dataesr/scientific-paragraphs-categorization huggingface dataset:
VNTR allele coding convention We have used the numeric coding convention for the 8 loci constituting MLVA8 proposed by Keim et al. [6]  except for vrrC1. vrrC1 was initially considered as a 36 bp repeat unit, but it varies in a more complex way. Lista et al. proposed to consider vrrC1 as a 9 bp repeat unit VNTR, and assigned a 53 U allele code to the Ames ancestor genome accession number NC_007530. 2. We have used the Lista et al. proposal  [11]  for the additional 17 loci constituting MLVA25 and Antwerpen et al. [26]  for the additional 6 loci constituting MLVA31 (including MLVA15 by Van Ert et al. [8] ; see Data S1 for more details on the coding conventions and comparison with alternative published coding conventions). Datasets using different conventions can be adjusted as far as the coding convention applied in silico to a sequenced genome is indicated.
Discriminatory power The discriminatory power was calculated by using Simpson's diversity index (DI)  [34]. A DI-value of 1 indicates that the typing method is able to discriminate between all isolates. A DI-value of 0 indicates that all isolates are identical. The discriminatory power of individual loci was calculated on a dataset comprising only one strain per MLVA31 genotype. The discriminatory power of the different MLVA panels was calculated on the full dataset.
Data analysis MLVA clustering was performed using the BioNumerics software package version 6. 6 (Applied-Maths, Sint-Martens-Latem, Belgium). Data were analysed as a character dataset using the categorical distance coefficient. Clustering was achieved either with UPGMA (Unweighted Pair Group Method with Arithmetic mean) or Minimum Spanning Tree (MST). The priority rule for constructing MST was set so that the type that had the highest number of single-locus variants (SLVs) would be linked first. A cutoff value of 85% of similarity was applied to define clonal complexes (CC). Congruence between different experiments was measured by BioNumerics using the categorical distance coefficient. Linkage disequilibrium was measured by using LIAN version 3. 5 software (http://guanine. evolbio. mpg. de/) as described by Haubold & Hudson  [35].
The linkage disequilibrium analysis for each MLVA panel was conducted on a dataset comprising one strain per MLVA genotype. The Monte-Carlo simulation was run with 10000 iterations. The contribution of each marker to the global standardized index of association (I A S ) was calculated as the difference between the MLVA31 I A S and the MLVA30 I A S without this marker. Suggestions for optimized panels were determined using the Automated Selection of Typing Target Subsets (AuSeTTS) Analysis  [36].
Online database MLVA31 data generated in this study can be accessed in the ''Bacillus anthracis'' database at MLVAbank http://mlva. u-psud. fr/. MLVAbank is a demo project first described by Le Fleche et al. , 2002  [37]  and the previous major modifications were described by  Grissa et al. , 2008 [29]. The 2008 MLVAbank version allowed the making by registered users of private or public MLVA databases (registration is free). Unregistered users can query public databases. For the present project, three major functionalities have been added. Firstly MLVAbank can now store any kind of genotyping data, including multiple locus sequence typing (MLST), SNPs, and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived data  [38]. Secondly, different databases can be declared as being part of a cooperative database, after a number of conventions have been agreed upon, regarding field names, genotyping markers names, allele coding conventions.
Thirdly, an in silico typing tool has been included, which can deduce MLVA and SNP genotypes from whole genome sequence data. The application is coded using Javascript. The input is PCR primers information (for MLVA typing) or SNP positions. A tutorial is available via the MLVAbank web page, and an overview is presented as File S3. MLVAbank provides minimum dendrogram tracing tools, based on the categorical distance coefficient and UPGMA method. In addition MEGA format distance matrix or Newick format tree text files can be exported for analysis with the MEGA software  [39]  and FigTree [http://tree. bio. ed. ac. uk] respectively.
Individual VNTR markers evaluation The diversity index of each VNTR marker and contribution to the standardized index of association (I A S ) was calculated on a dataset using one strain per MLVA31 genotype (Data S2 and Table  2 ). Three panels of markers can be distinguished (Figure  3 ). Panel A contains the 18 loci with positive I A S contribution. Diversity indexes of these loci vary from 0. 30 to 0. 77. Panel B includes six markers (vrrA, bams05, bams13, bams15, bams30, bams31) with a similar diversity range but a negative contribution to I A S. Panel C is composed of seven markers showing negative contribution to I A S and low diversity, comprising three monomorphic markers (bams21, bams25 and bams28) in addition to vrrB2, bams24, bams44 and vntr19.
Beyer et al. have published an extensive description of B. anthracis genetic diversity in Namibia as assayed by MLVA31 analysis  [16]. The global diversity index of the assay calculated on the full dataset is recalled in Table  2 , together with individual loci diversity calculated on a dataset comprising one strain per genotype. The situation in France and Namibia is similar with few canSNP lineages represented. Figure  5  illustrates the behavior of the different VNTR loci in both countries. Whereas some loci are similarly variable in both datasets (e. g. pXO1, pXO2, bams05, bams13 etc), others behave strikingly differently. Usually the diversity of this second group of VNTRs is higher in the French dataset, but this however simply reflects the presence of both cluster A and cluster B strains in the French dataset (Data S2).
The diversity index DI of each MLVA scheme calculated on both collections of strains and the standardized index of linkage association calculated keeping one strain per genotype are given in Table  2. The non-null standardized index of linkage association is in agreement with the clonal structure of the B. anthracis population. Direct comparison of the different MLVA schemes showed a strong correlation between the clustering methods, as illustrated by the congruence analysis in Figure  6a  for the ''France'' dataset. The congruence between MLVA31 and the MLVA25 scheme was 99. 7% for the set of 130 strains typed. The congruence between MLVA31 and MLVA15 was slightly lower (96. 1%) and much better than that determined with MLVA8 (85. 5%). Interestingly MLVA7 demonstrated a discriminatory power (0.
8584), I A S value (0. 2323) and congruence with MLVA31 (96. 5%) higher than both MLVA8 and MLVA15. The corresponding MLVA panels DI values deduced from the ''Namibia'' MLVA31 dataset  [16]  are indicated in Table  2. The global MLVA31 is significantly lower in the Namibia dataset as compared to the French dataset, but this might simply reflect a more extensive sampling in a geographically more focused area. The MLVA8 and MLVA15 DIs are almost as high, and this is largely due to the pXO1 and pXO2 loci. In comparison, MLVA7 has a much lower discriminatory power. Sixteen genotypes are resolved by MLVA7 (in comparison to 24 obtained with MLVA8 and MLVA15, and 38 with MLVA31). The maximum of 18 genotypes achievable with the 16 agarose-friendly loci would be reached by substituting bams21 to bams34, bams53 or vntr23.
Making of the MLVAbank Bacillus anthracis database The B. anthracis database at http://mlva. u-psud. fr was updated taking advantage of the new version of the MLVAbank software, which allows the making of cooperative databases, the integration of SNP data, and the in silico analysis of whole genome sequence (Data S3). The current version includes a ''B_anthracis_in_silico'', ''B_anthracis_2012'', ''B_anthracis_2013'' and ''B_anthracis_2014'' components. B_anthracis_in_silico was deduced from the in silico MLVA and SNP typing of the seven fully sequenced strains released to date. It also includes Bacillus cereus and Bacillus thuringiensis genomes showing an average sequence similarity with B. anthracis above 97%. B_anthracis_2012 contains MLVA data recovered from articles on B. anthracis genotyping published up to year 2012, together with the associated SNP data.
The second one is forensic microbiology, where traces of potentially degraded DNA must be analysed. In both contexts, targeted characterization of previously characterized polymorphic loci (VNTRs or SNPs) by PCR amplification might remain an essential tool. In the present work, the diversity of French B. anthracis was analysed in detail with MLVA31. The 130 strains were resolved into 35 different genotypes and four clonal complexes. This is very similar to the situation reported previously in Namibia  [16]. The discrimination power achieved by MLVA31 was clearly improved compared to the MLVA8 assay. Interestingly the four VNTRs coding for key components of the B. anthracis exosporium (bams13, bams15, bams30 and bams31  [43] ) are among the most diverse loci in both MLVA31 datasets currently available (France and Namibia).
We tried next to optimize at least for the present collection of strains the number of VNTR loci required to genotype with useful resolution and accuracy the French diversity of strains and designed a shortened MLVA scheme. Based on the individual marker evaluation performed in this study, the more informative VNTR markers suitable for agarose gel-based analysis could be identified. This panel contains seven loci and can be typed either by monoplex PCR and agarose gel electrophoresis or by multiplexing in a single PCR followed by capillary gel electrophoresis as previously illustrated for B. anthracis and other species  [11, 44, 45]. The MLVA7 assay is similarly efficient in both datasets, an excellent correlation between MLVA7 and MLVA31 results was observed. Despite its lower discriminatory power compared to MLVA31 the MLVA7 scheme appears as a good alternative for typing B.
anthracis whenever capillary electrophoresis technology is not available or a quick genotype analysis must be performed. The proposed MLVA7 panel correctly identifies the main features of the population structure of the two distinct populations investigated here. For higher discriminatory power, two additional panels could be used, each one fitting in one multiplex PCR: the ''exosporium loci'' panel including bams13, bams15, bams30 and bams31 and the ''plasmid loci'' panel including pXO1, pXO2, vntr16, vntr17. MLVA31 data generated in this study were deposited in the ''Bacillus anthracis'' database which can be accessed at http://mlva. u-psud. fr/. This is an open and collaborative database on B. anthracis that has been created to share all available MLVA (and SNP) typing data in a unique database for comparison testing and epidemiological studies.
A VNTR and SNPs search tool is also provided to determine in silico the genotype of any strain based on its whole genome sequence. In conclusion, the work undertaken in the present study updates the genetic landscape of B. anthracis diversity in France from previous publications  [17, 40]  and provides extended datasets about autochthonous strains that can be used for future epidemiological, epizootiological or preliminary forensic studies so that hypotheses can be made about strains origin. We expect that we have achieved now a correct coverage of the genetic diversity of B. anthracis naturally occurring in France and that the small assay which has been devised using this data can be confidently used as a routine, first line assay for typing new French isolates. This low-cost first line assay will be sufficient to recognize an abnormal exotic isolate.
The second line assay in this strategy will be whole genome SNP analysis, as can be done with currently available massively parallel sequencing technologies. Formal forensics investigations, as would be undertaken in the case of a bioterror event, will rely upon single nucleotide polymorphisms assayed via whole genome draft sequencing or large-scale SNP typing, rather than tandem repeats polymorphisms  [46, 47]. However, we believe that routine genotyping, useful for disease surveillance, or daily strain collection monitoring and quality checking, will still benefit from a very low cost assay such as MLVA analysis, especially if it can be run on agarose gels after simple PCR amplifications as well as be deduced in silico from future generations of massively parallel sequence data. The online database described in this study may provide the seed for the establishment of a larger collaborative aggregation of databases from other research groups working on genotyping of B.
Acknowledgments The authors thank  Benoit Durand  for his help in Figures 1b and2 and  Yolande Hauck  for help with MLVA typing. We thank  Christine Pourcel  and  Yolande Hauck  for their contributions to an early phase of this project, including the training of ND into MLVA typing. We are grateful to the  French departmental veterinary laboratories  for providing samples and clinical isolates.
IMGT R comprises 7 databases, 17 online tools and 15 000 pages of web resources, and provides a high-quality and integrated system for analysis of the genomic and expressed IG and TR repertoire of the adaptive immune responses, including NGS high-throughput data. Tools and databases are used in basic, veterinary and medical research, in clinical applications (mutation analysis in leukemia and lymphoma) and in antibody engineering and humanization. The IMGT/mAb-DB interface was developed for therapeutic antibodies and fusion proteins for immunologi-cal applications (FPIA). IMGT R is freely available at http://www. imgt. org.
INTRODUCTION IMGT R , the international ImMunoGeneTics information system R (http://www. imgt. org) (1) , was created in 1989 by Marie-Paule Lefranc at Montpellier, France (Université de Montpellier and CNRS). The founding of IMGT R marked the advent of immunoinformatics, a new science, which emerged at the interface between immunogenetics and bioinformatics  (2). For the first time, immunoglobulin (IG) or antibody and T cell receptor (TR) variable (V), diversity (D), joining (J) and constant (C) genes were officially recognized as 'genes' as well as the conventional genes  (3) (4) (5) (6). This major breakthrough allowed genes and data of the complex and highly diversified adaptive immune responses to be managed in genomic databases and tools. IMGT R manages the diversity and complexity of the IG and TR genes and proteins and the polymorphism of the major histocompatibility (MH) proteins of humans and other vertebrates.
IMGT R is also specialized in the other proteins of the immunoglobulin superfamily (IgSF) and MH superfamily (MhSF) and related proteins of the immune system (RPI) of vertebrates and invertebrates  (1). IMGT R provides a common access to standardized data from genome, proteome, genetics, two-dimensional (2D) and three-dimensional (3D) structures. IMGT R is the acknowledged high-quality integrated knowledge resource in immunogenetics for exploring immune functional genomics. IMGT R comprises 7 databases  (7) (8) (9) (10) (11) (12) , 17 online tools  (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28)  and more than 15 000 pages of web resources (Figure  1 ). Databases include IMGT/LIGM-DB D414 Nucleic Acids Research, 2015, Vol. 43, Database issue (175 898 entries from 346 species)  (7) , IMGT/CLL-DB and IMGT/PRIMER-DB for nucleotide sequences and their translation, IMGT/GENE-DB (3431 genes, 5079 alleles)  (8)  for genes, IMGT/3Dstructure-DB and IMGT/2Dstructure-DB (3682 entries)  (9) (10) (11)  for structures and IMGT/mAb-DB (488 entries)  (12)  for therapeutic antibodies and fusion proteins for immunological applications (FPIA).
IMGT-ONTOLOGY The accuracy and the consistency of the IMGT R data are based on IMGT-ONTOLOGY  (41) (42) (43) , the first, and so far, unique ontology for immunogenetics and immunoinformatics. IMGT-ONTOLOGY manages the immunogenetics knowledge through diverse facets that rely on seven axioms: IDENTIFICATION, DESCRIPTION, CLASSIFI-CATION, NUMEROTATION, LOCALIZATION, ORI-ENTATION and OBTENTION  (42). The concepts generated from these axioms led to the elaboration of the IMGT R standards that constitute the IMGT Scientific chart: e. g. IMGT R standardized keywords (IDENTI-FICATION axiom and concepts of identification)  (44) , IMGT R standardized labels (DESCRIPTION axiom and concepts of description)  (45) , IMGT R standardized gene and allele nomenclature (CLASSIFICATION axiom and concepts of classification)  (46) , IMGT unique numbering  (47) (48) (49) (50) (51) (52)  and its standardized graphical 2D representation or IMGT Colliers de Perles (53-57) (NUMEROTATION axiom and concepts of numerotation).
IDENTIFICATION: IMGT R standardized keywords More than 325 IMGT R standardized keywords (189 for sequences and 137 for 3D structures) were precisely defined  (44). They represent the controlled vocabulary assigned during the annotation process and allow standardized search criteria for querying the IMGT R databases and for the extraction of sequences and 3D structures. They have been entered in BioPortal at the National Center for Biomedical Ontology (NCBO) in 2010 (http://bioportal. bioontology. org/ontologies/IMGT-ONTOLOGY). Standardized keywords assigned to a nucleotide sequence are found in the 'DE' (definition) and 'KW' (keyword) lines of the IMGT/LIGM-DB files  (7). They characterize for instance the gene type, the configuration type and the functionality type  (44). There are six gene types: variable (V), diversity (D), joining (J), constant (C), conventional-withleader, conventional-without-leader.
DESCRIPTION: IMGT R standardized labels More than 560 IMGT R standardized labels (277 for sequences and 285 for 3D structures) were precisely defined  (45). They are written in capital letters (no plural) to be recognizable without creating new terms. These labels are necessary for a standardized description of the IG, TR and MH sequences and structures in databases and tools  (45). Standardized labels assigned to the description of sequences are found in the 'FT' (feature) lines of the IMGT/LIGM-DB files  (7). Querying these labels represent a big plus compared to the generalist databases (GenBank/European Nucleotide Archive/DNA Data Bank of Japan). Thus, it is possible to query for the 'CDR3-IMGT' of the human rearranged productive sequences of IG-Heavy-Gamma (e. g. 1788 CDR3-IMGT obtained, with their sequences at the nucleotide or AA level).
CLASSIFICATION: IMGT R standardized genes and alleles The IMGT-ONTOLOGY CLASSIFICATION axiom was the trigger of immunoinformatics' birth  (2). The IMGT R concepts of classification allowed, for the first time, to classify the antigen receptor genes (IG and TR) for any locus (e. g. IG heavy (IGH), TR alpha (TRA)), for any gene configuration (germline, undefined or rearranged) and for any species (from fishes to humans)  (3) (4) (5) (6). In higher vertebrates, there are seven IG and TR major loci (other loci correspond to chromosomal orphon sets, genes of which are orphons, not used in the IG or TR chain synthesis)  (3, 4). The IG major loci include the IGH, and for the light chains, the IG kappa (IGK) and the IG lambda (IGL) D416 Nucleic Acids Research, 2015, Vol. 43, Database issue in higher vertebrates (3) and the IG iota (IGI) in fishes (IMGT R http://www.
INTERACTION BETWEEN IMGT R DATABASES AND TOOLS IMGT R comprises 7 databases and 17 online tools for sequences, genes and structures which have been de-scribed in details, previously. Links to documentation, releases and statistics are available from the IMGT R Home page, http://www. imgt. org. Here, we will focus mainly on examples demonstrating the strong interactions which exist between IMGT R databases and tools, based on the IMGT R rules and standards, generated from the IMGT-ONTOLOGY axioms and concepts and described in the IMGT Scientific chart. First, we will describe briefly the IMGT R reference directory databases that support the most popular tools for sequence analysis, then we will describe how the IMGT/3Dstructure-DB and IMGT/2Dstructure-DB databases are intimately associated with tool functionalities/results.
IMGT R reference directory databases IMGT/V-QUEST reference directory. IMGT/V-QUEST  (13) (14) (15) (16) (17) (18)  and its high-throughput version, IMGT/HighV-QUEST  (18, 23, 24)  analyse nucleotide sequences of the IG and TR variable domains. These tools run against the IMGT/V-QUEST reference directory database (Table  1 ) that includes several sets. These sets comprise IMGT reference sequences from all functional (F) genes and alleles, all ORF and all in-frame pseudogenes (P) alleles from IMGT/GENE-DB  (8). By definition, the IMGT reference directory sets contain one sequence for each allele. By default, the user sequences are compared with all genes and alleles. However, the IMGT/V-QUEST and IMGT/HighV-QUEST option 'With allele *01 only' can be useful if the user sequences need to be compared with different genes or if the user sequences that use the same gene need to be aligned together (independently of the allelic polymorphism)  (13) (14) (15) (16) (17) (18).
To answer this higher-resolution analysis, additional labels (3 V-REGION, 5 J-REGION) and corresponding reference directory sets had to be created. IMGT/DomainSeq reference directory. IMGT/Domain GapAlign  (10, 25, 26)  analyses AA sequences of the V, C and G domains. This tool run against the IMGT/DomainSeq reference directory database (Table  1 ) that include sets for the V, C and G domains. These sets comprise sequences  from the IMGT Repertoire (1) and from IMGT/GENE-DB  (8). Owing to the particularities of the IG and TR V-DOMAIN synthesis  (3, 4)  there is no V-DOMAIN in the IMGT/DomainSeq reference directory. Instead, the directory comprises the translation of the IG and TR germline V and J genes (V-REGION and J-REGION, respectively). The IMGT/DomainSeq reference directory provides the IMGT R 'gene' and 'allele' names.
Data are comprehensive for human and mouse IG and TR, whereas for other species and other IgSF and MhSF they are added progressively. The IMGT/DomainSeq reference directory comprises domain sequences of functional (F), ORF and inframe pseudogene (P) genes. As IMGT R alleles are characterized at the nucleotide level, identical sequences at the AA level may therefore correspond to different alleles, in the IMGT/DomainSeq reference directory. The sequences of the IMGT/DomainSeq reference directory sets can be dis-played by querying IMGT/DomainDisplay (http://www. imgt. org).
IMGT structure databases IMGT/3Dstructure-DB. IMGT/3Dstructure-DB  (9) (10) (11) , the IMGT R structure database, provides IMGT R annotation and contact analysis of IG, TR, MH, IgSF and MhSF 3D structures, and paratope/epitope description of IG/antigen  (32, 33, 35, (63) (64) (65) (66)  and TR/pMH  (67, 68)  complexes (Table  2 ). There is one 'IMGT/3Dstructure-DB card' per IMGT/3Dstructure-DB entry and this card provides access to all data related to that entry. The 'Protein Data Bank (PDB) code' (4 letters and/or numbers, e. g. 1n0x) is used as 'IMGT entry ID' for the 3D structures obtained from the Research Collaboratory for Structural Bioinformatics PDB  (69)    QuickPDB', 'Renumbered IMGT files', 'IMGT numbering comparison', 'References and links', 'Printable card'  (9) (10) (11). The 'Chain details' section comprises information first on the chain itself, then per domain  (9) (10) (11).