dataesr/scientific-paragraphs-categorization huggingface dataset:
Metagenomic read mapping. A set of 1,005 metagenomic datasets from 14 studies (Supplementary Table  4 ) were retrieved from ENA and used to perform read mapping against the genome (UHGG) and protein (UHGP) catalogs. Only studies that were not used to generate the UHGG or UHGP catalogs were included. Reads were quality filtered and trimmed using TrimGalore v0. 6. 0 (https://github. com/ FelixKrueger/TrimGalore), and human contamination was removed by aligning the reads with BWA MEM v0. 7. 16a-r1181 (ref. 63 ; default options) against human genome GRCh38. Filtered reads were then mapped using Kraken v2. 0. 8-beta  38  (with default settings) against a custom database of the UHGG catalog available from the MGnify  46  FTP site (http://ftp. ebi. ac. uk/pub/databases/metagenomics/ mgnify_genomes/), and the standard RefSeq database (release 96).
Bracken 64 databases of the UHGG catalog for read lengths of 50, 100, 150, 200 and 250 bp were also generated and have been made available from the MGnify FTP site. Classification improvement was calculated on a per-sample basis as (proportion of reads classified with UHGGproportion of reads classified with RefSeq)/ proportion of reads classified with RefSeq × 100. DIAMOND v0. 9. 21. 122 (ref. 65 ) was used to translate and map the reads against the IGC-90 and UHGP-90 protein catalogs using the 'blastx' function with options '--id 90 --evalue 1e-6 -k 1 --max-hsps 1'. Phylogenetic analyses. Taxonomic annotation of each species representative was performed with GTDB-Tk v0. 3. 1 (refs. 29, 30 ; database release 04-RS89) using the 'classify_wf ' function and default parameters. To use consistent species boundaries between the genome clustering and taxonomic classification procedures, genomes were assigned at the species level if the ANI to the closest GTDB-Tk species representative genome was ≥95% and the AF was ≥30%.
py' script available in the GTDB-Tk repository (https://github. com/Ecogenomics/GTDBTk/). Maximum-likelihood trees were generated de novo using the protein sequence alignments produced by GTDB-Tk: we used IQ-TREE v1. 6. 11 (ref. 66 ) to build a phylogenetic tree of the 4,616 bacterial and 28 archaeal species. The best fit model was automatically selected by 'ModelFinder' on the basis of the Bayesian information criterion (BIC) score. The LG+F+R10 model was chosen for building the bacterial tree, while the LG+F+R4 model was used for the archaeal phylogeny. Trees were visualized and annotated with Interactive Tree Of Life (iTOL) v4. 4. 2 (ref. 67 ). Phylogenetic diversity (PD) was estimated by the sum of branch lengths, with the amount that was exclusive to uncultured species calculated as NAtuRE BiotECHNology | www.
The UHGP catalog was generated from the combined set of 625,255,473 CDS predicted. Protein clustering of the UHGP and IGC 5 was performed with the 'linclust' function of MMseqs2 v6-f5a1c  72  with options '--cov-mode 1 -c 0. 8' (minimum coverage threshold of 80% the length of the shortest sequence) and '--kmer-per-seq 80' (number of k-mers selected per sequence, increased from the default of 21 to improve clustering sensitivity). The '--min-seq-id' option was set at 1, 0. 95, 0. 9 and 0. 5 to generate the catalogs at 100%, 95%, 90% and 50% protein identity, respectively. We clustered the IGC only at 90% and 50% protein identity, as it was originally de-replicated at 95% nucleotide identity  5. Functional characterization of all protein sequences was performed with eggNOG-mapper v2 (ref. 73 ; database v5.
0 (ref. 41 )) and InterProScan v5. 35-74. 0 (ref. 42 ). COG  43  , KEGG  44  , CAZy  74  and viral annotations were derived from the eggNOG-mapper results. Differences in annotation coverage and COG functional categories between the core and accessory genes were evaluated with two-tailed Wilcoxon rank-sum tests in R v3. 6. 0 (function 'wilcox. test'). Expected P values were corrected for multiple testing with the Benjamini-Hochberg method. Cohen's d effect sizes were estimated with the function 'cohen. d' from the Effsize 75 R package. To accurately estimate the proportion of each KEGG module in the species pan-genome, we used the compositional data analysis R package CoDaSeq  76. Pseudocounts for zero-count data were first imputed using a Bayesian-multiplicative simple replacement procedure implemented in the 'cmultRepl' function (method 'CZM').
Final counts were thereby converted to centered log ratios using the 'codaSeq. clr' function to account for the compositional nature of the data and for differences in pan-genome size. SNV analyses. A total of 2,489 species with at least three conspecific genomes were used to generate a catalog of SNVs. For each species, we mapped all conspecific genomes to the representative genome using the 'nucmer' program from MUMmer v4. 0. 0. beta2 (ref. 62 ) and filtered alignments using the 'delta-filter' program with options '-q -r' to exclude chance-and repeat-induced alignments. Thereafter, we identified SNVs using the 'show-snps' program. Single-base insertions and deletions were not counted as SNVs. Each SNV locus was included in the catalog only when the alternate allele was detected in at least two conspecific genomes.
Acknowledgements We would like to thank  P. Bradley  and  Z. Iqbal  for their help in the BIGSI implementation;  D. Wu  for assistance in the identification of uncultured monophyletic groups;  M. Zolfo  for guidance in running CMseq; and  J. Lu  for building the  UHGG Bracken databases. Funding:  European Molecular Biology Laboratory (EMBL) ;  Biotechnology and Biological Sciences Research Council  ( BB/N018354/1  and  BB/ R015228/1 ); and  European Research Council  (project  ERC-STG MetaPG-716575 ) to N. S. S. N. and N. C. K. were supported by the  Chan Zuckerberg Biohub  and by the  US Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , under contract  DE-AC02-05CH11231  and used resources of the  National Energy Research Scientific Computing Center , which is supported by the  Office of Science of the US Department of Energy  under contract  DE-AC02-05CH11231.
Data availability Genome assemblies of the UHGG catalog have been deposited in the European Nucleotide Archive under study accession ERP116715. The UHGG, UHGP and SNV catalogs are available from the MGnify FTP site (http://ftp. ebi. ac. uk/pub/ databases/metagenomics/mgnify_genomes/) alongside functional annotations, pan-genome results and custom Kraken 2/Bracken databases of the UHGG catalog. These data, together with the BIGSI search index of the UHGG catalog, can also be accessed interactively via the MGnify website at https://www. ebi. ac. uk/metagenomics/genomes. Mash distance trees have been generated for each individual species cluster and are available at both the MGnify website and the associated FTP site.
LPIAT is Involved in Phosphoinositide Metabolism According to publicly available expression data, LPIAT was mostly expressed in seeds (Genevestigator (www. genvestigator. com); data hosted at www. arabidopsis. org). We measured the transcript level of LPIAT in seeds and leaves by semi-quantitative RT-PCR (Figure  S4 ). The expression of LPIAT in dry seed was 55-fold higher compared to leaves from one-week-old plants and transcript level in leaves decreased over time. Interestingly, we observed a transient increase of mRNA transcript level during imbibition with a maximal expression level after one hour of imbibition. Our results are consistent with the data of gene expression reported on BAR (http:bar. utoronto. ca/eplant/). Using a β-glucuronidase (GUS) reporter approach, we observed the expression pattern of the LPIAT gene by generating transgenic Arabidopsis lines expressing the β-glucuronidase (GUS) reporter gene under the control of the LPIAT promoter.
Noteworthy, the rescue line showed the same response to ABA treatment than the wild-type or the overexpressing line. To go further, it would be interesting to determine if and how ABA regulates the expression of LPIAT during seed germination, and more precisely during imbibition. A positive control of LPIAT expression by ABA can explain the early germination observed with lpiat mutant. In agreement, micro-array data available on Genvestigator showed a high expression of LPIAT during seed imbibition in presence of ABA. As ABA protects young seedlings against drought by blocking vegetative growth during early development  [50] , we examined the response of lpiat seedlings after a 6 h drought stress on 10 day-old seedlings grown in presence or absence of ABA. All seedlings that initially germinated and grew on medium without ABA died after the drought-stress, whatever the expression level of LPIAT (data not shown).
Seeds were surface-sterilized and plated on 1/2 Murashige and Skoog salt solution (pH 5. 7) containing 0. 7% agar, 2. 5 mM MES-KOH and 1% sucrose. Seeds were stratified on plates at 4 • C for 3 days and germinated at 23 • C in the light or dark. Radicle protrusion through the seed coat was used as the criterion for the assessment of differences in germination between wild type and LPIAT mutant seeds. Seedlings were removed from plates at the indicated times and hypocotyl measurements were made at the indicated times with photographs and Image J software. For primary root length measurements, after 3-day stratification, plates were grown vertically in the dark or light. After 3-day growth, primary roots were measured. For ABA sensitivity experiments, 1  2 MS medium was supplemented with an ethanolic ABA solution at a final concentration of 0.
Histochemical GUS Assays Whole seedlings or different tissue samples were analysed by histochemical assays for GUS activity as described somewhere else  [57]. The plants were observed with a Leica binocular loupe (Leica MZ16F) coupled with a camera (DFC 420) and data processing was done with Leica application suite version 2. 5. 0 R1. For histological analysis of seeds, dry or imbibed seeds were fixed in 4% paraformaldehyde, 4% DMSO in 10 mM PBS (pH 7), placed under vacuum for 15 min and incubated overnight at 4 • C before being dehydrated in alcohol and embedded in paraffin (Paraplast plus; Sigma-Aldrich). Eight µm-thick sections were prepared and were observed under a microscope (Zeiss-Axioplan).
Seed Coat Permeability Arabidopsis dry seeds were incubated in the dark at 30 • C for 48 h in an aqueous solution of 1% (w/v) tetrazolium red (2,3,5-triphenyltetrazolium)  [47]. Seeds were rinsed in water before imaging and percentage of positive seeds was calculated using image J software. Values represent the mean of coloured seeds (%) of 4 biological replicates from 3 independent experiments (n = 12).
Wing data acquisition The above-mentioned genetic characterizations were carried out using DNA extracted from the legs of the bees. All remaining biological material was stored in 90% ethanol at -20 ˚C. Under these conditions, the wings, when not damaged by capture or manipulations, kept their venation pattern intact. Thus, only individuals whose right and left wings were available and correctly preserved were used to perform morphometric measurements. For this, the wings of each individual were cut at their basis, mounted in distilled water on a micrometric slide for scaling, and then photographed with a high-resolution camera mounted on a microscope (Leica DMC5400, Leica Microsystems). Nineteen landmarks were digitized at the vein intersections following Smith et al. [45]  procedure (Fig  1A ).
The x and y Cartesian coordinates of each landmark were collected in ImageJ version 1. 49u  [46]  with the Point Picker plugin (http://bigwww. epfl. ch/thevenaz/pointpicker/) [47]. To avoid any user effect, all measurements were made by a single operator. A total of 283 individuals already genetically characterized (lineage, allele frequencies, HI) were thus characterized morphologically. In addition, 36   [40] ) subdivided into three discrete categories; Afr. , "pure" Africans (HI � 0. 1); Hybrids (0. 1 < HI < 0. 9); Eur. , "pure" Europeans (HI � 0. 9)  [59] ; na, data not available; N, number of individuals; M, managed colony; W, wild colony; E, entrance; I; inside. https://doi. org/10. 1371/journal. pone. 0242053. t001 individuals randomly selected were photographed and digitized twice in separate sessions to quantify measurement errors related to image acquisition and landmark positioning.
Congruence between morphological and neutral genetic variation To assess the congruence between morphometric and genetic datasets, we conducted partial least squares (PLS) analyses in MorphoJ using alternatively wings size and shape (i. e. , centroid sizes and Procrustes coordinates) against microsatellite allele frequencies. The PLS method is based on a singular value decomposition of the matrix of covariances between two separate blocks of variables  [55, 56]. It resulted in paired axes uncorrelated and ordered by decreasing singular values, each forming linear combinations of the variables from each dataset and accounting for as much as possible of the overall covariance. The number of axes was equal to the number of dimensions in the smaller dataset (i. e. 1 for size and 34 for shape).
The overall association between morphological and genetic datasets was assessed by the Escoufier's RV coefficient  [57] , a multivariate generalization of the squared Pearson correlation coefficient (r). Singular values, correlations between paired PLS axes as well as RV coefficients were statistically tested by randomly permuting the individuals (rows) in the shape block (10,000 iterations), so that the covariance structure between blocks was modified without altering the variance within blocks  [55, 56]. Additionally, to parallel dominant patterns of covariance between blocks to the major patterns of variation within each block, PLS singular axes were compared to their counterparts from principal component analyses conducted separately on shape and genetic datasets. For this purpose, angles and correlations between corresponding PLS and PCA axes were computed and statistically tested in MorphoJ  [58].
06/0. 06 for 'pure' Africans, 0. 39/0. 32 for hybrids, and 0. 96/0. 97 for 'pure' Europeans, denoting that hybrid values are slightly shifted towards the African genetic cluster. A one-way ANOVA with post-hoc tests was conducted to compare group means based on wings size. Regarding wings shape, given the unequal distribution of individuals in different clusters and the high dimensionality of the Procrustes dataset  (34 dimensions) , a betweengroup PCA was conducted, as recommended by Mitteroecker and Bookstein  [61]. This allowed (1) examining the relative arrangement of the three clusters in a reduced space of two dimensions, (2) evaluating the significance of group separation, and (3) visualizing related shape changes. Pairwise mean differences and reliability of classification rates were assessed by a leave-one-out cross-validation and permutation tests (30,000 iterations).
To determine if hybrids presented phenotypic transgression, the angle formed by the vectors joining the cluster means (Hybrids-Africans-Europeans: d HAE) in the shape-space was computed and tested. This analyze was achieved in RStudio software (https://www. R-project. org/) [62] , using the trajectory. analysis function from the package geomorph  [63, 64]. Additionally, correlations between size, bgPC1 and bgPC2 scores and HI were estimated.
Effect of hybridization on wing asymmetry Size FA (fluctuating asymmetry) of each individual was given by the difference between the CS of the left and the right forewing. The amount of shape FA of each individual was synthetized in the form of univariate scores based on either Procrustes distances (Procrustes FA scores) or Mahalanobis distances (Mahalanobis FA scores), which take the covariance structure into account  [56, 65]. To establish the reliability of these measures (delivered in MorphoJ when performing ANOVAs), intraclass correlation coefficients (2,1) were computed in the sub-dataset with repeats  [66, 67]. The association between the amounts of FA and size variability was then explored with regression analyzes and permutation tests (10,000 iterations). HI was treated as a continuous, rather than a categorical variable to better infer the relationship between the level of hybridization and the amount of developmental instability, while providing precision, flexibility and statistical power compared to ANOVAs  [68, 69].
Estimating measurement errors Repeated ANOVA measurements were performed on a subsample of 36 honeybees, photographed and digitized twice. The relative contribution of measurement errors (ME) to the total variation for centroid size and shape, due to imaging and landmark digitizing steps (estimated by the mean squares (MS)), was small compared to the components of symmetric and asymmetric variation (Table  2 ). In particular, fluctuating asymmetry (FA), used as an estimate of developmental instability, was found significant, with F ratios sufficiently high to consider ME as negligible (F > 18 and 64, for shape and size, respectively)  [71]. As a result, it was estimated that repeated measurements were unnecessary for the whole data set in order to obtain reliable and accurate estimates of FA.
Effect of hybridization with European-related subspecies on wing size and shape Results from multivariate analyses show that morphological variation coincides well with variation in microsatellite allele frequencies. The patterns of covariance for both wings size and shape, revealed by partial least-square analyses, are (essentially) one-dimensional and parallel the level of hybridization of the individuals (Fig  2 ). It results in an Afro-European continuum of admixture, which also emerges in PCAs as the dominant and only intelligible pattern of variation within shape and microsatellite datasets (except unstructured phenotypic plasticity) (S1 Fig). Overall, this highlights that demographic and stochastic processes, initiated by the recent successive imports of continental European subspecies in Mauritius, are still ongoing and prevail over possible selective processes.
CASCADE Setup In order to make CASCADE as portable as possible, we decided to embed it inside a virtual machine (VM) available for all major operating systems (OS, e. g. , Windows, Linux, Mac OSX). This was achieved using Oracle VM VirtualBox v5. 2. 1  A VM is a fully self-contained simulated computer system that can be executed on any host system (i. e. , a physical computer) that has the VM software installed and enough resources for the simulated system. The VM created for CASCADE uses only a single simulated CPU and 2GB of RAM, and hence should be able to run on most currently available computer systems. It was tested without returning problems on desktop and laptop computers with Intel CORE i5 and CORE i7 CPUs and 8GB of RAM using either Windows 10, MacOS El Capitan / Mojave, or Ubuntu 18.
04 LTS as host systems. As guest OS (i. e. , the operating system running inside the VM), we chose Ubuntu 18. 04 LTS, 2  which is available free online. The virtual disk image (VDI), i. e. , the file that contains the simulated hard disk of the VM, requires around 10GB of disk space which is mostly due to the size of the guest OS and other required software. Software includes the NGINX 3  v. 1. 14. 0 webserver executing the CASCADE source code, and the MySQL 4  v14. 14 database management system that handles all the data stored. Other prerequisites are the Laravel 5  PHP framework v5. 6, the Vue. js 6 JavaScript framework v2. 6. 10, and the Bootstrap 4 7  JavaScript & CSS library. CASCADE's back-end source code is fully written in PHP 7. 2 using Laravel's classes while the frontend is written in JavaScript with the support of Vue.
js. Styling is based on Bootstrap 4 with the addition of vector graphics from the Font Awesome 8  vector icon library. Vector graphics sourced from this library are under the Creative Commons Attribution 4. 0 International license. 9 ASCADE is accessed via web browser. It was developed specifically for Mozilla Firefox but has also been tested with recent versions of Google Chrome without encountering problems. Especially when the database reaches larger numbers of records (i. e. , ten-thousands of entries), Chrome seems to perform better, although parts of the layout may be interpreted differently to Firefox. The source code as well as the fully installed and configured VM can be provided upon request (please direct requests to one of the following authors: LO, ludovic. orlando@univ-tlse3.
fr; XM, xavier. mata@univ-tlse3. fr; CD, clio. dersarkissian@univ-tlse3. fr). We also provide an installation manual alongside the VM for those users willing to create and configure their own VM for running CASCADE. However, please note that the source code is available only upon request and requires an Atlassian Bitbucket 10  account which can be created for free. Using Bitbucket's collaborator feature allows us to safely exchange the code with trusted parties. In case such an account is not wanted, the source code can also be found in a folder inside the VM as indicated in the companion manual. Our goal is to provide CASCADE free to all scientists interested. The procedures for obtaining CASCADE were, however, developed as precautionary measures to reduce the risk of hacking. To maximize safety, we also recommend installing CASCADE on computers connected to firewallprotected, laboratory-internal networks and allowing access from outside the network only through VPN.
It is the user's responsibility to ensure the security of the computer installation, network configuration and password management for any computer running CASCADE in order to maintain the safety of the data stored in CASCADE. Hence, it should be installed and run only on computers for which access control can be guaranteed at all times, either directly or through the network. Moreover, as CASCADE can be used to store personal data (e. g. , from collaborators and study participants), it is subjected to legislation and rules about the protection of personally identifiable information in force in the users' country, state and/or institutions (e. g. , GDPR in the European Union) and with which the user must comply (e. g. , personal data anonymization, encryption, ethical clearance). Like all software under MIT license, CASCADE is provided "as is" without warranty of any kind.
Database Schema Design Each relational database project begins with the schema design during which all attributes required to describe every aspect of the system are defined. These are then further processed to remove redundancies and establish relationships between the different parts of the data. This procedure is called database normalization, which aims at satisfying so-called "normal forms"  (Codd, 1970). A data model that reaches at least the third normal form is usually described as "normalized"  (Date, 2003). Once in this configuration, most if not all anomalies that might arise from adding, deleting, or modifying data are removed. This feature guarantees the present and future integrity of the data and represents one of the major strengths of relational databases. Another one is that the relations established between the data sets allow flexibly combining them in different ways so as to query exactly the information required for certain analyses or tasks.