dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein abundance estimation. To avoid estimations based on potential false negatives derived from sample processing or genome binning, all raw reads were aligned on the unified archaeal protein catalogue using DIAMOND BLASTx  79. The hits were counted and the result was transformed into a matrix of the number of hits for each protein per study using the pandas library  80. This resulted in a mapped protein matrix used for further statistical analysis to minimize the risk for sample or batch effects in our dataset (Supplementary Material 2). Besides genomic information (genome length, number of contigs, N50, GC content, genome completeness, genome contamination, and number of rRNAs and transfer RNAs), 11 metadata categories (numerical 2, categorical 9) could be considered for the dataset. Information about the geographic origin was available for 1,063 genomes (91% of the dataset covered countries from maximum to minimum: the USA, Israel, Spain, Sweden, Fiji, UK, Austria, Denmark, the Netherlands, France, China, Peru, Germany, Madagascar, United Republic of Tanzania, Australia, Canada, Ireland, Italy, Russia, El Salvador, Iceland, Mongolia, Norway, on five continents; Supplementary Table  1d  and Fig.
3 ). To overcome biases introduced by potential residual MAGs contamination issues, we focused our analyses on patterns observed in two or more genomes, unless stated otherwise. In addition, we explored protein diversity patterns and their functional characterization among isolated genomes to corroborate those observed in MAGs. Finally, to avoid estimations based on potential false negatives derived from sample processing or genome binning, raw reads were mapped on the unified archaeal protein catalogue (Supplementary Material 1) as a reference to generate a mapped protein matrix (Supplementary Material 2), which minimized the risk for sample or batch effects in our dataset. Supervised classification and regressions with RandomForest were applied to predict respective metadata categories from the unified archaeal protein catalogue and the mapped protein matrix with the q2-sample-classifier plugin  81.
To reduce the risk of overfitting, the matrices were downsampled to a minimum of 50 genomes for each tested metadata category, as recommended by scikit-learn 0. 24. 1 (ref. 82 ). First subsets of each metadata category were created from the entire protein matrix and randomly split into a training set and a test set with the proportions 80%:20%. By using K-fold cross-validation, the training set served as a learning model to predict class probabilities with settings for optimized feature selection and parameter tuning. In the end, model accuracy was determined by comparing the predicted values between the training and test datasets. Pan-genome analysis. Pan-genome analysis was performed using Panaroo 83 in 'strict' mode because it accounts for potential annotation errors, fragment assemblies and contaminated genomes to recover an accurate pan-genome.
Pan-genome analysis was performed for archaeal genomes of the same families and the same genus. We used Heaps' law (η = κ × n -α) to estimate whether we had an open or a closed pan-genome  84  , This analysis was carried out in the R package 'micropan' 85 using a default permutation value of 100, where η is the predicted number of genes for a particular number of genomes (n), and κ (intercept parameter) and α (decay parameter) are the constants used to fit the curve after the genomes have been ordered in a random way. An open pan-genome is indicated by α < 1 whereas a closed pan-genome is indicated by α > 1. Estimation of growth rates. Growth rates were estimated using GRiD 86 in the multiplex mode (minimum coverage = 1 and reassignment of ambiguous reads) by a customized GRiD database based on the created subset of high-quality archaeal genomes on species level.
As the original raw reads were not available for each representative genome and the remaining read sets were not made publically available, growth rate estimates covered 131 metagenomic read sets (70% of all archaeal genomes grouped at strain level). In-depth taxonomic and clustering analyses of the various genera. ANI distances and tree matrices were calculated using the online resources of the enveomics platform  87  , MaGe  76  , as well as Microbial Genomes Atlas (MiGA)  88. Dendrograms, built on the ANI tree matrix, were annotated using the iTOL tool (Interactive Tree Of Life)  89  , and processed using InkScape. For specific considerations involving additional genomes from animals, a subselection of the archaeal genomes was reanalysed together with the additional genomes following the same settings as described for the protein catalogue procedures above (respective datasets are given in the Supplementary Table  12 ).
Initial HGT analysis. Representative genomes from isolates and MAGs with 0% contamination according to CheckM results were selected for these analyses (Methanosphaera spp. : 8 from humans, 7 from animals; Methanobrevibacter spp. : 30 from humans, 11 from animals). A list with full details is provided in Supplementary Table  11. Genomes from animals were obtained from NCBI (ncbi. nlm. nih. go/genome), representing all available high-quality genomes (isolates, MAGs) of the respective genus at the time point of analysis (2020; Supplementary Table  11 ). The selected genomes were further characterized as previously mentioned using eggNOG-mapper v. 2. 0. 0 (ref. 70 ) and the previously mentioned databases (Genome annotation and protein catalogue). Annotated genes were sorted according to their taxonomic affiliation (eggNOG output information: 'best_tax_level'), and the proportion of archaeal and bacterial genes was calculated for all genomes and genera.
Detection of virulence and resistance genes. To predict potential virulence genes in all 1,167 archaeal genomes, ABRicate v. 0. 5 (https://github. com/tseemann/ abricate) was used to profile the following databases: CARD  93  , Resfinder  94  , PlasmidFinder  95  , ARG-ANNOT  96  , EcOH 97 and MEGARes 2. 0 (ref. 98 ), as well as NCBI AMRFinderPlus  99. As ABRicate is solely based on DNA sequences, blastX searches using DIAMOND  79  was used to complement results from ABRicate on the level of protein sequences in the virulence factor database (VFDB v. 20191122)  100, 101  and CARD together with the Resistance Gene Identifier  102. Specific groups of proteins and genes involved in human interaction were investigated according to available annotations from MaGe 76 and eggNOG-mapper  70.
Viral identification, quality estimation and comparisons to viral databases. To assess the presence of prophages, VirSorter2 (ref. 103 ) was used to scan all MAGs. CheckV 104 was used to estimate completeness and assess the quality of VirSorter2-predicted viruses. To ensure that we overcame possible contamination issues that could potentially result from the binning process, we selected proviruses flanked within archaeal contigs for this analysis. VirSorter2 tends to overestimate provirus boundaries (https://github. com/jiarong/VirSorter2), therefore CheckV is recommended to apply a quality control check and remove false positives. CheckV looks for host-virus boundaries based on differences in GC content and gene annotation in a sliding window approach. Proviruses (detected by VirSorter2 followed by CheckV and CheckV on a separate run) that had a quality assignment of medium quality (50-90% completeness) of high quality (>90% completeness), or were complete, were considered for further analysis.
Quality assignments by CheckV are based on Minimum Information about an Uncultivated Virus (MIUViG) standards  105. It is worth mentioning that proviruses detected by Virsorter2 followed by CheckV were detected by running CheckV independently. The selected proviruses were subsequently clustered with MMseqs2 using the 'linclust' function with the same parameters previously specified and MMseqs function 'result2repseq' to select a viral cluster representative. We identified 94 viral populations in our genome datasets. This number is the result of clustering 45 high-quality (>90% completeness) and 130 medium-quality (50-90% completeness) archaeal proviruses, flanked within archaeal contigs, at 95% identity and 80% coverage, where one to a maximum of two proviruses were identified per host. The selected cut-off is commonly used for viral species  3, [105] [106] [107] [108] [109] [110]  definition (Extended Data Fig.
8  and Supplementary Table  10a-c ). Open reading frames of viral populations with the previously specified MIUViG quality were used as input for vConTACT2 (ref. 40 ) including Viral RefSeq genome (v. 97). VconTACT2 is used to affiliate a family or a genus rank group to viral populations and thus to determine taxonomic diversity. A recent study was published by Gregory et al. 3  where a human GVD harbours 33,242 viral populations, including 0. 1% archaeal viruses resulting from 2,697 gut metagenomes in 32 studies. This dataset was used as a reference database to scan the identified viral scaffolds using MMseqs2 'easy-linclust' function at 50, 80, 90 and 95% identity. Comparison to environmental archaea. For considerations based on 16S rRNA genes, 16S rRNA genes of representative genomes were extracted using Metaxa2 (ref.
111 ) (n = 314; not all 16S rRNA genes could be recovered). This dataset was supplemented with data from amplicon sequencing studies and clone sequences from archaeal signatures from human gastrointestinal samples (dataset described in ref. 9  ; n = 381 in total). These sequences were aligned and classified using the SILVA rRNA database  112. More specifically, the retrieved 16S rRNA genes were subjected to the ACT tool (alignment, classification and tree service)  113  , using the following parameters: basic alignment parameters: 'removed'; search and classify, minimum identity with query sequence: '0. 95'; number of neighbours per query sequence: '10'; compute tree; workflow: 'Denovo including neighbours' and default parameters; and advanced tree computation parameters, positional variability filter: 'none' , domain: 'archaea'.
Unclassified sequences were removed from the dataset. Via SILVA SINA, ten next neighbours were selected, and information on their isolation source was gathered through NCBI (Supplementary Material 3 and Supplementary Table  12a ; the final dataset contained 566 sequences). Grouping was performed at the genus/species level, and information on the percentage of host-associated archaea in all groups was displayed as a circle packing plot (RawGraphs online tool, https://app. rawgraphs. io). For genome-based analyses, a set of 623 archaeal MAGs identified from environmental and gastrointestinal samples (for example, rumen, guinea-pigs and baboon faeces) was used as a reference dataset for comparison to the set of archaea isolated from the human gut microbiome  2, 114. All environmental genomes used were >50% complete, and also up to 90% complete, with <5% contamination as well.
To estimate the pairwise ANI distance between environmental archaeal genome dataset (Supplementary Table  12 ) and the archaeal genomes from the human gut microbiome, we used fastANI  67  , a tool that effectively discriminated intra-and interspecies boundaries for >90,000 prokaryotic genomes. Articles NaTurE MIcrOBIOlOgy Articles NaTurE MIcrOBIOlOgy Extended.
Acknowledgements The research was funded in whole, or in part, by the  Austrian Science Fund (FWF)  (P 32697, P  30796  given to C. M. E. ). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. C. M. E. and A. M. thank the  Medical University of Graz  for the computational resources of the MedBioNode and the  Life Science Compute Cluster (LiSC)  operated by the  Computational Systems Biology group at the University of Vienna. We thank the  Medical University of Graz ZMF Galaxy Team: Core Facility Computational Bioanalytics, Medical University of Graz , funded by the  Austrian Federal Ministry of Education, Science and Research, Hochschulraum -Strukturmittel 2016 grant as part of  BioTechMed Graz.
We thank  K. Bick  for discussion on taxonomic naming, and the scientific support provided by Z. Hameed. R. S. received partial funding from the  Kiel Marine science cluster at the Christian-Albrechts-University (CAU)  and the  German Ministry of Education and Science, BMBF  (no. 031B0851B ), which is highly appreciated. C. M. C. and A. W. used the resources of the high-memory computer nodes and computation support from the  Computing Center of CAU Kiel. A. A. and R. F. were supported by  EMBL. J. F. B. thanks  Hub Innovergne  for a grant (' Investissements d' Avenir'  , no. 16-IDEX-0001 CAP 20-25 ). G. B. and S. G. thank the  Institut Pasteur  and the  French National Research Agency (ANR)  for their support through grants  Methevol  (grant no. ANR-19-CE02-0005-01 ) and  Archevol  (grant no.
Data availability All the recovered genomes are available for bulk download in an archived folder 'archaea_gut-genomes. tar. gz' in generic feature format at http://ftp. ebi. ac. uk/pub/ databases/metagenomics/genome_sets. All used genomes and metagenomes in the present study are publicly available on NCBI and MGnify resource. Accession no. details and paper references of used genomes and metagenomes are summarized in Supplementary Table  1a-f.
Density data Strong variations in population densities are recorded within the European continent, even inside similar regions, e. g. in the Iberian Peninsula a range from less than three to over 30 wild boars per km 2 is registered. Food availability, such as crops or supplementary feeding, are the main factors affecting wild boar productivity and consequent local population densities. 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000  1980 1985 1990 1995 2000 2005   (Massei et al. , (2015)  (courtesy of John Wiley and Sons, 02/06/2020). https://www. wageningenacademic. com/doi/pdf/10. 3920/978-90-8686-910-7_8 -Thursday,  March 11, 2021 12:53:25 AM -IP Address:79. 82. 72. 233 Estimates of the size and density of wild boar populations are key factors to monitor and understand the epidemiology of wild boar diseases, such as ASF.
Practically, a map of priorities was adapted every two weeks according to previous results (localisation of the last positive fresh cases, passive surveillance results from previous weeks) and sent to the field coordinators. Some criteria were also considered (such as topographic wetness index, heat load index and distances from rivers) to target specific environments according to the ASF carcass model previously described  (Morelle et al. , 2019). This model was built to respond to the emergency and was based on the first 200 positive cases in Belgium and on the datasets from the Czech Republic and Poland. As soon as the carcass was detected, the precise geographic location was reported to the team in charge of carcass removal and destruction.
Livestock units are defined by the association of one breeder, one type of animal and one type of production (dairy farms, fatteners or pasture farms). In accordance to the 2016-2017 national census database and Groupement de Défense Sanitaire de La Réunion (GDS Réunion survey, the main association of breeders of the island) survey, the island comprises 2560 livestock units (Fig. 1 ) of cattle (1337), goats (980), sheep (174), deer  (12)  and horses (57) distributed among 2070 breeders. For the model, livestock units occupying the same production space, whatever the type of animal, were considered as a single geographic unit. Conversely, geographically separated livestock units of the same breeder were considered as separate units. Therefore, 2214 husbandry locations with distinct geographical coordinates, composed of single or multiple livestock units, were defined and used as elementary geographic units in the Culicoides abundance model.
Models of Culicoides presence and abundance For each husbandry location, the abundance of each Culicoides species from February 2016 to June 2018 was estimated using the temporal dynamics models developed in  [46]. This estimated abundance reflects the number of Culicoides that could have been captured near the animals by an OVI (Onderstepoort Veterinary Institute) light trap overnight. The temporal models are hurdle models, i. e. a presence/absence model (logit-link logistic regression model) combined with an abundance model (log-link mixed-effect zero-truncated negative binomial model), built from a 2-year dataset of biweekly catches in 11 farms. All variables and coefficients used to estimate Culicoides abundances are presented in Additional file 1: Tables  S1 to S5. To apply the models to the scale of Reunion Island, the random effect of farms in the count part of the hurdle model was neglected.
Spatio-temporal dynamics The representation of the spatio-temporal dynamics of Culicoides was built with Ocelet language and open modeling platform (www. ocelet. org; Ocelet codes are freely available on the CIRAD Dataverse). Ocelet is an openaccess domain-specific language and simulation tool for modeling changes in geographical landscapes and facilitating the processing of geographical information  [66]. Our model comprises four main elements called "entities": (i) the 2214 husbandry locations (point geometry), characterized by their respective values of eco-climatic area, land use, husbandry location density, animal density and building opening size, (ii) the weather stations (point geometry) whose daily minimum and maximum temperatures, rainfall, wind speed, global radiation and humidity are imported as text files (csv format), (iii) the satellitederived vegetation indices, imported as raster data, and (iv) a 1-km-wide hexagonal grid to map model output (Fig.
imicola and C. enderleini at low altitude and during the hot and rainy season; (iii) showed that C. imicola and C. kibatiensis have a wide spatial distribution and that there is virtually not a single husbandry location that would not be affected by Culicoides. This prediction was verified by data from the March 2018 campaign where Culicoides were found in 95 out of the 101 catches. This confirms that Culicoides species can occupy a wide variety of climates encountered on Reunion Island as already mentioned for the Palearctic region  [11]. The spatial distribution of species observed during the March 2018 survey also confirmed the global distribution in the island described by Desvars et al. [45]  and Grimaud et al. [46]. However, the greater sampling effort made and the use of the reference trap (OVI) enabled detecting a larger distribution of C.
Usually, for Culicoides, the spatial and temporal components of their abundance distribution are modeled separately. Only Brugger and Ruble  [41]  and Rigot et al. [43]  built spatio-temporal models on C. obsoletus spp. and C. imicola, respectively. However, these models did not incorporate environmental variables such as land use, eco-climate, host type and density although Conte et al. [88]  and Purse et al. [83]  suggested the increased model accuracy when estimating Culicoides abundance. In this study, environmental and host variables as well as the entrance size of buildings, which may influence Culicoides abundance depending on their exophilic or endophilic behaviors, were included for the first time to model the spatio-temporal distribution of Culicoides. It should be noted that the use of the Ocelet modeling platform, a free software dedicated to the modeling of spatial dynamics, greatly facilitated the integration and processing of the geographic information corresponding to the climate, environment and host variables  [66].
Conclusions The modeling approach enabled developing dynamic maps of abundance for the five Culicoides species present in Reunion Island for the first time to our knowledge. Indeed, the use of the GIS and Ocelet spatial modeling platform enabled easy integration of different climatic and environmental variables and facilitated the development of dynamic risk maps. As the approach relied on the extrapolation to the entire island of dynamic models initially developed in 11 sites, an extensive trapping campaign was set up to validate models on a new dataset. Models suggest that the five species have a wide distribution and long period of activity, which could support continuous circulation of BTV and EHDV. Fit of abundance models were acceptable for C. bolitinos, C. enderleini and C. kibatiensis but were not as good for C.
google. com/ site/ ventu Wind speed at 2 m 24. 1% of missing data; correction of the wind riec1/ calen dar above the ground (30)* data provided at 10 m above the ground according to the roughness of the landscape according to [46] Global radiation (28)* Vegetation index (NDVI) At site location MODIS Terra 16-day composite images; 250 m Product MOD13Q1 [61]; https:// lpdaac. usgs. spatial resolution gov/ Non-dynamic variables * Number of meteorological stations recording the data Eco-climate area At site location Map of "Urban Planning and Native Plants Approach" (DAUPI): http:// daupi. cbnm. org/ palet te/#/ taxons) Land use Buffer area 2016-2017 land-use map [62, 63]: http:// aware. cirad. fr Husbandry location density 2016-2017 national and GDS Réunion census databases Animal density Building opening size At site location.
c Motif enrichment analysis of core promoter elements in Zld-dependent (Zld-dep), H2A. Z positive (H2A. Z pos) and H2A. Z negative (H2A. Z neg) promoters. d Gene set enrichment analysis using Metascape  56  showing the top 5 most significant GO terms per group (Zld-dep, H2A. Z pos and H2A. Z neg). Each GO-term is supported by the color scale in -log10 q-value and the fraction of observed genes presenting the proportion of observed genes in the respective GO term. Data representation was arranged as proposed by ref. 115. See also Supplementary Fig. 3l  This finding was further corroborated by analyzing gene expression over developmental time using data produced for the modENCODE project  57. Here, we found that H2A. Z-positive genes are transcribed continuously throughout embryonic development and at relatively stable levels (a characteristic of housekeeping genes), whereas H2A.
DNA from these libraries was then purified using AMPure beads (Beckman Coulter, A63881) following the manufacturer's instructions. The quality of the purified libraries was assessed with Fragment Analyzer (Advanced Analytical). MNase-seq. MNase-seq data presented in Fig. 1a, c  was obtained from ref. 22. MNase-seq data of Fig. 4a, b  was produced for this manuscript, with two replicates per genotype. This protocol was modified from the Cut and Run protocol published by ref. 82. Embryos were smashed using a pestle in 50 µl of the MNase wash buffer (20 mM HEPESpH7. 5, 150 mM NaCl, 0. 5 mM Spermidine, 1× Protease inhibitor cocktail), then rinsed with 150 µl of MNase wash buffer and centrifuged (600 × g, 3 min at room temperature). Cells were wash afterwards in 500 µl MNase wash buffer and resuspended in 1 ml of MNase wash buffer.
Integrity and size-distribution of the samples was assessed also after library preparation by running on Fragment Analyzer (Advanced Analytical). ChIP-seq, RELACS ChIP-seq, ATAC-seq and RNA-seq data processing. Raw short read sequencing was processed uniformly using default parameters of snakePipes-v2. 0. 2  83. RNA-seq was processed using the mRNA-seq workflow (-trim option). ChIP-seq, RELACS and ATAC-seq were processed with the DNAmapping workflow (-trim, --dedup, -properPairs), followed by assay-specific workflow, i. e. , ChIP-seq and RELACS with the ChIP-seq workflow and ATAC-seq by the ATAC-seq workflow. All libraries were mapped to dm6, using the Ensembl version 96 annotation  84. Blacklisting of hyper-accessible regions. ATAC-seq was used to identify hyperaccessible regions. For that, we used peaks called using snakePipes-v2.