dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, pairwise F ST (i. e. median genomic distance as defined in the Wright's formulation, where 0 indicates no genomic differentiation and 1 means maximal genomic differentiation) between all station pairs were computable for P. cordata, whereas for P. calceolata, the station 30 had to be removed, and for B. prasinos 20 pairwise F ST were not computable. We obtained 78 pairwise genomic distances (pairwise F ST ) for P. cordata, 66 for P. calceolata and 58 for B. prasinos. Our results show that the Mediterranean Sea populations of B. prasinos had a stronger global genomic differentiation (global F ST = 0. 136) than P. calceolata (global F ST = 0. 066) and P. cordata (global F ST = 0. 045) (Figure  3 ). B. prasinos also showed the most contrasted genomic differentiation (pairwise F ST ) ranging from little (0.
8% of the total variance) separated the saltier and ammonium-rich stations (Dim2 > 0) from the less salty and ammonium-poor stations (Dim2 < 0); and the third PCA axis (Dim3, 15. 2% of the variance) divided the silicate-rich stations (Dim3 < 0) from the phosphate and nitrite-rich ones (Dim3 > 0). Environmental distances were calculated as the variance-weighted Euclidean distances on the first three dimensions (Supporting Information S4). Oceanographic distances were inferred from Lagrangian modelling both as the mean connection times (data from  Berline et al. , 2014)  and as connection probabilities among stations  (Ser-Giacomi et al. , 2015). They ranged between 46. 15 and 545. 9 km (mean: 219. 9 km) and 1. 72 Â 10 À7 and 9. 07 Â 10 À3 (mean: 5. 98 Â 10 À4 ), respectively (Supporting Information S5).
Main challenges for population genomics approach based on metagenomics In this study, we developed a computational method in order to apply population genetic concepts from metagenomic data. This pipeline was designed according to the FAIR principles  (Wilkinson et al. , 2016)  and it can be transposed to any protist lineages as long as reference sequences are available. The number of species that can be investigated with this pipeline is thereby limited by the number of available reference assemblies. Historically, sequencing projects prioritized species of biomedical and biotechnological interest, or species that were easy to cultivate. This has led to strong biases in public molecular databases in which protist values (due to absence of common SNPs) were replaced by the mean value of F ST of the stations.
Type of shape: geographic entities (defined in Figure  1 ). Colours: environmental entities (defined in Figure  4 ). Sibbald & Archibald, 2017). In the last decade, an increasing number of sequencing initiatives ha tried to cope for this limitation  (Keeling et al. , 2014) , but most protist lineages remain unrepresented in public databases (e. g. about 200 genomes in the Genome Online Database in February 2017, mostly parasitic;  Sibbald & Archibald, 2017). So far, only a few dozen species are available for a focused analysis of the Mediterranean Sea. It is, however, very likely that the increasing availability of reference sequences will allow population genomics' analyses based on metagenomic data in the near future. Based on metabarcoding data, our three targeted species were potentially abundant in the Mediterranean Sea (Supporting Information S1).
Our isolation-by-environment hypothesis is in line with this assumption and suggests that the environmental conditions might drive the genomic differentiation of this lineage. For P. cordata, gene flow decreased with geographic distances, supporting a hypothesis of isolation-by-distance for this species  (Wright, 1943). Isolation-by-distance has frequently been suggested as an important driver of genomic differentiation for protists  (Casteleyn et al. , 2010; Demura et al. , 2014; Nagai et al. , 2007)  and can act at different scales. Indeed, isolation-by-distance has been described as a driver of genomic differentiation for the diatom Pseudo-Nitschia pungens at global  (Casteleyn et al. , 2010) , regional  (Casteleyn et al. , 2009)  and local scales  (Evans et al. , 2005). Finally, the spatial scale of our sampling impacts the detection of genomic isolation in our datasets.
ACKNOWLEDGEMENTS The authors thank  J. O. Irisson  for help in calculating geographic distances and  L. Berline  for sharing oceanographic distances. The authors are also thankful to  P. Debeljak  for proofreading the manuscript and to  F. Riquet  for interesting discussions about population genetics. The authors want to thank all people involved in the Tara Oceans project for making data publicly available. This work was funded mainly by our salaries as French state employees and therefore by French taxpayers. Funding for this research was provided by the  ModelOmics project of the Emergence program  of  Sorbonne Université , and partly supported by the project  MEGALODOM , part of the  MASTODON program  from the  MITI, CNRS France. Ophélie Da Da Silva  was supported by a PhD grant from the  French Ministry of Higher Education, Research and Innovation for 3 years.
Data collection Data were collected by computer-assisted personal interviews (CAPI) in study offices in each city, in which blood samples were drawn for HIV and syphilis testing. Upon completion of the interviews, data were immediately sent via internet to a central server. The research project was approved by the Research Ethics Committee of the Federal University of Ceará (n. 1,024,053 -June 23, 2015). Further details about the methodology can be found in Kendall et al. 28.
Data analysis Gile's successive sampling estimator  32  was used to estimate the weighted ratio estimators via RDS Analyst (http://hpmrg. org). Data from the 12 cities were merged into a single database in which each city was treated as its own stratum. The previously defined latent variable DDSO  21  and RDS weights were transferred to the current database for descriptive, bivariate, and multivariate analyses. Stata 15. 0 (https://www. stata. com) was used with complex survey data analysis tools. Multivariate analysis included ordinal logistic regression modelling as the outcome variable was ordinal. Estimators were adjusted with the simultaneous generation of a constant association measure across the categories of the outcome variable  33. The construction of weighted odds ratio (OR) in the logistic model began with a bivariate analysis to assess the factors associated with the outcome variable.
Acknowledgments We wish to thank the  Department of Chronic Conditions  and  Sexually Transmitted Infections/Brazilian Ministry of Health , the gay and other MSM who were interviewed, and the  Brazilian HIV/MSM Surveillance Group: Alexandre Kerr Pontes (Federal University of Rio de Janeiro/Rio de Janeiro) ;  Ana Cláudia Camillo (Alfredo da Mata Foundation/ Manaus) ;  Ana Maria de Brito  ( Oswaldo Cruz Foundation/Recife) ;  Ana Rita Coimbra Motta-Castro  ( Oswaldo Cruz Foundation and Federal University of Mato Grosso do Sul/Campo Grande) ;  Daniela Riva Knauth  and  Andréa Fachel Leal  ( Federal University of Rio Grande do Sul/Porto Alegre );  Edgar Merchan-Hamann e Ximena Pamela Díaz Bermúdez  ( University of Brasília/Brasília );  Luana N. C. Lima  ( Evandro Chagas Institute/Belém );  Maria Amélia Veras  ( Santa Casa de São Paulo School of Medical Sciences/São Paulo) ;  Inês Dourado  ( Federal University of Bahia/Salvador );  Lígia Regina Sansigolo Kerr ,  Raimunda Hermelinda Maia Macena , and  Rosa Salani Mota  ( Federal University of Ceará/ Fortaleza );  Lisangela Cristina de Oliveira  ( Autonomous University Center of Brazil/Curitiba );  Mark Drew Crosland Guimarães  ( Federal University of Minas Gerais/Belo Horizonte );  Maria do Socorro Cavalcante  ( Ceará State Health Secretariat/Fortaleza );  Carl Kendall  ( Tulane University/New Orleans and Federal Univeristy of Ceará/Fortaleza ); and  George Rutherford  and  Willi McFarland  ( University of California San Francisco/San Francisco ).
Testing hypothesis about the biogeography of genes using large data resources such as Tara Oceans marine metagenomes and metatranscriptomes requires significant hardware resources and programming skills. The new release of the 'Ocean Gene Atlas' (OGA2) is a freely available intuitive online service to mine large and complex marine environmental genomic databases. OGA2 datasets available have been extended and now include, from the Tara Oceans portfolio: (i) eukaryotic Metagenome-Assembled-Genomes (MAGs) and Single-cell Assembled Genomes (SAGs) (10. 2E+6 coding genes), (ii) version 2 of Ocean Microbial Reference Gene Catalogue (46. 8E+6 non-redundant genes), (iii) 924 MetaGenomic Transcriptomes (7E+6 unigenes), (iv) 530 MAGs from an Arctic MAG catalogue (1E+6 genes) and (v) 1888 Bacterial and Archaeal Genomes (4.
5E+6 genes), and an additional dataset from the Malaspina 2010 global circumnavigation: (vi) 317 Malaspina Deep Metagenome Assembled Genomes (0. 9E+6 genes). Novel analyses enabled by OGA2 include phylogenetic tree inference to visualize user queries within their context of sequence homologues from both the marine environmental dataset and the RefSeq database. An Application Programming Interface (API) now allows users to query OGA2 using command-line tools, hence providing local workflow integration. Finally, gene abundance can be interactively filtered directly on map displays using any of the available environmental variables.
1 million gene catalogue mostly from marine prokaryotes (4), the Tara Oceans pan-oceanic expedition applied a holistic sampling of plankton from viruses to fish larvae coupled with comprehensive in situ biogeochemical measurements, albeit with sampling bias towards the epipelagic sunlit layer  (5, 6). Marine gene catalogues released from the Tara Oceans sequencing effort include datasets specific to prokaryotes  (7)  as well as eukaryotes  (8). The Malaspina 2010 global circumnavigation (9) used a similar sampling approach applied to the tropical and subtropical deep oceans from surface down to 4000 m depth. Resulting environmental genomics resources have been made available via a variety of modes, including the MAR databases  (10) , MGnify  (11) , Planet Microbe  (12)  and the Ocean Microbiomics Database  (13).
The updated Ocean Gene Atlas  (14)  presented here is unique in presenting 8 trillion of marine environmental read sequences in their environmental context, hence allowing marine biologists to explore the biogeography and phylogeny of plankton genes among a total of 228 millions. Indeed, the Ocean Gene Atlas v2. 0 (OGA2) provides an integrated interactive interface to mine all major Tara Oceans and Malaspina gene datasets characterized as of early 2022 without any requirement for programming or dedicated hardware. Moreover, no account or identification is necessary to run queries, and results visualization occurs on-the-fly.
OGA v2. 0: NEW FEATURES AND UPDATES The Ocean Gene Atlas v2. 0 (OGA2) web service provides a user-friendly interface to identify and geolocate marine environmental homologous sequences using a nucleic acid or protein sequence query. The web service update consists on the one hand in the integration of six datasets from Tara Oceans and Malaspina consortium sequencing efforts, and on the other hand new tools to quantitatively explore contextualized genes of interest in the global ocean ecosystem. An updated user manual is provided online from the OGA2 service web pages.
New resources The first version of the Ocean Gene Atlas deployed its analyses based on two datasets: (i) the Ocean Microbial Reference Gene Catalogue (OM-RGC) comprising 40 million non-redundant mostly prokaryotic gene sequences associated with both Tara Oceans and Global Ocean Sampling (GOS) gene abundances  (7)  and (ii) the Marine Atlas of Tara Ocean Unigenes (MATOU) composed of >116 million eukaryotic unigenes  (8). The OGA2 includes the following new Tara Oceans and Malaspina datasets:   (17). This MGT database is mostly eukaryotic and was built based on the MATOU catalogue (Tara Oceans). 4) 530 bacterial and archaeal MAGs containing 1 million genes  (18). This Arctic MAGs dataset was built using the Tara Oceans Polar Circle expedition. 5) 317 bacterial and archaeal MAGs containing 0.
9 million genes  (19). This MDeep-MAGs dataset was built using the Malaspina metagenomes. 6) version 2 of Ocean Microbial Reference Gene Catalogue (OM-RGCv2) with additional data from the Arctic Ocean comprising a total of 47 million non-redundant gene sequences from 370 marine metagenomes and 187 metatranscriptomes  (20). Together with the gene sequence catalogues, two additional complementary data objects were also included in OGA2: gene abundances for each sample, and sample biogeochemical environmental context (see Data availability section and Table  1 ).
New implementations 1) Application Programming Interface (API) The Application Programming Interface (API) offers researchers the option of command line to facilitate access to OGA2 and ensure the datasets are explored to their fullest. The API uses standard protocols and readily available programming languages, allowing for instance full control of OGA2 through a simple bash script. A tutorial with examples of codes is available at the following address: https://tara-oceans. mio. osupytheas. fr/ocean-geneatlas/build/script/API tutorial. pdf. Three types of API commands are possible as described in Figure  1. The first type is to 'Submit a request' using a JSON file with search parameters, such as a FASTA sequence or Pfam identifier, as well as the dataset to be mined. The Laravel application server (detailed in the 'Data integration and framework' paragraph) from OGA2 sends a response in JSON format with the request identifier and an estimation of the computation time.
The second type of command is to 'Check results' accompanied with the request identifier provided after the initial query submission above. Once the computation is completed, the server will return the URL of the results web page. The third API command is the 'Fetch results' request using the request identifier and the results file of interest. Three files can be provided containing the alignment results, the homologues sequences or the homologues abundances together with the associated contextual environmental data. A throttling limits users to 200 jobs per 24 h, and we advise users to submit no more than one request every 30 seconds. In order to provide the best possible interactive experience, Nucleic Acids Research, 2022 3 Table  1. Dataset information queries launched from the web interface have priority over API requests.
2) Phylogenetic analysis A new feature of OGA2 is the phylogenetic analysis of the user query sequence together with its closest (applying the user defined E-value threshold, or the maximum number of aligned sequences) BLAST hits in both marine metagenomic homologues and reference databases. To do so, the 'Phylogenetic tree' option should be selected on the website submission form. An additional panel section will then display a phylogenetic tree in the results page. For this purpose, the sequence query is used to search homologues in the RefSeq database  (21). If the number of RefSeq homologues is greater than the number of metagenomic homologues, the RefSeq homologous sequences are progressively clustered with CD-HIT  (22)  until the sequence number is equal or less than that of metagenomic homologues sequences (to avoid some cases we observed where RefSeq homologues could clutter the resulting tree, such as queries close to over-represented enterobacteria).
This clustering step is done iteratively by gradually decreasing the threshold of clustering from 100% to a minimum of 60%. The sequences in the resulting combined dataset, consisting of the user query sequence, the metagenomic homologues, and the reference RefSeq homologues, are then aligned with MAFFT  (23). This alignment is cleaned with MaxAlign (24) and trimAl (25) before submission to FastTree  (26)  for phylogenetic tree inference (Figure  2 ). To visualize the resulting tree, the Newick Utilities  (27)  tools suite is used. Once the phylogeny workflow has completed successfully, the resulting phylogenetic tree is rendered in the results interface in a new panel with several phylogenetic tree formatting options. The user query sequence is represented in blue, the metagenomic homologues appear in red, and the RefSeq reference homologues are labelled in green (Figure  3 ).
4) Abundance normalization The abundance of each catalogue gene (for OM-RGCv1 and MATOU) in specific biosamples was estimated by evaluating the coverage of raw sequencing reads mapped to the gene's nucleotide sequence as described earlier  (14). Briefly, depending on the database queried (Table  2 ), abundance estimates may be expressed in one of three available normalization schemes: (i) the gene's read coverage is divided by the sum of the total gene coverages for the sample ('percent of total coverage'), (ii) the gene's read coverage is divided by the total number of reads for the sample ('percent of total reads'), (iii) the gene's read coverage is divided by the median of the coverages of a set of 10 universal single copy marker genes ('average copies per cell') that were previously benchmarked for their suitability for prokaryotes metagenomics data analysis  (28).
In order to estimate the abundance and expression of each MGT unigene in each sample, cleaned reads (from metagenomes and metatranscriptomes) were mapped against the reference catalog as described in  (17). Reads covering at least 80% of read length with at least 95% of identity were retained for further analysis. Unigene expression values and genomic occurrences were computed in RPKM (reads per kilo base covered per million of mapped reads). Gene abundance from MAG catalogues was computed using reads per genomic kilobase and metagenomic gigabase (RPKG). For Euk SMAGs, BAC ARC MAGs and Arctic MAGs gene abundance, we attributed to gene its MAGs abundance computed as described in  (15, 16, 18). For the MDeep-MAGs dataset, the abundance of each MAG was expressed by the number of mapped reads per genomic kilobase and sample gigabase as described in  (19).
5) Data integration and framework All data objects (sample gene abundance tables, environmental context and gene catalogues) were downloaded from ENA, Pangaea or companion websites (Table  1 ) and preprocessed using bash, perl or R (version 4. 03) scripts to generate files for database integration (Figure  6 ). Figure  7  represents the MariaDB version 10. 3. 27 managed relational database schema (note that MAG datasets use a dedicated table). These datasets are queried by Laravel 5. 4 PHP application server that uses a classical Model-View-Controller pattern architecture to create web interfaces. Hosted on dedicated Linux hardware, the application server commu-nicates with the user through an Apache2 HTTP server using HTML5, CSS3, Javascript and AJAX to retrieve user requests and display results.
CONCLUSIONS OGA 2. 0 is a web service for biogeographical analysis of large scale marine environmental genomics datasets. The additional datasets presented here now offer users access to a comprehensive set of environmental sequences, including metagenomes, metatranscriptomes, MAGs and SAGs. The API allows users to run several requests using a command Several options allow user to download the tree in SVG format and intermediate files used in the phylogeny workflow (multi-FASTA homologues, multiple alignment before and after trimming and newick formatted tree). The link 'view multiple alignment' shows the HTML file generated by trimAl. The tree can be changed from radial to linear, the substitution mode or tree inference (gamma law) can be modified, and it is possible to root the tree (with the longest branch or branch specified by the user) and zoom in or out.
The colored multiple sequence alignment can also be displayed. line to facilitate access and ensure the datasets are explored to their fullest. Moreover programmatic execution allows a better documentation of the requests (with the trace of the script) and increased repeatability of results. The automated phylogenetic tree option provides an initial view of the homologues neighborhood that can be valuable for evolutionary studies  (30). OGA 2. 0 has recently been awarded ELIXIR-FR accreditation for its Service Delivery Plan and we maintain our commitment to high performance and stability. The increasing number of users since 2018 illustrated in Figure  8  and the appreciable number of citations (74) since the initial OGA paper (  14 ) underline community interest in the services offered by the Ocean Gene Atlas.
In terms of future development, we plan to explore further available dataset annotation such as MAG ecological niches (15) (https://end. mio. osupytheas. fr/ Ecological Niche database/) as well as Gene Ontology to allow users to query MAG contig sequences for a particular gene but also gene environment to address genome plasticity and evolution (e. g. collinearity and synteny). We encourage scientists to solicit our help in order to integrate additional datasets into OGA2, to which end we can provide a user-friendly data preparation and integration tool.
DATA AVAILABILITY Ocean Gene Atlas 2. 0 is freely available and can be accessed via the following link: https://tara-oceans. mio. osupytheas. fr/ocean-gene-atlas/. Source code is available at GitLab repository: https:// gitlab. osupytheas. fr/ocean atlas/oga. Shotgun sequences are available at the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena) under accession number PRJEB7988 (OM-RGCv1 and v2), PRJEB6609 (MATOU), PRJEB41575 (Tara Arctic metagenome co-assemblies) and PRJEB402 (EUK SMAGs) (see Table  1 ). The predicted genes from the OM-RGC are available at ENA under the accession numbers ERZ094224 and ERZ096909 to ERZ097151, and the protein sequences are available at: ftp://ftp. genome. jp/pub/db/mgenes/ Environmental/Tara. pep. gz. For OM-RGCv2, all data files can be found through BioStudies with the accession S-BSST297 and for the 530 Tara Arctic metagenome co-assemblies with S-BSST451.
All MATOU, EUK SMAGs, MGT and BAC ARC MAGs resources are available at http: //www. genoscope. cns. fr/tara/. Registry of all the samples from the Tara Oceans Expedition (2009-2013) with environmental metadata are available at PANGAEA: https://doi. org/10. 1594/PANGAEA. 875582. For the Global Malaspina 2010 Expedition, all raw sequences are publicly available at both DOE's JGI Integrated Microbial Genomes and Microbiomes (IMG/MER) and the European Nucleotide Archive (ENA). Individual metagenome assemblies, annotation files, and alignment files can be accessed at IMG/MER. All accession num-bers are listed at https://www. nature. com/articles/s42003-021-02112--2#MOESM4 in Supplementary Data 1. The metagenomic data can be found through ENA with accession number PRJEB44456 and the co-assembly for the MAG dataset construction with accession number PR-JEB40454, the nucleotide sequence for each MAG and their annotation files can be found through BioStudies with accession S-BSST457 and also in the companion publication website at: https://malaspina-public.
ACKNOWLEDGEMENTS This article is contribution number  134  of  Tara Oceans. The web server is hosted by the  OSU  Pythéas cluster with the help of  Cyrille Blanpain  and SIP members. Adrien Malgoyre  from  SIP  is thanked for the development of the  OSU Pythéas gitlab. We are grateful to the  Institut Francais de Bioinformatique  for providing help and computing resources. Tara Oceans  (which includes both the Tara Oceans and  Tara Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Ocean Foundation  and the continuous support of  Tara Oceans  consortium members. We further thank the commitment of the following sponsors:  CNRS  (in particular Groupement de Recherche GDR3280 and the  Research Federation for the study of Global Ocean Systems Ecology and Evolution ,  FR2022/Tara Oceans-GOSEE ),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , the  French Ministry of Research , and the  French Government 'Investissements d'Avenir' programmes, FRANCE GENOMIQUE, MEMO LIFE  and  PSL* Research University.