters resulting from the processing of the OG from OrthoFinder by the MAPBOS pipeline.
Functional annotation of sequences and OGs Protein domain architectures of euk_db sequences and of prok_db captured sequences (see above) were determined with PfamScan 58 using Pfam A v29. Cluster of Orthologous Groups functional categories (functional categories) and KEGG Orthology Groups (KOs)  59  were annotated to euk_db sequences with eggNOG-mapper 60 v1. 0. 3-3-g3e22728, using DIAMOND for the alignments of euk_db sequences against the eggNOG database (the functional category 'S: unknown function' was ignored as it does not include functional information). Once sequences were annotated, the functional categories and KO annotations of every cluster were determined by averaging the annotations of the corresponding cluster members. For example, if a cluster includes two sequences (SeqA and SeqB), and SeqA was annotated with the functional category K and SeqB with the functional categories B and K, that cluster would be annotated as 0.
Statistical analyses Statistical analyses were carried out either in Python, mainly with the libraries Pandas  61  and NumPy  62  , or in R. All descriptive statistics plots (with the exception of those including phylogenetic trees, which were constructed with ITOL  63  ) were done in R, particularly with the ggplot2 package  64. Mann-Whitney U-tests (one-tailed) were done in Python with SciPy  65  (scipy. stats. mannwhitneyu). More specific statistical analyses are detailed below.
Correspondence analyses of relative functional category compositions The relative genomic representation of functional categories are examples of compositional data (CoDa)  66  , in which every column (a functional category) is represented by a relative fraction and the sum of all values is the same for every row (genome). Owing to the fact that no orthogonality and collinearity are properties of CoDa, most commonly used multivariate analyses techniques such as principal component analyses are unappropriated for CoDa analyses and alternatives such as correspondence analyses are recommended instead  66. Correspondence analyses were done in R  67  with FactoMiner package  68  and the plots were constructed with the factoextra package  69.
The code is available at https://doi. org/10. 6084/m9. figshare. 13140191. v1 ('fungiMetazoa_predModels' in Code. 300322. zip). We expect the predictors to capture the genomic compositional features well, as, for example, in the case of Metazoa, Trichoplax adherens, the animal with the lowest degree of phenotypic complexity among the sampled species, is the node with lowest probability (Extended Data Fig. 4d ). All of these analyses were carried out in Python using packages from Sci-kit learn  70  , TensorFlow  71  and Keras 72 libraries.
Extended Data Fig. 1 | The importance of taxon sampling in ancestral gene content reconstructions and intron density across eukaryotes. (A) Influence of taxon sampling in the ancestral reconstruction of protein domains innovations (Pfam domains). Note that with the addition of taxon sampling from unicellular relatives of animals (Choanoflagellatea -C-, Filasterea -F-, Teretosporea -T-), the number of pre-metazoan protein domain originations increase at the expense of originations that were originally detected at M4 in the 'No unicell. Holozoa' condition. The origin of every protein domain was inferred at the last common ancestor of all the species in which the domain is represented. This analysis was carried out with the taxon sampling euk_db, first excluding all representatives from C, F and T groups ('No unicell.
Holozoa'), and then progressively adding data from these groups in a chronological order corresponding to when the genomic data from the representatives of these groups became publicly available. Ancestral node abbreviations: M4 = last common ancestor (LCA) of Metazoa. M3 = LCA of Choanoflagellatea and M4. M2 = LCA of Filasterea and M3. M1 = LCA of Teretosporea and M2. O = LCA of Opisthokonta. (See Fig. 1d  for an illustration of the phylogenetic context of these ancestral nodes). (B) Distribution of introns per kb in an eukaryotic dataset including the four genomes sequenced for this manuscript as well as the metrics included in the Fig. 1 -source data 1 of ref. 18. Extended Data Fig. 2  | Genome size and gene count metrics in eukaryotes. Distrubtion of (A) 'Genome size (Mb)' and (B) 'Number of genes' in an eukaryotic dataset including the four genomes produced as well as the metrics included in the Fig.
1 -source data 1 of ref. 18. Extended Data Fig. 3  | Intron per gene and mean intro size metrics in eukaryotes. Distrubtion of (A) 'Introns per gene' and (B) 'Mean intron size (bp)' in an eukaryotic dataset including the four genomes produced as well as the metrics included in the Fig. 1 -source data 1 of ref. 18. Whereas a potential loss of non-coding regions in the P. atlantis genome during the metagenome decontamination could have led to an underestimation of the genome size metric, the high ratio of introns per gene and the small size of introns found strongly suggests that the intron-richness of this nucleariid is not an artefactual result.
(E) Cluster of Orthologous Groups (COG) categories with functional information (referred to as functional categories along the manuscript). 7 ) in the species from euk_db dataset representing (A) Metazoa and Fungi (B) Opisthokonta (i. e. , Metazoa, Fungi, and also the other Holozoa and Holomycota sampled, Supplementary Table  4 ), and (C) every ancestor represented by an internal node in the Opisthokonta phylogeny (see Fig. 7  in Supplementary Information 3 for a mapping of every ancestral lineage to the phylogeny). (D) Phylostratigraphic origin of each functional category for those gene families that experienced increments in copy number (either gene gains or gene originations) in the last common ancestor of Metazoa for each functional category (Supplementary Table  12 ). (E) Phylostratigraphy of the ancestral gene content of Homo sapiens for each functional category (Supplementary Table  11 ).
Acknowledgements E. O. -P. was supported by a predoctoral  FPI grant  from  MINECO  ( BES-2015-072241 ) and by  ESF Investing  in your future. E. O. -P. , D. L-E. , A. S. A. and I. R. -T. received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7-2007-2013 ) (Grant agreement No. 616960 ) and also from grants ( BFU2014-57779-P  and  PID2020-120609GB-I00 ) by  MCIN/AEI/10. 13039/501100011033and  ' ERDF A way of making Europe '. E. O. -P. and G. J. Sz. received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 714774 ). T. A. W. was supported by a  Royal Society   University Research Fellowship  ( URF\R\201024 ) and  NERC  standard grant  NE/P00251X/1.
This work was supported by the  Gordon and Betty Moore Foundation  through grant  GBMF9741  to T. A. W. and  G. J. Sz. J. S. P. and E. B. received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7-2007-2013 ) (Grant agreement No. 615274 ). D. V. T. and cell culturing were supported by the  Russian Science Foundation  grant no. 18-14-00239 , https://rscf. ru/project/18-14-00239/. Culture of P. vietnamica was obtained as the result of field work in Vietnam as part of the project ' Ecolan 3. 2' of the  Russian-Vietnam Tropical Center. P. J. K. is supported by an  Investigator Award  from the  Gordon and Betty Moore Foundation  (https://doi. org/10. 37807/GBMF9201 ). We thank the  CRG/UPF FACS Unit , the  CRG Genomics Unit  and  M. Antó-Subirats  for technical assistance;  D.
Data availability The raw sequence data and assembled genomes generated in this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB52884 (https://www. ebi. ac. uk/ena/browser/view/PRJEB52884). The genome assemblies are also available in figshare (https://doi. org/10. 6084/m9. figshare. 19895962. v1). Protein sequences of the species used in this study were downloaded from the GenBank public databases (https://www. ncbi. nlm. nih. gov/protein/), Uniprot (https://www. uniprot. org/), JGI genome database (https://genome. jgi. doe. gov/portal/) and Ensembl genomes (https://www. ensembl. org). The following specific databases were also used in this study: Pfam A v29 (https://pfam. xfam. org/), EggNOG emapperdb-4. 5. 1 (http://eggnog5. embl.
As expected  11, 12  , IFNα inhibited infection by sevenfold (Fig. S1 ). Infected cells were sorted based on S expression (Fig. 1a ), and DNA was extracted from infected and non-infected control cells. Integrated sgRNA sequences in each cell fraction were amplified by PCR and sequenced by Next Generation Sequencing (NGS). Statistical analyses using the MAGeCK package  35  led to the identification of sgRNAs significantly enriched or depleted in infected cells representing antiviral and proviral genes, respectively (Fig. 1b ). Although our screen was not designed to study proviral genes, we did successfully identify the well-described SARS-CoV-2 co-factor cathepsin L (CTSL)  36  , validating our approach. USP18, a gene encoding a negative regulator of the IFN signaling pathway  37  , and ISG15, which favors Hepatitis C Virus replication  38  , were also identified as proviral ISGs.
Viral replication was assessed by measuring the levels of viral RNA in the supernatant of infected cells using RT-qPCR (Fig. 2a ). In parallel, we titrated the levels of infectious viral particles released into the supernatant of infected cells (Fig. 2b ). As expected, infection was significantly reduced in CTSL KO cells, confirming the proviral effect of this gene 36. Among the selected antiviral candidate genes, only 2 had a significant impact on SARS-CoV-2 replication: LY6E (as expected), and to an even greater degree, DAXX. Both genes restricted replication in absence of IFNα, an effect which was detectable at the level of viral RNA (8-fold and 42-fold reduction of infection, respectively, Fig. 2a ) and of infectious virus (15-fold and 62-fold reduction, Fig. 2b ). Based on available single-cell RNAseq datasets,  43  DAXX is expected to be expressed in cell types physiologically relevant for SARS-CoV-2 infection such as lung epithelial cells and macrophages (Fig.
DNA was extracted from both cellular fractions and sgRNA loci amplification was carried out by PCR. Following NGS, bio-informatic analysis using the MAGeCK package was conducted. This figure was created with BioRender. com. b Screen results. By taking into account the enrichment ratios of each of the 8 different sgRNAs for every gene, the MAGeCK analysis provides a positive score for KO enriched in infected cells (i. e. restriction factor, represented in the top fraction of the graph) and a negative score for KO depleted in infected cells (i. e. proviral factors, represented in the bottom portion of the graph). Genes with an FDR < 0. 05 are represented in black. 3 genes with a FDR > 0. 05, but with a p value < 0. 005 were additionally selected and are represented in red. c Individual sgRNA enrichment.
The strains BetaCoV/France/IDF0372/2020 (Lineage B); hCoV-19/France/IDF-IPP11324/2020 (Lineage B. Besson J. , Bioliance Laboratory, saint-Herblain France; Dr. Vincent Foissaud, HIA Percy, Clamart, France, respectively. These strains were supplied through the European Virus Archive goes Global (Evag) platform, a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement #653316. The hCoV-19/Japan/TY7-501/ 2021 strain (Lineage P1) was kindly provided by Jessica Vanhomwegen (Cellule d'Intervention Biologique d'Urgence; Institut Pasteur). The mNeonGreen reporter SARS-CoV-2 was provided by Pei-Yong Shi  53. SARS-CoV FFM-1 strain  65  was kindly provided by H. W. Doerr (Institute of Medical Virology, Frankfurt University Medical School, Germany).
The Middle East respiratory syndrome (MERS) Coronavirus, strain IP/COV/MERS/Hu/France/FRA2 (Genbank reference KJ361503) isolated from one of the French cases  66  was kindly provided by Jean-Claude Manuguerra (Cellule d'Intervention Biologique d'Urgence; Institut Pasteur). SARS-CoV-2 viral stocks were generated by infecting VeroE6 cells (MOI 0. 01, harvesting at 3 dpi) using DMEM supplemented with 2% FBS and 1 μg/mL TPCK-trypsin (Sigma-Aldrich #1426-100MG). SARS-CoV and MERS-CoV viral stocks were generated by infecting VeroE6 cells (MOI 0. 0001) using DMEM supplemented with 5% FCS and harvesting at 3 dpi or 6 dpi, respectively. The Yellow Fever Virus (YFV) Asibi strain was provided by the Biological Resource Center of the Institut Pasteur. The Measles Schwarz strain expressing GFP (MeV-GFP) was described previously.
Nested PCR was performed using the Herculase II Fusion DNA Polymerase (Agilent, #600679) and the DNA oligos indicated in Supplementary Table  1. PCR1 products were purified using QIAquick PCR Purification kit (Qiagen #28104). PCR2 products were purified using Agencourt AMPure XP Beads (Beckman Coulter Life Sciences #A63880). DNA concentration was determined using Qubit dsDNA HS Assay Kit (Thermo Fisher #Q32854) and adjusted to 2 nM prior to sequencing. NGS was performed using the NextSeq 500/550 High Output Kit v2. 5 75 cycles (Illumina #20024906). Screen analysis. Reads were demultiplexed using bcl2fastq Conversion Software v2. 20 (Illumina) and fastx_toolkit v0. 0. 13. Sequencing adapters were removed using cutadapt v1. 9. 1. 70 The reference library was built using bowtie2 v2. 2. 9. 71 ead mapping was performed with bowtie2 allowing 1 seed mismatch in -local mode and samtools v1.
9. 72 Mapping analysis and gene selection were performed using MAGeCK v0. 5. 6, normalizing the data with default parameters. sgRNA and gene enrichment analyses are available in Supplementary Data 1 and 2, respectively and full MAGeCK output at https://github. com/Simon-LoriereLab/crispr_isg_ sarscov2. Generation of multi-guide gene knockout cells. 3 sgRNAs per gene were designed (Supplementary Table  2 ). 10 pmol of NLS-Sp. Cas9-NLS (SpCas9) nuclease (Aldevron #9212) was combined with 30 pmol total synthetic sgRNA (10 pmol for each sgRNA) (Synthego) to form RNPs in 20 µL total volume with SE Buffer (Lonza #V5SC-1002). The reaction was incubated at room temperature for 10 min. In total, 2 × 10 5 cells per condition were pelleted by centrifugation at 100 × g for 3 min, resuspended in SE buffer and diluted to 2 × 10 4 cells/µL.
Sanger data files and sgRNA target sequences were input into Inference of CRISPR Edits (ICE) analysis https://ice. synthego. com/#/ to determine editing efficiency and to quantify generated indels. 73 Percentage of alleles edited is shown in Table  1  (n = 3). Hit validation. In total, 2. 5 × 10 4 A549-ACE2 KO cells were seeded in 96-well plates 18 h before the experiment. Cells were treated with IFNα and infected as described above. At 72 h post-infection, supernatants and cellular monolayers were harvested in order to perform qRT-PCR and plaque assay titration. Infectious supernatants were heat-inactivated at 80 °C for 10 min. For intracellular RNA, cells were lysed in a mixture of Trizol Reagent (Invitrogen #15596018) and PBS at a ratio of 3:1. Total RNA was extracted using the Direct-zol 96 RNA kit (Zymo Research #R2056) or the Direct-zol RNA Miniprep kit (Zymo Research #R2050).
Non-infected, antibody-stained samples served as controls for signal background. The number of cells infected with MeV-GFP were assessed with the GFP signal, using non-infected cells as controls. Data were acquired with an Attune NxT Acoustic Focusing Cytometer (Life technologies) and analyzed using FlowJo software. Entry assays. Cells were seeded at 1 × 10 5 cells per well in 24-well plates in DMEM with 1% FBS. The next day, cells were infected with VSV* (MOI 0. 001) or VSV*ΔG-S (MOI 7) in DMEM without FBS. The virus suspension was removed after 2 h and replaced with DMEM with 1% FBS. 16 h p. i. , cells were washed once with PBS, trypsinized and subsequently fixed in 4% PFA. Fixed cells were washed once with PBS and analyzed by flow cytometry. The percentage of infected cells was identified based on GFP expression.
Acknowledgements We thank the  Cytometry Platform, Center for Technological Resources and Research, Institut Pasteur , for cell sorting experiments. This work was funded by the  Institut Pasteur Coronavirus Task Force ,  CNRS  ( UMR 3569 ), the  Labex IBEID  ( ANR-10-LABX-62-IBEID ), the Occitanie region and by the  ANR  ( ANR-20-COVI-000 , projects  IDIS-COVR  to M. V. and  Alpha-COV  to S. N. ). A. M. K. is supported by a grant of the  French Ministry of Higher Education, Research and Innovation. G. M. is supported by a grant from the  Agence nationale de  recherches sur le sida et les hépatites virales (ANRS). S. M. A. is supported by the  Pasteur-Paris University (PPU)  International PhD Program. We thank  Michael Emerman ,  Daniel Marc  and  Ignacio Caballero-Posadas  for helpful comments on the manuscript.
We first compile the largest to-date atlas of crossmate cross-trait phenotypic correlations, examining a broad array of previously-studied metabolic, anthropometric, psychosocial, and psychiatric phenotypes across two large population based samples in the United Kingdom (n=81,394) and Denmark (n=746,566). We find that these cross-mate correlations, which are derived from phenotypic measurements, explain a major portion of empirical marker-based genetic correlation estimates for the same trait pairs (R 2 =76% across datasets). We next present theoretical and simulation-based results demonstrating that xAM biases genetic correlation estimates and yields significant estimates even among traits with uncorrelated genetic effects. We then use a simulation-based approach to evaluate the extent to which empirical level of xAM alone might plausibly explain empirical genetic correlation estimates among previously studied traits, finding that substantial fractions of empirical genetic correlation estimates are congruent with expectations for biologically independent traits subject to xAM.
Genetic correlation estimates mirror cross-mate phenotypic correlations We begin by quantifying the extent to which empirical genetic correlation estimates align with cross-trait spousal correlations across a broad array of previously studied phenotypes: a set of 20 anthropometric, metabolic, and psychosocial traits measured in the UK Biobank (UKB)  [21]  and a collection of five psychiatric disorder diagnoses ascertained from Danish civil registry data  [22] [23] [24] [25] [26]. We first identified 40,697 spousal pairs within the UKB sample and randomly selected 373,283 mate pairs within the Danish sample and then estimated cross-mate correlations. For a pair of phenotypes Y , Z, there are three cross-mate correlation parameters: r yy (resp. r zz ) the correlation between mates on phenotype Y (resp.