dataesr/scientific-paragraphs-categorization huggingface dataset:
Only genera found in at least 70% of the normal TSAT or in 70% of the high TSAT samples were represented. Four similarity measures were calculated: the Bray Curtis and Kullback-Leibler non-parametric dissimilarity indices and the Pearson and Spearman rank correlations. The threshold for the similarity measures was set at 0. 5, and only edges with merged p-values < 0. 05 (Benjamini-Hochberg correction) were kept. Genera from the co-occurrence networks that had an average relative abundance ≥0. 5% were used to generate correlation matrices (calculated using Spearman's test and plotted using the 'corrplot' package for R  79  ) between the genera relative abundance and bioclinical parameters with both excess iron and periodontitis treated as continuous variables. Additional correlations between species level taxa and clinical data were also computed with Spearman's test.
Acknowledgements The authors are indebted to the medical and nursing staff of the  Liver Unit  for participating in the recruitment and follow-up of patients. We thank the  GeT-PlaGe facility  and the  NED team  for sequencing the samples. We also thank  Adina Pascu  for her contribution to this work and the  "Direction de la Recherche et de l'Innovation" at the University hospital of Rennes  for their support. This study was funded by the  "Comité de la Recherche Clinique" of the University Hospital of Rennes  ( 2009-A00998-49 ) and by the  University of Rennes 1.
Availability of Data and Materials The sequence data were submitted to the NCBI Sequence Read Archive (https://www. ncbi. nlm. nih. gov/Traces/ study/) under BioProject accession number PRJNA416501. Due to the low sequence counts, the Hemoparo37 and Hemoparo43 samples were withdrawn from the study. The metadata and datasets (OTU tables) supporting the conclusions of this article are included within the article as additional files (see Supplementary Tables  S3  and S4 , respectively).
In recent years, massively parallel sequencing has almost entirely been conducted using Illumina's MiSeq and HiSeq platforms  [17, 18]. Prior to these developments, most whole-genome sequencing (WGS) projects were conducted on organisms that were selected due to their relevance to medicine or biotechnology  [19] , resulting in a strongly biased portrait of microbial diversity  [20]. Researchers such as those involved in the Genomic Encyclopedia of Bacteria and Archaea (GEBA) are currently attempting to compensate for this by sequencing at least one species from each known genus  [19]. WGS generates primary information and a catalog of reference genomes. The associated biological information is typically stored in online databases and used for downstream purposes such as comparative genomics, transcriptomics, and proteomics  [16, [21] [22] [23].
The third strategy is based on genome maps  [38, 39]. These methodologies incur additional cost and require significant time investments. In fact, in a WGS project, assembly finishing comprises over 95% of the total cost and timeframe. Therefore, researchers often decide that finishing is not cost-effective, preferring to publish the draft genomes, which abound in the databases  [40]. The accumulation of numerous draft genomes creates massive databases; unfortunately, the quality of the genomes is decreased since it is based on incomplete and/or incorrect genomic data  [41, 42]. Indeed, draft genome assemblies are not sufficient for studying large-scale genome architecture  [43] , can result in incomplete or incorrectly annotated genes (e. g. , partitioned annotation of ORFs), and may hinder evolutionary studies  [44, 45]  by leaving significant portions of the genome unclear or inaccurate  [17].
Analysis of the NCBI genome database On 3 April 2017, we accessed all data through 2016 in the NCBI genome database (https://www. ncbi. nlm. nih. gov/ genome/browse). For analysis purposes, we considered the "FCB Bacteroidetes/Chlorobi," "Terrabacteria Firmicutes," "Terrabacteria Actinobacteria," and "All Proteobacteria" subgroups to represent the phyla Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria, respectively. The levels "Complete" and "Chromosome" were considered finished or complete genomes, and "Scaffolds" and "Contigs" were considered draft or incomplete genomes. Analysis was performed using R (v3. 3. 2)  [48]  and in-house parsing scripts in Python (v2. 7. 10). We excluded all entries marked "Candidatus" and entries that did not include full species identification specifying both the genus and the species.
The tree was simplified to the class level according to the NCBI taxonomy. The isolation sources of all complete genomes were identified via the NCBI BioProject and BioSample databases. The sources were classified into three categories: environmental (soil, fresh or marine water, sludge, mud, plants and algae samples), animal (insects, mollusks, fish, birds, cattle, and domestic animals), and human (isolated from various body sites of healthy or sick individuals).
Complete Bacteroidia genomes and genomic repeats Genomes of species of the class Bacteroidia were classified by sequencing status ("Complete" or "Draft") and by year of publication. The number of contigs in draft genomes was analyzed by genus. We retrieved the complete genomes (those having no assembly gaps) of all species identified as having only one chromosome in the NCBI database. If plasmids existed, only the chromosome was analyzed. Species with two or more chromosomes were excluded (Additional file 1: Table  S1 ). For further analysis, the complete genomes of species for which genomic sequences of two or more strains were available were studied; these included the four Bacteroides species B. dorei, B. fragilis, B. ovatus, and B. thetaiotaomicron, Porphyromonas gingivalis, and Tannerella forsythia.
Simulated read assembly For each genome, artificial reads were produced using ART software (v2. 5. 8)  [56]  set for paired-end reads of 250 nucleotides (nt) each, 500 nt insert size, and a coverage of 40; the built-in MiSeq simulation profile (v1) was used. For the P. gingivalis genomes, eleven de novo assemblers were tested using the default parameters: A5miseq (v20160825)  [57] ; CodonCode Aligner (CCA v7. 0. 1); CLC Genomics Workbench (CLC-GW v8. 5. 1); fermi (v1. 1)  [58] ; Geneious (v10. 2. 3)  [50] ; Minia (v2. 0. 3)  [59] ; MIRA (v4. 0. 2)  [60] ; PERGA (v0. 5. 03. 02)  [61] ; SOAPdenovo (v2. 04)  [62] ; SPAdes (v3. 10. 1) [63] ; and Velvet (v1. 2. 10)  [64]. For CCA and Geneious, reads were preassembled with PEAR (v0. 9. 10)  [65]. Where required, KmerGenie (v1. 7016) was used to determine the k-mer length parameter  [66].
After the initial test step, we selected three software packages for assembly of all 24 genomes. The first was A5-miseq, in which we used the default parameters. For Geneious, reads were preassembled with PEAR and then assembled using the default Medium Sensitivity/Fast setting and generating consensus with a 50% strict threshold for calling bases. Finally, SPAdes was used with the default parameters but with the "careful" option and kmer lengths of 21 to 127. For each genome and assembly tool, we rejected contigs of less than 1 Kbp because they are not informative. We assessed the assemblies with QUAST (v4. 5)  [67]. The fidelity of each assembly was evaluated by mapping each set of contigs to the reference genome using Geneious mapper (Medium Sensitivity/ Fast option); the unmapped contigs were rejected, and QUAST was then used again.
Genome annotation and biocuration Genomes were annotated using Prokka (v1. 12-beta)  [71] , Genix online  [72] , and RASTtk (v1. 3. 0) [73]. The annotations for the gene starts/ends, gene names, gene product descriptions, gene status (gene, pseudogene by stop in-frame or frame-shift), EC numbers, and functional descriptions were all manually biocurated  [74]. For this purpose, we performed NCBI BLAST searches  [75]  against non-redundant databases as well as domain searches using the Conserved Domains search tool  [76]. The results were then compared to the precomputed annotations from MicroScope  [77]. The presence of signal peptides was analyzed using the SignalP 4. 1 server  [78]  and SOSUIsignal  [79]. Protein subcellular localization predictions were made using PSORTb (v3. 0)  [80]  and CELLO (v2.
5)  [81]. The outer membrane proteins predicted by these tools were then confirmed using BOMP  [82] , and LipoP (v1. 0)  [83]  and DOLOP  [84]  were used to predict the lipoproteins. Insertion sequence transposases were renamed as per the ISfinder  [85]  nomenclature. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) were identified using CRISPRfinder  [86] , CRISPRDetect  [87] , CRISPI  [88]  webtools and the CRT plug-in  [89]  with Geneious. We predicted genomic islands using Island-Viewer (v4)  [90] , which has precomputed predictions from SIGI-HMM  [91] , IslandPath  [92] , and IslandPick  [93]. Island prediction was also performed using EGID  [94] , which implements AlienHunter  [95] , SIGI-HMM, IslandPath, INDeGenIUS  [96] , and PAI-IDA  [97]. All genomic sequences and the reads used to produce them were deposited in GenBank and in the NCBI Bio-Project database with links to the BioProject accession number PRJNA393092.
Comparative genomics Genome-level comparisons were made with progressive-Mauve  [98]  using the default settings. The same program was used to identify locally collinear blocks (LCBs) and single-nucleotide polymorphisms (SNPs). For each strain, we compared the available annotation in the NCBI database to our manually biocurated annotations feature-by-feature: rRNA; tRNA; tmRNA; ncRNA; regulatory; repeat region; and coding DNA sequence (CDS). The CDS were separated into pseudogenes and "true" coding regions. For the latter category, the CDS/pseudogenes present in both annotation versions were termed "common," and those that were present only in our new version were called "new. " We noted all instances of changes from a CDS to a pseudogene and vice versa, fusion (more than one interval becoming one interval) and separation (one interval becoming more than one interval), and changes in the coding strand.
Results The Bacteroidetes phylum is represented by few genomes, mostly incomplete and with variable numbers of contigs Bacterial genomes represent approximately 85% of the available genomes in the NCBI genome database. Despite the fact that Proteobacteria represents a relatively minor constituent of human microbiota, it is the most sequenced bacterial phylum, even in comparison to the two most abundant phyla, Bacteroidetes and Firmicutes (Additional file 2: Figure  S1a ). Furthermore, the accumulation of incomplete draft bacterial genomes is notable, comprising 85-95% of all entries depending on the phylum (Additional file 2: Figure  S1b ). Within the phylum Bacteroidetes, the classes Bacteroidia and Flavobacteriia constitute 90% of the listed genomes. Flavobacteriia is mostly associated with environmentally obtained samples, particularly with aquatic species such as water and fish pathogens, and rarely (18% of complete genomes) with humans.
Simulated sequencing reveals a correlation between contig and repeat counts To generate the artificial reads, we used ART software. This set of tools mimics the real sequencing process and permits simulation of the data that are produced by massively parallel methods; the simulated data display each technology's inherent empirical errors and profile qualities. For the seven complete P. gingivalis genomes, 11 assemblers were tested. The assembler spectrum was restricted to software that can treat Illumina reads but was otherwise chosen to be as wide as possible. Three of the assemblers are part of commercial software suites (Geneious, CLC Genomics Workbench, and CodonCode Aligner); the others are freely available for academic purposes. Since de Bruijn graphs are inescapable (they are used by A5-miseq, CLC Genomics Workbench, Geneious, Minia, MIRA, SOAPdenovo2, SPAdes, and Velvet), we made an effort to also include different assembly methods, including Overlap-Layout-Consensus (CodonCode Aligner), string graph (fermi), and greedy algorithm (PERGA).
We did not annotate any short repeats (3 to 31 bp) or sequence-tagged sites. Finally, the longest and probably the most important biocuration work involved the CDS and pseudogenes, which we were able to dramatically improve. To facilitate traceability in publications and databases, we kept the initial locus_tags when the DNA sequence was unchanged or very similar (only having a new start and/or stop position). When the differences were larger (e. g. , changes in the coding strand, ORF fusion, or a new CDS), we created new locus_tags marked PGN_n, PGTDC60_n, and PG_n for ATCC 33277, TDC60, and W83, respectively. In the previous versions, the pseudogenes had coding sequences and gene annotations, with pseudogene status annotated in the gene qualifier "note. " These could be classified into the categories "frameshifted," "incomplete," and "internal stop.
" Following the NCBI Prokaryotic Genome Annotation guidelines (https://www. ncbi. nlm. nih. gov/genbank/genomesubmit_ annotation/), we annotated the pseudogenes with the gene feature only (no CDS) and added an asterisk to the gene name field so that pseudogenes could be easily distinguished from normal coding regions. The gene_desc qualifier field describes the function of the pseudogenized gene, and the reason for the pseudogenization ("fragment" or "frameshift") is listed in the note qualifier. Where they are relevant, gene fusions and coding strand changes are also mentioned in the "note" field. For the three strains, our revised annotation contains fewer CDS and pseudogenes than are found in the original annotations (Table  3 ). These differences are essentially due to over-annotation of the original genomes and to genome-to-genome annotation propagation caused by the difficulty of distinguishing between small coding ORFs and Evil Little Fellows or ELFs  [105, 106] ).
Comparison of the original annotations and our comprehensive syntactic re-annotations To compare the original and curated annotations, we began by summarizing the common CDS and genes in the two versions as well as the number of curated start and stop positions and eliminated ELFs (Table  3 ). Additionally, for ATCC 33277, we added 55 new coding sequences; these usually corresponded to the reestablishment of a complete CTnPg1 copy or the reconstruction of complete ISPg CDS copies. For the TDC60 strain, we added 2 new pseudogenes (ISPg) and 6 new CDS (coding for 2 ISPg, an NAP, a peptidase, a   77 ) 1703 (  45 ) 163 (  14 ) 165  (53)  rhodanase, and a PF07877 domain-containing protein). Finally, in the W83 strain, we de novo annotated 6 pseudogenes (5 ISpg and a PF07877 domain-containing protein) and 12 CDS (these encode 7 ISPg, 2 integrases, an NAP, a transcriptional regulator HxlR, and a virulencerelated RhuM protein).
Discussion Bacteroidetes dominate human intestinal bacterial communities  [112] , but these taxa are also associated with other animals and found in soil and aquatic environments. This suggests that they play an important role in biogeochemical processes  [8]. In silico modeling allows us switch from describing microbial communities to actually predicting genotype-phenotype relationships and microbe-microbe and microbe-host interactions, but it requires genomic reference sequences that are both accurate and exhaustive  [113, 114]. The existing databases contain thousands of bacterial genomes, but their quality is poor since nearly 90% of all available bacterial genomes are merely drafts  [40, 115]. Furthermore, the biodiversity of the databases is biased because the data were primarily obtained from pathogenic or biotechnologically/economically interesting bacteria  [116] [117] [118].
As an example, Bacteroidetes, despite their abundance among human microbiota, represent a minority of the species in genome databases, with some overrepresented species such as Bacteroides fragilis. Moreover, even when a reference genome is available for a bacterial species, this is not sufficient to permit evaluation of the true biological diversity of its strains  [119]. Of even more concern, the number of drafts is actually underestimated since some genomes marked "complete" or "finished" are fragmented and have not been fully annotated. For example, we observed (Additional file 16: Table  S1 ) that two of nine Porphyromonas gingivalis genomes described as complete were artificially circularized: HG66  [120]  has an assembly gap of unknown length (represented by 100 N), and JCVI SC001  [121]  contains 282 assembly gaps, abnormalities also noticed by Chen et al.
Our study shows the importance of submitting the sequencing reads to databases such as the NCBI SRA sequence read archive so that they can be reused for further analysis. This allows the scientific community to complete the study of genomes, adding value to the work of the initial researchers. Unfortunately, however, many reads are not publicly available, since it is not mandatory to upload them (even if it is highly recommended). As previously stated, the main reason for sequencing several strains from a single species is comparison of their genomes with the goal of explaining phenotypic differences and understanding the evolutionary history and adaptation of the species. To do this, we compared the three resequenced P. gingivalis strains after performing a thorough manually biocurated annotation.
Availability of data and materials All genomic details (including sequences and isolation information) are publicly available in the appropriate NCBI databases (https://www. ncbi. nlm. nih. gov). The resequenced strains from this study are commercially available from the ATCC (Catalog numbers ATCC® 33277™ and ATCC® BAA308™ for P. gingivalis strains ATCC33277 and W83 respectively) or JCM (Catalog number 19600 for P. gingivalis strain TDC60). Raw sequencing reads and genomes were deposited to the NCBI SRA and Genome databases. The BioProject accession number for this study is PRJNA393092.
Results Three-dimensional structure of GldM. The GldM (accession number GI: 58531935) and PorM periplasmic domains (GI: 188595218) (GldMp, PorMp) were cloned from residues 36-513 and 36-516, respectively. GldMp crystallized readily, and its structure was solved using the Se edge of a SeMet derivative for phasing (Table  1 ). One molecule was present in the asymmetric unit, but strong contacts exist with a symmetry-related protein in the crystal. The assembly of GldMp as a dimer was confirmed because domain swapping and tight locking to the symmetryrelated dimer were observed, which have been previously demonstrated for GldM in solution, as assessed by size exclusion chromatography (SEC). The GldMp dimer is elongated and straight with overall dimensions of ~180 Å × 50 Å × 35 Å (Fig. 1 ). The dimer structure contains four domains, D1-D4 (Fig.
The contribution of PorM domains for contacting PorK and PorN was tested by BACTH. Our results show that the PorM-D4 domain is sufficient for interacting with PorN. By contrast, PorM interaction with PorK requires the D2-D3 and D4 domains, suggesting that either the three domains are required for interaction or that D2-D3mediated dimerization of the D4 domain (monomeric in the isolated form) is necessary to properly interact with PorK (Fig. 5a ). A putative multimer model of a complex. Using the data from Sato et al. 1  , proposing a mass slightly larger than 1. 4 MDa of the (PorKLMN) 2 complex 1 and from Vincent et al. 3  , together with the structures reported here, we speculated on the possible architecture of the T9SS core machinery. We used Symmdock software  18  to identify which part of PorM/GldM might fit together to form a multimer of dimers.
4 MDa 1. We recently reported that PorM, PorN, and PorK form homodimers, whereas PorL forms homotrimers  3. Hence, the stoichiometry of the assembly is expected to be PorL 3 /PorM 2 /PorN 2 -PorK 2 , resulting in an overall mass of ~410 kDa. Therefore, three or four copies of the above-described assembly would be necessary to form the ~1. 4 MDa isolated by Sato et al. 1  In contrast, Gorasia et al. 12  reported data that differ from those reported above. In their report, a P. gingivalis membrane fraction was purified leading to large rings that were analyzed with electron microscopy. These rings, which were attached to the membrane, measure ~50 nm in diameter (35 nm internally) and are formed of 32-36 1:1 PorK:PorN complexes. They did not observe the presence of PorM or PorL. These authors proposed that the native complex therefore contains 32-36 copies.
Crystals were briefly soaked in crystallization solution supplemented with 20% (v/v) propylene glycol before being flash-frozen in a nitrogen gas stream at 100K. Native PorMp 224 diffraction data were collected to 2. 85 Å resolution on beamline ID23-1 at the European Synchrotron Research Facilities (ESRF, Grenoble, France), using a Pilatus detector. PorMp 224 SeMet MAD data were Complete data sets of PorMp 315 /nb130 and PorMp Nt /nb01 were collected at beamline PROXIMA 1 at SOLEIL, Paris, France. Data were integrated and scaled as for PorMp 224. Data collection statistics are reported in Table  1. Initial crystallization trials of GldMp and SeMet GldMp were performed by the sitting-drop vapor-diffusion method at 293K in 96-well Swissci plates using a Mosquito Crystal robot (TTP Labtech) with the following screens: Stura Footprint Screens (Molecular Dimensions), Structure Screen and Structure Screen 2 (Molecular Dimensions), PEGs Suite and PEGs II Suite (Qiagen), JCSG+ Suite (Qiagen), and Index (Hampton Research).
The building of GldMp was then completed manually with COOT  34. Refinement, correction, and validation of the different structures were performed with autoBUSTER  36  , COOT  34  , and Molprobity 37 , respectively. More technical details are provided elsewhere  16, 17. Refinement statistics are reported in Table  1. OM OM Secretion pore N 2 N 2 N 2 N 2 K 2 M 2 L 3 L 3 L 3 L 3 M 2 M 2 M 2 K 2 K 2 K 2 IM IM Symmdock modelization. Symmdock software  18  complex modeling was performed using the straight GldMp structure as input, with both threefold and fourfold symmetry. The two best solutions were found to be close together resulting in a tight-packed N-terminal domain. To note, Symmdock works by maximizing the contact surface between monomers and minimizing the steric clashes. Bacterial two-hybrid.
Acknowledgements We thank members of the  Roussel and Cascales groups  for insightful discussions. We thank the  Soleil (Saint Aubin, France)  and  ESRF (Grenoble, France)  synchrotrons for beam time allocation. This work was supported by the  Centre National de la Recherche Scientifique  and the  Aix-Marseille Université , and grants from the  Agence Nationale de la Recherche  ( ANR-15-CE11-0019-01 ) and the  French Infrastructure for Integrated Structural Biology (FRISBI). J. R. and M. S. V. were supported by doctoral fellowships of the  Ministère Français de l'Enseignement.
Statistics The raw variations (Δ) of the different studied parameters were calculated according to the following formulas: Δ ¼ PI t1 À PI t0 ð Þ : with: PI: Silness and Löe Plaque Index; t0: before brushing; t1: after brushing. The descriptive statistics for quantitative data were computed for each time point, for each surface (lingual and labial) as well as for the change between (t1-t0). To assess the effectiveness of the device after brushing, a paired t-test was applied on the change outcome (t1-t0). The normality assumption was checked with a Shapiro-Wilk test (α = 0. 01). The type I error probability (α) was set at 5% in bilateral mode. The software used was Microsoft Excel® 2010 and SAS® v9. 2.