dataesr/scientific-paragraphs-categorization huggingface dataset:
Development of the Drug-Event Bipartite Network A drug-event bipartite network was created using the R software (version 3. 5. 23) based on the previously compiled data from the different data sources. Each node in the network represents either a drug or an event (MIE, KE, or AO). To enrich the connections between drugs and events, and to have a more comprehensive mapping of the drugs in the AOP space, KERs were investigated using the AOP-wiki database (as of September 2020). Globally, if a KER exists between two events (MIE, KE, AO) that are present in the bipartite network, then the biological events linking this KER was selected and included to the network.
Visualization and Analysis of the Loss Information of the Developed Network In both the methods, an increase in uncertainty and loss of coverage were implemented under python 3. 7 and R, using the R package Reticule as an interface provider between R and Python (https://cran. r-project. org/web/packages/reticulate/reticulate. pdf) (R version 3. 5. 3). All implemented methods and applications in this work are available on GitHub (https://github. com/ QierWU/drug-AOP). Results were visualized and analyzed in Cytoscape V3. 7. 2.
Drug-Event Data Drug-event associations were compiled from two complementary data sources and merged to obtain unique relationships. From the CompTox database, we were able to collect 248 drugs associated with 23 unique MIEs (as of March 2021) (Supplementary Table  S1 ). From the AOP-wiki database, we extracted 117 unique drugs having at least one corresponding event (as of August 2020) (Supplementary Table  S1 ). However, 46 among these 117 drugs were only connected to an AOP, with no information concerning the event linkage (MIE, KE, AO). For example, meclofenamic acid was associated with the AOP 152 'Interference with thyroid serum binding protein transthyretin and subsequent adverse human neurodevelopmental toxicity, " but no KE was specified. Therefore, to develop a more complete network, we manually explored the literature using the PubMed database to enrich the connection between the drugs and the 11 events known to be involved in the AOP 152 (Supplementary Table  S2 ).
For meclofenamic acid, 1,158 publications were identifided. Then, we searched among these abstracts, those mentioning KE of interest. As an example, the MIE 957 "Binding, Transthyretin in serum, " which is know to be linked to the AOP 152  (33)  was retrieved in 5 out of the 1,158 abstracts. With this curation, we were able to decipher the linkage between 46 drugs and 18 events via 41 publications. As a result, drugs were connected to 89 unique events (52 MIEs, 31KEs, 6 AOs). In addition, to enrich the drug-event associations network, we used KERs information that link event pairs from the AOPwiki database (as of September 2020) (Supplementary Table  S3 ). For example, the MIE 718 "Binding, Tubulin" and the AO 728 "Increase, Aneuploid offspring" were present in the developed bipartite network (Supplementary Table  S1 ).
The size of the drug node corresponds to the number of events associated with this drug (from 1 to 12). The added KEs, using the KERs information from the AOP-wiki database, are in directed green dash lines. Directed solid lines indicate direct drug-event associations extracted during te compilation phase. These edges are colored by the event type to which drugs are connected i. e. , drug-MIE in purple, drug-KE in green, and drug-AO in red. ¬COV Edge (MIE 167, KE 468) = 0. 75, leading to a higher confidence about the relationship between these two events. Similarly, the drug propylthiouracil, which targets the MIE 1009, is also linked to 10 other events, among them the MIE 18 "Activation, AhR" and the MIE 245 "Activation, PXR/SXR. " These two MIEs have 19 drugs in common, resulting in a weak loss of coverage (¬COV Edge(MIE 18, MIE 245) = 0.
" Results indicated that the level of information loss during monopartite projection mainly depends on the number of events involved in a linkage pattern (also called the size of linkage pattern), which is illustrated by a case study with the health effect "infertility" (Supplementary Material). Therefore, such methods allow to identify uncertainties and data gaps between the different events involved in an AON, and also to quantify the confidence of a relation between the two events. Although this method is able to illustrate links between drugs and AOPs, one limitation of the developed network is that it relies on existing information. Some causal linkages may have been overlooked or disregarded because of missing or incomplete information. Morever such modeling study should take into consideration the so-called "Matthew effect, " that reflects the difference, in terms of available knowledge (database, literature), between the very well investigated drugs and the ones less studied  (41).
Therefore, an extension of such model could be done by integrating more data, including quantitative information, and to fully evaluate the toxicological effects of drugs. One way could be to adapt existing tools such as AOP-helpFinder, which uses artificial intelligence and text mining to automatically screen the literature, to compile more data  (42, 43). For example, this tool was successfully applied to a set of pesticides by automatically identifying among the 32 million abstracts present in the PubMed database, links between pesticides and events  (44). Another way would be to gather information from publicly available databases, which should become more accessible within the next few years following the interoperability of the data based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) data principles  (45).
There is a need to implement standardization of data to facilitate the use of multiple data sources, as a lack of interporability between all existing databases increases the difficulties for developping integrative models. Other data types could also be considered to have a more complete model as possible, reflecting therefore the biological "true. " As an example, environmental chemicals, pharmacogenomics, or drug-drug interactions could also be taken into consideration. All these data should allow the development of predictive models and emerging tools. Nevertheless, the challenge will be on how to integrate all these heterogeneous data (from various species, different technologies. ) in a common platform  (46). Finally, our study relies on the available names of the KEs extracted from existing databases, and some KEs may have a similar biological meaning (e.
We also identified the most active gut bacteria and their metabolic contribution to tadpole physiology including carbohydrate breakdown, nitrogen recycling, essential amino-acids and vitamin biosynthesis. Conclusions: We present a comprehensive new microbiome dataset of a laboratory amphibian model. Our data provide evidences that studies on the Xenopus tadpole model can shed light on the interactions between a vertebrate host and its microbiome. We interpret our findings in light of bile acids being key molecular components regulating the gut microbiome composition during amphibian development and metamorphosis. Further studies into the metabolic interactions between amphibian tadpoles and their microbiota during early development and metamorphosis should provide useful information on the evolution of host-microbiota interactions in vertebrates.
Finally, the PCR products were sequenced using a Miseq (supplied by Illuminaâ). We obtained a total of 3,647,548 clean read counts from 132 samples, with a minimum of 23,872, a maximum of 179,732 and a median of 43,174 reads. The metadata and number of sequences obtained by sample is given in Supplementary_table_1. 16S rRNA and 16s rDNA sequence analysis We used the FROG pipeline for OTUs identification and taxonomic affiliation  (Escudié et al. , 2018). Overlapping reads were assembled and sequences were dereplicated before clustering using the SWARM algorithm. Chimeric sequences were then removed and OTUs were filtered on abundance for at least 0. 005%. Taxonomic affiliation of the OTUs was performed based on the 16S SILVA database (V. 123) using blast and RDP classifier. We used script implemented in R to perform different microbiome community analysis (provided on GitHub at https://npollet.
Taxonomic assignment for metagenome and metatranscriptome sequences We used MATAM and phyloFlash to extract and assemble full-length or near full-length 16S and 18S rRNA gene sequences from the same set of filtered reads as the one used for the assembly  (Pericard et al. , 2018; Gruber-Vodicka et al. , 2020). The assembled read and the details of the command lines are provided on GitHub at https://npollet. github. io/metatetard/Metagenome. html. Metagenome assembly and gene prediction We filtered and trimmed reads according to their quality and excluded those derived from Xenopus genome using metaWRAP. We selected one assembly of these reads obtained with IDBA-UD using k-mers from 72 to 124  (Peng et al. , 2012; Uritskiy et al. , 2018). More details are given in the Supplementary Extended_materials_and_methods.
We performed binning on contigs longer than 1000 bp using maxbin, concoct, metabat and DAStool  (Alneberg et al. , 2014; Wu et al. , 2016; Sieber et al. , 2018; Kang et al. , 2019). We visualized binning results using vizbin  (Laczny et al. , 2015). We mapped OTUs, metagenome and metatranscriptome reads to the assembly using BBTools to derive coverage and tpm values at the scaffold and gene levels (BBMap). We predicted CDS on the assembled metagenome scaffolds using Prokka  (Seemann, 2014). We used Minpath for metabolic pathway prediction following the strategy and the tools provided in the IMP pipeline scripts as described in https://metagenomics-workshop. readthedocs. io  (Ye and Doak, 2009; Narayanasamy et al. , 2016). We mapped KEGG and EC identifiers on the Interactive Pathway Explorer V3 (iPATH3) to obtain a map of the tadpole's gut microbiota metabolic pathway  (Darzi et al.
Whole shotgun metagenomic and metatranscriptomic analysis of X. tropicalis gut microbiota In the final part of our analysis and since few studies have investigated the genomic functions of the amphibian microbiome, we used a whole shotgun metagenomic and metatranscriptomic approach to better characterize the Xenopus gut metagenome and its activity in prometamorphic tadpoles (Figure  7 ). We obtained a dataset made of 23. 6 and 19. 8 million paired reads using the metagenomic and metatranscriptomic approach, respectively after filtering out reads mapping to the nuclear or the mitochondrial genome of X. tropicalis. We used MATAM, phyloFlash, kraken2 plus bracken and kaiju to perform taxonomic assignation of these reads against nucleotidic and proteic databases  (Menzel et al. , 2016; Pericard et al.
, 2018; Wood et al. , 2019; Gruber-Vodicka et al. , 2020). As expected, bacterial reads were predominant, but we identified also a few microeukaryotes, including various parasites (Figure  S9 , Figure  S10 , Figure  S11 , Figure  S12 ). We found low levels of reads assigned to Archaea using kraken2/bracken and kaiju but these results turned out to be false positives upon closer inspection. Indeed, more discriminant sequence comparison tools such as MATAM, phyloFlash or megablast against SILVA or the NCBI nonredundant nucleotide databases evidenced that these reads were of bacterial origin. We also found a few thousands bacteriophage viral sequences using kraken2/bracken or kaiju but we did not investigate them in more details. Metagenomic and metatranscriptomic sequencing gave similar taxonomic profiles for bacteria according to the number of reads matching 16s rRNA gene sequences (Figure  7A ).
Xenopus tropicalis gut microbiota genes catalog To characterize the Xenopus microbiome, we compiled a gene catalog from our metagenome and metatranscriptome data sets. We assembled the metagenomic sequence reads using different assemblers and compared the resultant assemblies with metaQUAST  (Mikheenko et al. , 2016). In addition, we mapped back all metagenomic and metatranscriptomic reads and also our previously found OTU sequences (Supplementary table  3 ). We finally selected one assembly made using idba_ud, based on the rate of properly mapped reads: 93. 36% and 77. 58% for DNA and RNA reads, respectively and on the number of OTUs identified: 23 OTU sequences matching perfectly and 78 OUT sequences matching with 97% identity to at least one contig. This assembly consisted of 72,183 contigs larger than 500 bp, with a total assembly length of 161.
A more detailed view of the pathways predicted from the tadpole gut metagenome can be seen on the Krona plots available in Figure_S13 and Figure_S14. Next, we compared the metabolic pathways encoded by the X. tropicalis gut metagenome with those encoded by the X. tropicalis genome (Figure_S15). To do so, we mapped the EC and KEGG identifiers associated to metagenome-predicted CDS or to annotated Xenopus genes on the Interactive Pathway Explorer  (Darzi et al. , 2018). We queried the resulting pathway map using terms corresponding to some of the numerous metabolites known to be derived from microbiome activity in vertebrates (Figure  8B ). In herbivorous and omnivorous organisms, gut microbial communities are known to degrade complex carbohydrate fibers and provide short chain fatty acids. We identified 6,823 genes encoding 205 different families of carbohydrate-active enzymes (89 glycoside hydrolase, 50 Glycosyltransferase, 16 Polysaccharide lyase, 16 Carbohydrate esterase and 32 Carbohydrate-Binding Modules as defined in www.
Funding: We used the Data  Intensive Academic Grid  conducted on the  National Science Foundation  funded  MRI-R2  project # DBI-0959894 " to perform some of the bioinformatic analyses. The cytometry facilities of Imagerie-Gif (http://www. i2bc. paris-saclay. fr/spip. php?article279) is member of the  Infrastructures en Biologie Santé et Agronomie (IBiSA) , and is supported by the  French National Research Agency  under  Investments for the Future programs  " France-BioImaging ", and the  Labex "Saclay Plant Science"  ( ANR-10-INSB-04-01  and  ANR-11-IDEX-0003-02 , respectively). Author's contributions: All authors read and approved the final manuscript. TS and IC performed experimental work under the guidance of NP. MB designed and supervised TC for cytometry, and analyzed cytometry results.
Acknowledgements We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees (Bioinfo Genotoul)  for providing computing and storage resources at the start of this project. The present work has benefitted from the facilities and expertise of the cytometry facilities of Imagerie-Gif, (http://www. i2bc. parissaclay. fr/spip. php?article279). We thank  Catherine Zanchetta  and the team of the  GeT facility  for 16S metabarcoding sequencing. We thank  Odile Bronchain ,  Albert Chesneau  and  Muriel Perron  for providing access and use of their Xenopus animal facility in Orsay. We thank  Yves Carton  for pointing us the work of  Olga Metchnikoff. We thank  Franck Bourrat  and  Jean-Michel Rossignol  for critically reading our manuscript. We are thankful to  Wirulda Pootakham ,  Vanessa Marcelino  and one anonymous reviewer for taking the time to review this manuscript.
Data accessibility The sequence datasets generated and/or analyzed during the current study are available in the EMBL Nucleotide Sequence Database (ENA), metagenomic (ERS716504) and metatranscriptomic (ERS716505) https://www. ebi. ac. uk/ena/data/view/PRJEB9311. 16S rRNA gene sequence data are available at https://www. ebi. ac. uk/ena/data/view/PRJEB38248. OTU sequences are available at http://www. ebi. ac. uk/ena/data/view/LR992138-LR992913. Other data type supporting the findings are available within the article and its supplementary files.
Introduction The Jalview  [1]  platform has many features for sequence analysis and visualization and is freely available both as a native "app" and single-page web application  [2]  from its web site  [3]. A core function is to make it easy to run state-of-the-art methods for multiple sequence alignment (MSA). The resulting alignments can be visualized and integrated with other information to further interpret them and create figures for publication. In this chapter, we describe the steps involved in a typical Jalview sequence alignment workflow, as depicted in Fig. 1. Sequences for alignment may be retrieved from public databases or loaded via a variety of common file formats. Access to a range of alignment programs is achieved directly within Jalview through web services  [4, 5] , but Jalview also allows alignments generated by external programs to be imported.
Materials Availability, download, and installation. The latest version of the Jalview Desktop application can be obtained from http://www. jalview. org/download. Installers are provided for Â86-based Windows, Linux, and OSX operating systems, and the minimum Fig. 1  Workflow for creating, analyzing, and generating figures for a multiple sequence alignment in Jalview. Numbers for each stage correspond to stages described in Sect. 3. An interactive version of this workflow can be viewed online at https:/ /tess. elixir-europe. org/workflows/multiple-sequence-alignment-analysis-and-fig ure-generation-with-jalview recommended physical memory is 1 GB. The installers provide the "Jalview Native Application"-which offers enhanced usability through features such as file-type associations, automatically managed Java installation, and "Over the Air Update" to ensure the latest and most secure version of Jalview is launched.
An "executable Jar" file is also provided for users wishing to launch a specific version of Jalview or execute it on a platform for which a Jalview Native Application is not yet available. Jalview packages may also be obtained in Homebrew  [6]  and BioConda  [7]  and are primarily designed for users integrating Jalview into a command-line workflow rather than performing interactive analyses as described here. Memory settings and working with larger datasets. The Jalview Native Application and Jalview Executable Jar will automatically request up to 90% of physical memory to be allocated to a Jalview session. The percentage of memory allocated can be modified via a command-line argument. Currently, no support is provided for working with alignments too big to load into memory. The JalviewJS Web Application.
A range of common bioinformatics sequence file formats (see Note 1) can be imported by "drag and drop" or via options provided on the "File" menu. Data from the system clipboard may also be pasted by right-clicking on the desktop background and selecting the "Paste to New Window" menu option that appears. 2. In addition, Jalview provides a "Sequence Fetcher" which allows import of sequences, alignments, and 3D structures from databases hosted by EMBL-EBI  [8, 9]  (see Note 2). Once the import has completed, an alignment window containing the sequence data is displayed. More sequences may be added by the same methods (via the alignment window's own "File" menu, by pasting from the clipboard, or by dragging files onto the window).
Saving and Loading Project Files A Jalview Project File (JVP) can be created via the Desktop's File-> Save Project menu entry or an alignment window's File-> Save As. menu entry. JVP files store data and visualization settings for alignment windows, including any associated views such as trees and 3D structures (e. g. , the example file that is automatically loaded by default on startup-see Note 4). It is recommended that work is saved after each stage below to avoid data loss, and if the same filename is used, then Jalview will create versioned backups (see Note 5).
Align Sequences A range of alignment programs is offered via the Alignment submenu of the Alignment Window's "Web Services" menu. Jalview provides access to public services provided by the University of Dundee which are suitable for aligning up to 1000 sequences of at most 1000 residues each, and jobs are allowed to run for up to 1 h. Note 6 discusses how to perform alignments of larger sequence sets with Jalview. 1. For some alignment programs, Jalview provides a "Realignment" option. This allows sequences to be added to an existing alignment. See Note 7 for a discussion of its effective use. 2. If a selected region is defined in the alignment window (see Note 8), then only data in that region will be submitted for alignment. Similarly, hidden sequences will not be included in the alignment and hidden columns will force a series of local multiple alignments to be performed on just the visible regions (see Note 9).
EPS Export as "Characters" or Line Art When EPS export is selected, Jalview can either represent each sequence symbol as a character or render the shape of each symbol in the EPS file. The former allows EPS files that can be easily edited in a vector graphics program such as Illustrator-e. g. , to modify sequence ID labels, but can result in EPS files that appear different to Jalview's alignment view. The latter results in larger files but ensures all aspects of the MSA visualization (e. g. , sequence logos, character alignment) are faithfully reproduced.
Interactive Figure Export in HTML Web Pages Other options in the Export submenu of the Alignment View's File menu allow HTML pages to be generated containing either an embedded SVG rendering of the view or a JavaScript visualization such as the BioJS Multiple Sequence Alignment Viewer (BioJSmsaviewer)  [13]. In general, interactive figure export results in web pages that will look and behave differently to visualizations provided by the Jalview Desktop app or the JalviewJS web component (see Note 15).
Export Alignment for Visualization with BioJS-msaviewer (a) The "BioJS" export option generates an HTML page containing embedded BioJSON and the JavaScript code necessary to display the alignment with BioJS-msaviewer. HTML files generated by Jalview that contain BioJSON can be imported like any other alignment file (see step 3. 1). However, Jalview Project files are recommended for long-term archiving of data and visualizations generated during multiple sequence alignment and analysis.
Notes 1. Table  1  lists the file types currently supported by Jalview, the type of data provided, and whether Jalview can export as well as import in that format, along with any caveats regarding their use. For completeness, we also include here formats for annotation, 3D structure, phylogenetic trees, and the Jalview specific formats: Jalview Features Format, Jalview Annotations Format, Jalview Project, and BioJSON. [22]  style PIR description lines  [23]  to specify the filename and chain in a PDB  [24]  or mmCIF  [25]   When importing sequence data, Jalview employs a series of rules to determine the format. If no format is found to match based on these rules or an error is encountered while parsing the data according to the determined format, then Jalview will default to the "Pfam" format reader.
The Pfam format is also used by default when sequence data are copied directly from an alignment window to the system clipboard for other applications. 2  details the databases that Jalview 2. 11 is able to access and the kinds of data they provide. Jalview will present an interactive query dialog for databases that support free text search or a simple "Fetch IDs" dialog for databases for which no query client is available. The specific search capabilities provided when performing a free text search depend on the particular database being queried: all provide a range of fields that can be used to restrict a query (via a drop-down menu to the left of the search box) and also allow structured queries to be entered directly (please see Jalview documentation for details of these).