dataesr/scientific-paragraphs-categorization huggingface dataset:
XenoGI was used only with the L-dataset as it requires a manual selection of related genomes with a phylogeny. We selected four or five genomes from closely related species and compared them with mashtree  (Katz et al. , 2019)  to obtain the phylogenetic tree for each analyzed species. The selected genomes and the trees that were used for XenoGI are available from https://github. com/axbazin/panrgp_supdata. For Islandviewer and PredictBias, the predicted GIs were downloaded from their respective websites. Sofware versions, or commit numbers, and the mode of installation are provided in https://github. com/axbazin/panrgp_supdata. To evaluate these tools, we compared their predictions with the positive and negative regions of the two datasets using the protocol described in  (Bertelli et al.
Preparation of the MAG dataset In addition to the benchmark, 1416 Escherichia coli MAGs that have been published in a recent metagenomic study  (Pasolli et al. , 2019)  were downloaded from https://opendata. lifebit. ai/table/SGB. These MAGs were annotated using Prokka 1. 13  (Seemann, 2014)  before being analyzed by panRGP to predict RGPs and spots. The analysis was made with version 1. 1. 72 of PPanGGOLiN using the -defrag parameter to link potential gene fragments to their native gene family.
Software overview The methods of panRGP for the detection of RGPs and spots have been implemented in the PPanGGOLiN pangenomic software suite (version ≥ 1. 1. 72) available through Github (https://github. com/labgem/PPanGGOLiN) under the CeCiLL 2. 1 open source license. It is coded in Python3 with embedded code in C and can be easily installed using bioconda  (Grüning et al. , 2018). We have also written an extensive documentation of every possible output files and the different ways of using the software in the GitHub wiki (https://github. com/labgem/PPanGGOLiN/wiki). An overview of the panRGP workflow is given in Figure  2. The whole workflow can be run through the command 'ppanggolin panrgp'. First, the PPanGGOLiN partitioned pangenome graph is built from a set of input genomes chosen by the user.
Each step of the workflow has a dedicated subcommand in the software suite. The user can then adapt parameters or (re-)execute only a part of the workflow. These subcommands take as input either the raw original files or an HDF5 file representing the pangenome. When a reference pangenome is available, a new genome of the same species can be used as input to predict RGPs using the algorithm described in section 2. 1. This is done by the 'align' subcommand that maps the genes of the genomes to the gene families of the pangenome using the MMseqs2 software. The 'align' subcommand can also be used with protein sequences as input. In this case, they will be aligned to the pangenome gene families and the related RGPs and spots will be extracted, thus providing contextual information for proteins of interest.
The panRGP workflow ends by providing different output files. Tabseparated values (tsv) files contain a summary of the predicted RGPs and spots. Figures can be drawn to represent all RGPs of a spot using the genoplotR library  (Guy et al. , 2010). Furthermore, a subgraph of the pangenome graph (in a GEXF format) can be extracted to represent all the gene families found in a spot with their genomic organization (see subsection 3. 3). It can then be visualized with the Gephi software  (Bastian et al. , 2009)  by applying a layout algorithm such as ForceAtlas2  (Jacomy et al. , 2014).
Benchmark results To evaluate the panRGP method, we ran a benchmark as described in 'Materials and Methods' section in comparison with ten other tools for GI prediction and on two different reference datasets. Those methods i i "output" -2020/3/26 -8:51 -page 5 -#5 i i i i i i The C-dataset contains GIs on 81 genomes that were automatically predicted using a comparative genomic method. Results for the different methods are presented in Table  1. The panRGP method gives the best results in terms of M CC, accuracy and precision whereas IslandViewer4 produces better results regarding F 1score and recall. Overall, computed metrics for IslandViewer4 and panRGP are very close. Methods based on sequence composition do not perform as well. Some have a good precision (e. g. SigiCFR, IslandPath-DIMOB) but their recall and accuracy values are lower than comparative genomics based methods.
It should be noted that the C-dataset was generated using comparative genomics which may explain the good performance of IslandViewer4 and panRGP, which are the only tools using comparative The L-dataset contains curated GIs on 6 genomes that were expertized. Results for the different methods are presented in Table  2. The benchmark was carried out on the same methods plus XenoGI which is a comparativebased method that uses a phylogenetic tree to detect insertion events from HGT. As with the C-dataset, panRGP performs best in terms of M CC, accuracy and precision, as well as the F 1score. Regarding the recall, XenoGI provides the best results. IslandViewer4 performs well but the metrics drop somewhat in comparison to the C-dataset. IslandCafe and GI-cluster which both rely on composition and functional annotation fare much better on this dataset.
For XenoGI, authors indicate the use of 500 gigabytes (GB) of memory and a run time of 20 hours on 50 threads for 40 strains, thus limiting its use on larger datasets. Conversely, panRGP can use far more than hundreds of genomes without requiring extensive computational resources. For the complete workflow of panRGP including the pangenome construction and RGP/spot prediction, it takes 3 minutes and 1. 2 GB of memory to analyze 40 strains of E. coli and 45 minutes and 14 GB of memory for 1000 strains of E. coli on 16 threads of an Intel Xeon CPU E5-2699v3. Most of the time is dedicated to genome annotation and pangenome partitioning.
Application of panRGP on a pangenome built from MAGs To illustrate the potential of panRGP on MAGs, we studied the genomic context of a previously described hotspot in E. coli  (Lescat et al. , 2009)  using a pangenome constructed from MAG sequences from a recently published metagenome dataset  (Pasolli et al. , 2019)    Each node is a gene family and an edge is drawn between two families that share neighboring genes. Colors for persistent, shell and cloud pangenomes partitions are orange, green and blue, respectively. The size of the nodes is proportional to the number of occurrences in the spot. For each family, the most represented gene name is indicated. The tRNA leuX and the fim operon are not represented as they are not members of spot predicted by panRGP. leuX is located next to yjgB and the fim operon is just before nanC.
This visualization was produced using Gephi  (Bastian et al. , 2009)  and the ForceAtlas2 layout algorithm  (Jacomy et al. , 2014). similar to the one that was already computed on a pangenome built from GenBank genomes (3706 families)  (Gautreau et al. , 2020)  indicating that the persistent was well retrieved even though the MAGs are much more fragmented and incomplete than genomes from isolates. As a consequence, pangenome partitions obtained on these MAGs can be a reliable source of information to predict GIs with panRGP. A total of 47 692 regions were predicted by panRGP among which 18 030 are in-between persistent genes and could be to predict spots of insertion with the algorithm previously described. Those 18 030 RGPs have been grouped into 294 spots of insertion. A list of all spots with descriptive metrics are available from https://github.
Conclusion We presented an original method that can identify RGPs on thousands of genomes and analyze them together to detect spots of insertion. Indeed, panRGP uses a partitioned pangenome graph of gene families that makes comparative-based approach to predict GIs more efficient. Indeed, our method is much more scalable on large datasets than already published tools, which rely on time-consuming pairwise sequence comparisons. We showed that panRGP results are highly reliable when compared to a dataset of curated GIs. We introduced a novel algorithm for the detection of spots of insertion which was illustrated in the context of the analysis of an E. coli hotspot using MAGs from the human gut. Overall we believe that panRGP provides an original approach to detect GIs to study their diversity and dynamics in a species of interest.
Its ability to predict GIs and spots among thousands of genomes makes it an ideal approach for large scale studies. The tool is freely available and easily installable as part of the PPanGGOLiN software suite. It is also integrated in the MicroScope platform with a dedicated web page for result analysis and exploration of prokaryotic genomes  (Vallenet et al. , 2019). An improvement of panRGP could be to analyze conserved alternative paths within RGPs using the pangenome graph structure. This could allow to automatically identify functional modules, i. e. set of genes involved in the same biological process akin to what was described in  (Lescat et al. , 2009)  and  (Touchon et al. , 2009).
Statistical Analysis Data collected were processed using R software version 3. 4. 2. Normality of all data sets was assessed using the Shapiro-Wilk test and the equality of variances by the Levene test. For data following normal laws, a two-way ANOVA was performed to evaluate the effects of inoculation and species on survival rate, height, and collar diameter. Chemical parameters, coverage, specific richness, Shannon index, and dry biomass were also analyzed with Bonferonni test with a significance threshold set at 0. 05. We analyzed species composition below and outside Casuarina canopy as a function of years using non-metric multidimensional scaling (NMDS).
Acknowledgments: The " Laboratoire Mixte International Adaptation des Plantes et microorganismes associés aux Stress Environnementaux " (LAPSE). We thank  Chris McMillan  ( International Programs Coordinator Jesuit Mission ) for his participation in English correction and  Amadou Mbarrick Diarra  for the design of the area map. Our thanks to the whole village of  Palmarin, especially Camille and Gilbert  for their help in maintaining the plantation.
Biological material We used a Drosophila melanogaster (Meigen, 1830) Oregon-R strain usually maintained on banana medium (233 g. L -1 banana, 62 g. L -1 sugar, 62 g. L -1 dead yeast, 25 g. L -1 ethanol, 10 g. L -1 agar and 5 g. L -1 nipagin) at 21°C with a 14 h/10 h day-night cycle. The four bacterial strains used had been isolated from feces of adult Oregon-R flies and had been described in  Guilhot et al. (2019). Bacterial strains were identified as Staphylococcus sp. (accession number MK461976 in the NCBI database), Enterococcus sp. (MK461977), an Enterobacteriaceae (MK461978) and an Actinobacteria (MK461979). The taxonomic resolution of our analyses is unfortunately modest and these bacteria do not usually dominate the Drosophila microbiota. However similar strains had already been identified as associated with Drosophilids  (Chandler et al.
54, df = 1, p = 0. 01) (Fig. S2 )supporting the need to include this factor in all the analyses. The age of freshly emerged adult flies (χ² = 0. 65, df = 1, p = 0. 42) and the yeast concentration in the larval medium (χ² = 0, df = 1, p = 1) had not significant influence on yeast presence in adults. The bacterial treatment had no significant effect on the yeast concentration in the medium two days after the formation of the last pupa (F5,49 = 1. 18, p = 0. 33) (Fig. 2 ). Yeast presence in fruit flesh was detected in all replicates but one. Bacteria could be observed in young adults that emerged from most combinations of larval environment and bacterial treatment (Fig. S3 ). However, the proportion of bacteria-positive groups never exceeded 25%. When bacteria were detected, load varied from 1 to 33 bacterial cells per adult fly (data not analyzed statistically).
Known IMEs use many different mobilization strategies and therefore exhibit diverse mobilization modules  [3]. In this work, we consider as IMEs only elements whose mobilization module encodes a relaxase, eventually a CP but no T4SS protein (no VirB4). In Firmicutes, IMEs encode relaxases that belong to superfamilies found in ICEs (MobP, MobC or MobT) and conjugative plasmids (MobV or MobQ). We recently proposed that IMEs could also encode relaxases belonging to superfamilies of initiators of rolling circle replication harboring PF01719, PF01719-PF00910, PF02407 or PHA00330 domains  [16]. CIMEs are decayed elements deriving from ICEs and IMEs by deletion that retained their att recombination sites but not their conjugation/mobilization modules and all their genes involved in recombination (for a review see  [2] ).
S. salivarius Strains and Genome Analysis In this work, the genomes of 75 S. salivarius strains were analyzed. Dates and sites of sampling of the strains are given in Table  S1. Twenty of the 21 strains isolated from our strain collection  [33]  were subjected to whole genome sequencing using an Illumina HiSeq2000 sequencer by Beckman Coulter Genomics (2 × 100 bp after paired-end library construction, at least 60 × coverage). De novo assemblies were performed using CLC Genomics Workbench (CLC Bio) using default parameters. Scaffold of the genomes was built by using the Genome Finishing module of CLC Genomics Workbench with the S. salivarius JIM8777 genome as Guédon et al. [34]. Some assembly gaps were filled by PCR and sequencing. Automatic annotation for each genome utilized the pipeline AGMIAL (Bryson 2006 agmial).
The sequences (raw reads and assembled scaffolds) of the 20 strains have been deposited in the EBI-ENA database under the study number PRJEB37543. Accession numbers of the assemblies are also indicated in Table  S1. The coordinates of the elements are included in Table  S2. The remaining 54 genomes were retrieved from the NCBI genome databank either as complete genomes (n = 7) or as scaffolds of WGS (n = 47) (last accessed may 2019). Pseudocontigs were generated using CONTIGuator (http://combo. dbe. unifi. it/contiguator, with default parameters) with the S. salivarius JIM8777 genome as reference. Unmapped contigs were added at the end of the pseudocontigs. When available, the annotations were transferred to contigs using Geneious prime 2020. 1. 1. The nucleotidic sequences of elements are available in the Supplementary File S1-55.
Phylogenetic Tree Based on Single Nucleotide Polymorphisms (SNPs) Phylogenetic relationships among S. salivarius strains were evaluated by analysis of single nucleotide polymorphisms (SNPs) using the CSI Phylogeny software (version 1. 4) on the CGE website (https://cge. cbs. dtu. dk/services/CSIPhylogeny/). A multifasta file was generated using the NCTC8618 type strain as reference and including SNPs of all the 75 S. salivarius aligned genome assemblies. The phylogenetic tree of the strains was inferred using the Maximum Likelihood method based on the Tamura-Nei model implemented in the Mega7 software  [35].
Detection and Delineation of the Integrative Elements The workflow used to detect, delineate and classify ICEs was described previously  [12]. Briefly, for ICE identification, signature proteins of the recombination module (integrase) and the conjugation module (relaxase, CP and VirB4) were searched by BLASTp comparison (BLAST 2. 9. 0+) against a curated database of 1029 signature proteins extracted from Firmicutes ICEs and IMEs. False positives were then filtered out by retaining only candidates that meet the four following criteria: the identity percentage (≥25%), the alignment coverage (≥40%, must cover the functional domain), the E-value (≤1 × 10 -5 for CP and VirB4, ≤1 × 10 -4 for relaxase and integrase) and the hit length (≥320 aa for integrase, ≥180 aa for relaxase, ≥500 aa for VirB4, between 180 aa and 700 aa for short CP and between 1000 aa and 1200 aa for long CP).
Genes encoding signature proteins were then co-localized. If all four proteins were present, the element was considered as ICE. Its delineation was done by searching for DRs at their two ends by BLASTn analysis (word = 7 bp) using either the 3 or the 5 end of the potential target gene as query. When DRs were absent, too short or too degenerated to be detected by BLASTn, the sequence of the region containing signature proteins was compared to chromosomal sequences of S. salivarius strains devoid of the analyzed element and/or to sequences of elements sharing a very closely integrase using Megablast (word = 16 bp). The DRs were then identified by manually comparison of the ends of syntenic regions. To resume, all elements flanked by recombination sites and/or DRs and which encode an integrase, a relaxase, a CP and a VirB4 were considered as ICEs.
Characterization of Cargo Genes Encoded by ICEs and IMEs Taking into account data on cargo genes in the literature, 31 functional categories related to fitness and 10 category tags were defined. The attribution of cargo genes encoded by ICEs and IMEs to these categories was based on keywords found in the functional annotation of the genes or on alignment with six external resources with significant hits: AMRFinderPlus  [37] , BACTIBASE  [38] , VFDB  [39] , REBASE  [40] , NORINE  [41]  and MEROPS  [42]. AMRFinderPlus is packaged with its own search engine (AMRfinder) and was used with default parameters. BLASTp was used to query the other resources. Alignments were considered significant if they met the following stringent criteria: (i) for protein of size ≤100 aa: coverage ≥ 80%, identity ≥ 80%, e-value ≤ 1 × 10 -10 ; for protein of size >100 aa and ≤250 aa: coverage ≥ 70%, identity ≥ 70%, e-value ≤ 1 × 10 -20 ; (ii) for protein of size >250 aa and ≤500 aa: coverage ≥ 65%, identity ≥ 65%, e-value ≤ 1 × 10 -40 ; (iii) for protein of size >500 aa: coverage ≥ 60%, identity ≥ 60%, e-value ≤ 1 × 10 -60.
IME Prevalence and Diversity Among the 75 S. salivarius genomes, more than 90% (n = 69) carry at least one IME/dIME and only six genomes are devoid of it. Among the latter, two are also devoid of ICEs (S. salivarius HSISS4 and S. salivarius F4-20). A total of 165 IMEs and two dIMEs were identified, one of which is carried by a plasmid (IME_SsalM18_rpmG) (Table  S3 ). As seen in Table  S3 , the occurrence of IMEs/dIMEs varied within strains. Numerous chromosomes carry two IMEs/dIMEs (n = 30) but this number can go up to seven in strain AF23-9AC. These 167 IMEs/dIMEs were classified into five superfamilies in Table  1  by taking into account the domain composition of their relaxase that is the main protein of the mobilization module. The most prevalent superfamily of IMEs/dIMEs is the IME_PF02486 superfamily (n = 113, including two dIMEs) that comprises elements with a relaxase exhibiting a PF02486 domain Table  1 , also known as Rep_trans.
The four other superfamilies of S. salivarius IMEs/dIMEs are IME_PF01719 (n = 28), IME_PF13814 (n = 13), IME_PHA00330 (n = 6) and IME_PF01719-PF00910 (n = 7). The 40% sequence identity clustering of the relaxases sharing the same catalytic domain allows to classify relaxase families. In total, 11 families of relaxases were retrieved Table  1 , column 2. The comparison of these families with those previously described in streptococci  [16]  reveals the existence of a new family of relaxases, Rel_PF01719-PF0910_5. In this previous study  [15] , we also found that half of the streptococcal IME mobilization modules include a CP and that all these CPs except two do not belong to the canonical VirD4 superfamily but to the TcpA superfamily. Here, the percentage of S. salivarius IMEs/dIMEs encoding a CP is somewhat lower (33%, n = 54) and the majority of these CPs also belong to the TcpA superfamily, but the fraction is somewhat lower (77%, n = 42).
A 40% sequence identity clustering of CP allows to subdivide the TcpA superfamily into seven families, see Table  1 , column 3. The TcpA_2, TcpA_6 and TcpA_12 families were already described  [15] , whereas TcpA_13, TcpA_14 and TcpA_15 are novel families. The most prevalent family is the TcpA_12 (n = 32), as previously described for streptococcal IMEs  [16]. The VirD4 proteins identified in this work (n = 12) all belong to the IME_PF13814 family. The association of a VirD4 protein with a Rel_PF13814 was already described in IME mobilization modules  [16]. However, one Rel_PF13814 is not associated with a CP, which is unusual. TcpA_6 (n = 1) TcpA_14 (n = 1) none (n = 1) TcpA_12 (n = 4) IME_ PF01719-PF0910 (IME_class_4) Rel_PHA00330_1 (n = 3) TcpA_15 (n = 1) none (n = 2) IME_PHA00330 (IME_class_3) Rel_PHA00330_2 (n = 2) TcpA_8 (n = 2) Rel_PHA00330_3 (n = 1) TcpA_13 (n = 1) Rel_PF13814 (n = 1) none (n = 1) IME_PF13814 (IME_class_8) Serine (n = 12) Rel_PF13814 (n = 12) VirD4 (n = 12) In bold, new families of relaxases and CPs.
Diversity of Integrase-Relaxase-CP Combinations within IMEs The analysis of the co-occurrence of the superfamilies and specificity of integrases, families of CPs and families of relaxases did not reveal exclusive associations, see Table  1  and Figure  2. Altogether, 21 different ternary associations were observed, suggesting a high frequency of shuffling between signature proteins. Among these associations, five have never been observed before: those involving the three newly identified superfamilies of CPs (TcpA_13; TcpA_14 and TcpA_15), those involving the newly discovered Rel_PF01719-PF0910_5 family of relaxases associated with CPs of the TcpA_12 family and those from the IME_PF13814 superfamily that are composed of a Rel_PF13814 associated with a tyrosine integrase in the absence of CP.
Function Encoded by the Cargo Genes In addition to functions that are essential for their mobility, almost all conjugative and mobilizable elements carry cargo genes, i. e. , genes not involved in the transfer of the element. In this work, blast analysis against diverse databases (AMRFinderPlus, BACTIBASE, VFDB, REBASE, NORINE and MEROPS) were undertaken to identify cargo genes encoded by S. salivarius elements. This allowed assigning the biological function of 667 potential cargo genes Figure  3. Figure  S2  lists the elements carrying cargo genes and indicates their presumed function. The most frequent function was the one corresponding to "Signal transduction and regulatory system" that includes 230 genes of which 168 encode transcriptional regulators. This number is overestimated since it is not possible to precisely distinguish cargo regulatory genes from those dedicated to the control of ICEs and IMEs transfer.
salivarius. Among the S. salivarius set of genomes, 13 have been initially screened and selected because they carry members of the ICESt3 family  [32]. However, ICESt3-related elements are also present in many other genomes (25 ICEs or dICEs in 62 strains), showing the high prevalence of this ICE family in this species. The complete genomes retrieved from Genbank that were analyzed in this study include many strains that have been initially sequenced and studied because they are resistant to antibiotics. Therefore, since Tn916-related ICEs from streptococci confer resistance to antibiotics, the high frequency of the Tn916 family found in this work is probably overestimated. Our analysis also revealed ICEs or dICEs belonging to two other families, TnGBS2 family (n = 13) and Tn1549 family (n = 2), that have not been previously reported in S.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4425/11/9/999/s1, Table  S1 : Date and origin of Streptococcus salivarius sampling and genbank accession number. Table  S2 : Coordinates of Streptococcus salivarius integrative elements transferable by conjugation. Table  S3 : Diversity and abundance of Streptococcus salivarius integrative elements transferable by conjugation.
" The box is split in two (or more) when two elements (or more) of the same superfamily are present at distinct integration sites. If several elements of the same superfamily are present at the same integration site, the number of elements is indicated in the box (for example, two IMEs with a PF02486 domain in strain T93). The colored boxes indicate the genes where the elements are inserted. The medium-grey boxes materialize low-specific integration (NS).