dataesr/scientific-paragraphs-categorization huggingface dataset:
0 software (Bruker) and analysed against the main spectra of bacteria included in two databases (Bruker as well as Microbes Evolution Phylogeny and Infections (MEPHI), which is constantly updated). No identification was obtained because the strain displayed scores below 1. 7, supporting the suggestion that our isolate was not a member of a known species. The spectrum of strain OS09 T has been added to the local MEPHI database. A dendrogram made with Biotyper 3. 0 software comparing the spectrum of the OS09 strain to those of the other Bartonella species is shown in Fig. 3.
Genome sequencing information Genome project history. The OS09 strain was selected for sequencing on the basis of its phylogenetic position and phenotypic differences with other members of the Bartonellaceae family. This strain was isolated in a study on the role of the soft tick, O. sonrai, as a host of Bartonella  [11]. Currently 29 genomes are available in GenBank database for the genus Bartonella. The genome of strain OS09 T is the first genome of Bartonella massiliensis sp. nov. , and is assembled and deposited under GenBank accession numbers CABFVS010000001 to CABFVS010000091. A summary of the project information is presented in Table  2. Growth conditions and DNA isolation. The OS09 T strain of Bartonella massiliensis (= CSUR B624T = DSM 23169) was cultured on Columbia agar enriched with sheep's blood (bioMérieux) with 5% CO 2 at 32°C.
Genome annotation Open reading frames (ORFs) were predicted using Prodigal  [25]  with default parameters, but predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database using BLASTP and the Clusters of Orthologous Groups (COGs) database using Cognitor  [26]. The prediction of RNA genes, including rRNAs, transfer RNAs and other RNAs, was carried out using the RNAmmer  [27]  and ARAGORN  [28]  algorithms. The transmembrane helices and signal peptides were identified by TMHMM  [29]  and SignalP  [30]  respectively.
Genome properties The genome is 2 227 694 bp long with 37. 76 mol% GC content (Table  3 , Fig. 5 ). It is composed of 91 contigs. Of the 1967 predicted genes, 1925 were protein-coding genes and 42 were RNAs (including one 16S rRNA, one 23S rRNA, one 5S rRNA and 39 transfer RNA genes). A total of 1309 genes (68%) were assigned a putative function (by COGs or NR BLAST). A total of 111 genes were identified as ORFans (5. 77%). The remaining genes (n = 386) were annotated as hypothetical proteins (20. 05%). The distribution of genes into COGs functional categories is presented in Table  4. The properties and statistical information of the genome are summarized in Tables  3  and 4. The degree of genomic similarity of OS09 T closely related species was estimated by OrthoANI software  [31]. Values among closely related species ranged from 81.
Nucleotide sequence accession number The 16S rRNA, ITS, ftsZ, rpoB and gltA gene sequences and genome sequences of Bartonella massiliensis sp. nov. , strain OS09 T , are deposited in GenBank under accession numbers HM636440, HM636449, HM636443, HM636452 and HM636446 and CABFVS010000001 to CABFVS010000091 respectively.
Acknowledgements The authors thank  C. Robert  for sequencing the genome,  A. Caputo  for submitting the genomic sequence to GenBank and  C. Couderc  for generating the MALDI-TOF MS reference spectrum. Supported in part by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection  and the  French National Research Agency  under the  programme 'Investissements d'avenir,' reference   ANR-10-IAHU-03.
44 Nucleotide transport and metabolism H 58 3. 01 Coenzyme transport and metabolism I 41 2. 13 Lipid transport and metabolism P 84 4. 36 Inorganic ion transport and metabolism Q 15 0. 78 Secondary metabolites biosynthesis, transport and catabolism R 209 10. 86 General function prediction only S 119 6. 18 Function unknown - 563 29. 25 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome. 6 New Microbes and New Infections, Volume 32 Number C, ---2019 NMNI © 2019 The Authors. Published by Elsevier Ltd, NMNI, 32, 100596 This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4. 0/).
With the success of the model ligations, which showed that the hdmb auxiliary could promote efficient ligation on a Lys side-chain even below pH 7, we addressed our goal, the ubiquitylation of mouse NEMO CoZi domain (NEMO CoZi )(PDB ID:3F89). Lys 302 corresponding to human NEMO Lys 309 has been identified as a position that is linearly ubiquitylated  38  and also the target of bacterial ligase activity to switch off NEMO  39. The structure of (NEMO CoZi )(PDB ID:3F89) as a dimer has been reported and was chosen as an ideal target for synthesis because of its size  40. The 83-mer NEMO CoZi was synthesized in a single piece with Lys 302 side-chain protected with Alloc and an N-terminal biotin. After Alloc removal (Supplementary Fig. 5 ), the hdmb salicylaldehyde 3 was added and reductive amination, cleavage and Mob removal performed satisfactorily as established previously for the model peptide (Supplementary Figs.
Folding and di-ubiquitin chain binding of NEMO CoZi derivatives The folding of synthetic NEMO CoZi derivatives was studied by circular dichroism. NEMO CoZi displayed far-UV CD spectra characteristic of helical secondary structure (Fig. 6a ), which converts to a random coil at elevated temperature (Fig. 6b ). To demonstrate that the α-helical structure of NEMO CoZi coils together to form a dimer we determined the melting point of the protein at different concentration (Fig. 6c ). The melting temperature is clearly shifting with increasing concentrations of NEMO CoZi , indicating that synthetic biotinylated NEMO CoZi forms a coiled-coil dimer consistent with the crystal structure derived from bacterially expressed NEMO CoZi (PDB: ID 3F89)  3, 40. We also determined CD spectra of synthetic ubiquitylated NEMO CoZi , which displays additional secondary structure to NEMO CoZi alone (Fig.
6a ). The CD spectra of NEMOCoZi-Ub closely resembles the sum of the two separate spectra of NEMO CoZi and ubiquitin, indicating that both parts, the NEMO coiled-coil as well as the ubiquitin are properly folded. Altogether, the circular dichroism data strongly suggest that our synthetic NEMO CoZi derivatives adopt the native fold of their endogenous counterparts. NEMO CoZi domain is not only a substrate for ubiquitylation, but also binds linear Ub chains with high affinity  3, 40. It is believed that linear Ub binding of NEMO is required for recruitment of IKK into the TNF receptor-signalling complex (TNFRSC), which results in ubiquitylation of NEMO by LUBAC and triggers subsequent kinase activation of IKK. However, little is known about the impact of NEMO linear ubiquitylation on non-covalent complex formation.
LC-MSMS analysis of NEMO CoZi -Ub NEMO CoZi -Ub was resuspended in 50 mM NH 4 HCO 3 and digested o/n using trypsin. Digested peptides were dried using a centrifugal SpeedVac and resuspended in MS solvent (3% CH 3 CN, 0. 1% TFA). Tryptic peptides were analysed on a Q Exactive coupled to an RSLCnano3000 (Thermo Scientific). Peptides were resolved using a 50 cm EASYspray column with a gradient rising from 3 to 40% CH 3 CN over 23 min. MS spectra were acquired from m/z 300-1500 with peptides selected for fragmentation in a DDA fashion. MSMS spectra were acquired at 17,500 fwhm. Raw files were processed in PEAKS studio using the PEAKS search engine. Data was searched against a database consisting of the synthetic NEMO sequence in an E. coli K12 database and a database of common contaminants. A PEAKS PTM search was performed with confident modification sites requiring a minimum ion intensity of 5% (Supplementary Fig 8).
E. Geffard et al. selective pressure, genetic drift, non-random mating, recombination events, and shared genetic effect between alleles  (Goodin et al. , 2018; Ahmad et al. , 2003). HLA typing techniques have considerably evolved over the years with a wide array of methods providing increasing levels of resolution  (Erlich, 2012 , Table  1 ). Historically, phenotyping was performed by detecting HLA proteins on cell surface with specific antibodies. These serologybased methods have progressively been replaced with DNA-based typing. Today, full-length HLA genes sequenced through NGS (Next-Generation Sequencing) provides the highest standard and resolution. In parallel with HLA typing methods, nomenclature has greatly evolved (Table  1 ), which nowadays significantly hampers retrospective analyses and HSCT compatible donor search from archived HLA datasets recorded in low/mid resolutions with possibly some missing genes (e.
Overall, the most commonly used methods to compute HLA haplotypes are EM-based algorithms  (Eberhard et al. , 2013) , which can accommodate several loci with an arbitrary number of alleles for a large number of individuals with ambiguous haplotypes  (Eberhard et al. , 2013; Salem et al. , 2005). However, they show limited performance with small sample size and do not support haplotype determination from a unique individual. Moreover, results are dependent on inherent dataset characteristics: individuals genetic heterogeneity, number of loci, and genotype resolution  (Eberhard et al. , 2013). These methods are not always straightforward or need powerful computation  (Salem et al. , 2005). Previously, a maximum likelihood-based HLA haplotype imputation technique was validated on several datasets for unrelated HSCT donor search  (Gourraud et al.
, 2005). This method computes the most likely haplotype pair from HLA genotypes based on HLA genotypes frequencies throughout donor transplant registries. Most of the reference haplotype frequencies come from the large reference population of the National Marrow Donor Program (NMDP). NMDP designates the US voluntary bone marrow donor registry. This registry has proposed several breakthroughs in the field of bone marrow transplantation by making available the large HLA haplotype database used in the current study, and also, by creating a specific nomenclature: "NMDP codes". These codes allow to describe HLA typing with some allele ambiguity represented by 2 to 5 letter codes (table  1 ). Following this strategy, we developed Easy-HLA, a user-friendly web application designed to deliver a complete suite of HLA annotations (freely available through a secure connection at https://hla.
Implementation Easy-HLA is a web-based application suite designed to predict haplotypes from HLA genotypes. The input HLA genotypes can be entered with low/mid resolution and/or can contain ambiguities, in a single request (one individual genotype) or batch mode (several individuals genotypes). Regarding security and data storage, the loaded data files are deleted immediately after analysis completion, and the output data files are safely conserved on our server for one week. We implemented our tools with web scripting languages using PHP combined with the pgSQL procedural language. The pgSQL language is used to interrogate the haplotype database and find haplotype pairs corresponding to the input genotype. PHP functions were designed to query multiple databases (serological identity, NMDP nomenclature equivalence) to translate the HLA nomenclature complexity.
Database Easy-HLA main algorithm is based on HLA haplotype frequencies from a large reference population, these frequencies were obtained with a maximum likelihood-based HLA haplotype imputation technique previously validated  (Gourraud et al. , 2005). We stored our data in a PostgreSQL database. The core reference haplotype frequencies come from the National Marrow Donor Program (NMDP) published in 2013 for Downloaded from https://academic. oup. com/bioinformatics/advance-article-abstract/doi/10. 1093/bioinformatics/btz875/5637224 by INSERM user on 17 December 2019 uses in clinical transplant and immunological research  (Gragert et al. , 2013). From the HLA genotypes of 6. 59 million US subjects, the NMDP estimated high-resolution HLA haplotypes frequencies in five ancestral populations using an EM algorithm  (Schaid et al.
, 2002). The large sample size allows an accurate estimation of rare alleles and haplotypes frequencies. The NMDP haplotype database thereby reports frequencies of over 600,000 haplotypes divided into 5 ancestral populations  216; Asian and Pacific Islanders: 158, 307; Europeans: 304, 697; Hispanics: 220, 020; 417). We completed this large dataset with RFGM, a French population database containing more than 16,000 haplotypes  (Gourraud et al. , 2015; Pappas et al. , 2015). The user has the possibility to choose the best matching reference population with his/her input individual(s) ancestry among these 6 reference datasets.
Equation 1-Enumeration of diplotypes 𝐺(A a ∼ Bb ∼ Cc) { d1 (A ∼ B ∼ C, a ∼ b ∼ c) d2 (A ∼ b ∼ C, a ∼ B ∼ c) d3 (A ∼ b ∼ c, a ∼ B ∼ C) d4 (A ∼ B ∼ c, a ∼ b ∼ C) Our algorithm is founded on a reference database of HLA haplotype frequencies (f) in different populations: haplotypes not reported in the reference dataset are removed from the haplotype list (a~B~C strikethrough in d3 in equation 1), resulting in n previously observed pairs of haplotypes (here, n=3) and therefore reducing the space of haplotypes to explore. We calculated genotypic frequencies from haplotype frequencies by following Hardy Weinberg's genetic distribution law. When a diplotype is homozygous, the likelihood (L) is the squared value of the haplotype frequency (f2). When the diplotype is heterozygous the likelihood (L) is: Equation 2-Likelihood of an enumerated heterozygous diplotype 𝐿(d1(A ∼ B ∼ C, a ∼ b ∼ c)) = 2 * 𝑓(A ∼ B ∼ C) * 𝑓(a ∼ b ∼ c) Where f(A ∼ B ∼ C) and f(a ∼ b ∼ c) corresponds to the respective frequencies of each haplotype.
The number of homozygous diplotypes is low  (Gragert et al. , 2013) , therefore we consider only the likelihood for heterozygous diplotypes. When HLA genotypes are specified with allelic ambiguities (lowresolution) and/or untyped loci (incomplete genotype), multiple alternative diplotypes can be inferred. For allele ambiguity, the Easy-HLA imputation algorithm produces all possible HLA genotypes associated with the ambiguous input. Correspondingly, when a locus is missing (in our example, the HLA-B gene was not typed and is recorded as XX -see equation 3), Easy-HLA generates all possible alleles for this missing gene (B, b, and β). Equation 3 displays only haplotypes pairs reported in our reference database with a frequency above the user-defined threshold. Indeed, we do not show every possible theoretical haplotype pairs as many are not observed in our population datasets, and would therefore have a null estimated frequency.
Equation 3-Enumeration of diplotypes from an incomplete genotype with a missing locus using all compatible haplotypes present in our database 𝐺(A a ∼ XX ∼ Cc) { d1 (A ∼ B ∼ C, a ∼ b ∼ c) d2 (A ∼ b ∼ C, a ∼ B ∼ c) d3 (A ∼ β ∼ c, a ∼ b ∼ C) d4 (A ∼ β ∼ c, a ∼ β ∼ C) From an incomplete HLA genotype, Easy-HLA algorithm hence produces all possible diplotypes and then computes their corresponding likelihood. For each diplotype, a confidence measurement named postprobability (Post-P) is calculated as the ratio of likelihood of a particular diplotype (L(dt)) among the likelihood of all n possible diplotypes (L(di)): Equation 4-Post-probability of each possible diplotype 𝑃𝑜𝑠𝑡 -𝑃(𝑑𝑡) = 𝐿(dt) ∑ 𝐿(di) 𝑛 𝑖=1 Where i is an index for enumerating the different diplotype, and n is the number of possible diplotypes.
The post-probability of the most likely diplotype is then: Equation 5-Post-probability of the most likely diplotype 𝑃𝑜𝑠𝑡 -𝑃 = max (𝐿(di)) ∑ 𝐿(di) 𝑛 𝑖=1 The diplotype with the highest post-probability is by definition dependent of the haplotypes frequencies in the reference dataset. When interpreting the output, one has to be cautious when top post-probabilities are close, as the real haplotype pair might then not always be the most likely. From the likelihood of each predicted diplotype, Easy-HLA can then infer a high-resolution genotype for the incomplete or ambiguous input genotype. The likelihood (L) of the imputed high-resolution genotype is: Equation 6-Likelihood of the imputed high-resolution genotype 𝐿(𝐺(𝐴 𝑎 ∼ 𝐵𝑏 ∼ 𝐶𝑐)) = ∑ 𝐿( 𝑛 𝑖=1 𝑑𝑖) Where i is an index for enumerating the different diplotypes di, n is the number of possible diplotypes and L is the likelihood of a diplotype obtained from haplotype frequencies f.
E. Geffard et al. genotypes with their likelihood and post-probability starting with the most probable one (Figure  1A ). Optionally, it is possible to select only the most probable high-resolution genotype (in batch mode). When our algorithm does not find a corresponding pair of haplotypes in the reference dataset for a given genotype, it cannot make a prediction and returns an information message: "No matching donor found with your selected criteria". However in the HLA-2-Haplo module, if one reported haplotype corresponds to half the given genotype, the algorithm infers the missing second haplotype to propose a haplotype pair solving the genotype. The HLA-Upgrade module can predict a full 5 loci HLA-A, -B, -C, -DRB1 and -DQB1 genotyping at high-resolution level by statistically resolving missing, low-resolution, or ambiguous typings such as NMDP codes.
By updating HLA genotypes, HLA-Upgrade empowers the analysis of old cohorts or cohorts with a long delay of recruitment (Figure  1B ), for which HLA-C and HLA-DQB1 genes are often missing (only recently added in routine genotyping). The HLA-2-Haplo module predicts the most likely haplotype pair from a given genotype (Figure  1B ) and provides their frequencies in different populations. HLA-2-Haplo can be a particularly useful tool to determine if unrelated individuals have a chance to be haplo-identical in a HSCT clinical setting and to provide supplementary information for research. To solve phasing ambiguity for a given HLA genotype, our algorithm compares the potential haplotype pairs with the previously reported haplotypes stored in our large reference database, and can impute the second haplotype if only one haplotype from the diplotype was previously observed.
HLA-Upgrade validation We validated the HLA-Upgrade module using two independent cohorts of unrelated individuals with high-resolution (second-field) HLA genotyping for HLA-A~B~C~DRB1~DQB1 loci: (1) 1,579 Europeans from the Nantes blood center, and (2) 917 individuals of African ancestry from CAAPA (Consortium on Asthma among African-ancestry Populations in the Americas)  (Barnes et al. , 1996; Mathias et al. , 2016). To evaluate the database exhaustiveness on the presence of haplotypes from both cohorts in the database, we tested HLA genotypes at highresolution from both cohorts in HLA-Upgrade. We found 96. 5% of the European cohort genotypes and 70. 1% of the African-American cohort genotypes represented in the database. From these full high-resolution datasets, we simulated low-resolution HLA genotypes for both cohorts by creating 12 different situations based on 4 resolution levels (first-field, second-field, serology, and NMDP simulated with correspondence table [https://www.
ebi. ac. uk/ipd/imgt/hla/]) and on 3 levels of input loci (HLA-A~B~DRB1, HLA-A~B~C~DRB1, and HLA-A~B~C~DRB1~DQB1). We used HLA-Upgrade to predict full HLA-A~B~C~DRB1~DQB1 highresolution genotypes for each of the 12 simulated datasets, and defined accuracy as the percentage of correct predictions compared to the original HLA typing. We defined call rate as the number of predictions compared to the total number of expected predictions. The resolution level impacts prediction accuracy, prediction is almost twice as good for intermediate-resolution (NMDP) and high-resolution (second-field) genotypes compared to low resolution genotype (serology and first-field) (Figure  S1 ). For the HLA-A~B~C~DRB1~DQB1 genotype, the prediction accuracy was 58. 1%, 58. 6%, 97. 6%, 100% for first-field, serology, NMDP and second-field resolution level respectively.
Acknowledgements The authors thank  Labex IGO  and  IHU-CESTI  for their support. Nicolas Vince  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 846520. dbGaP Study Accession: phs001123. v1. p1. Downloaded from https://academic. oup. com/bioinformatics/advance-article-abstract/doi/10. 1093/bioinformatics/btz875/5637224 by INSERM user on 17 December 2019.
Discussion Easy-HLA is a web application suite designed to facilitate large-scale immunogenetic analyses and gain knowledge from HLA typing, regardless of the variety of nomenclature or typing methods. In this report, we presented two tools, HLA-Upgrade and HLA-2-Haplo, based on a large HLA haplotype reference database. Our tools integrate published external data comprising a published set of haplotype frequencies estimated from bone marrow donor registries (more than 6. 5 million individuals and 600,000 unique haplotypes) in order to facilitate an accurate interpretation of the input dataset. HLA-Upgrade can successfully predict a full HLA-A, B, C, DRB1, and DQB1 genotyping in different populations from low resolution and/or partially known HLA typing. As expected, HLA-Upgrade performance positively correlates with HLA typing input resolution: when there is more uncertainty or missingness in input, prediction will be lower.
The HLA-2-Haplo module accurately predicts haplotype pairs from HLA genotypes. From high-resolution input and a 30% post-probability threshold, we obtained a 99% and 96% call rate and 70% and 76% prediction accuracy for African and European ancestry populations, respectively. Importantly, HLA-2-Haplo systematically outperforms "haplo. stats", a pre-existing HLA haplotyping R package, independently of sample size. Both Easy-HLA inference tools rely on a statistical algorithm based on HLA haplotype frequencies from a large reference database (>600,000 haplotypes from 5 different ancestral populations). One major strength of our strategy is the size and diversity of our reference registry including the biggest published haplotype frequency database from the NMDP  (Gragert et al. , 2013)  and the RFGM databases  (Gourraud et al.
, 2015). However, these databases also convey most of Easy-HLA's limitations: exhaustiveness (96. 5% European and 70. 1% African-American cohort genotypes are present in the database), population diversity coverage (mixed populations (REF)), typing errors, resolution level, and HLA loci coverage. For example, the current haplotype frequency databases do not include HLA-DPB1. Our reference database samples HLA haplotypes from the USA and from France and therefore does not represent exhaustive HLA genetic diversity. However, we believe this bias is mostly compensated by the large size of the database, which allows both an accurate estimate of haplotype frequencies and the presence of many rare haplotypes, overall improving our predictions. Finally, Easy-HLA is flexible and our algorithms are fully compatible to evolve regularly with future database releases.
Here, we developed algorithms implemented in a user-friendly web application to facilitate the analysis and reveal the full details of HLA typing. Easy-HLA tools are of great interest both for biomedical research and clinical applications. First, HLA-Upgrade allows to update archived HLA cohorts recorded in low/mid resolutions and/or with missing loci (such as HLA-C), which empowers the users to combine old and recent datasets to perform large immunogenetic association analyses with various immune-related pathologies. HLA-Upgrade could also be translated into clinics to assess HSCT compatibility between a patient and potential donors with low/mid-resolution. Second, HLA-2-Haplo predicts haplotypes that are the breeding-ground for further research investigations and for functional immunogenomic annotations: HLA-C expression (HLA-expr), amino acid equivalence (HLA-AA), and KIR ligand classification (HLA-KIRlig).