dataesr/scientific-paragraphs-categorization huggingface dataset:
Some software try to fill this gap, such as IgGalaxy. However it relies on software that are not specifically suited to the analysis of high-throughput sequencing (with millions of sequencing reads). Also the user needs to upload the results from IMGT/HighV-QUEST, together with the sequence files, which makes the use of the software less practical. ARResT/Interrogate allows to upload results in tabular form (obtained, for instance, with IMGT/HighV-QUEST), and to display results and statistics. However, it does not provide easy sequence upload and processing, handling of large databases of patients or experiments and storage of user annotations. We present here the Vidjil platform, that is ready for hospital or research lab use. The platform is made of efficient algorithms, a flexible web application consisting in the visualization and annotation of one or several samples, as well as a database storing valuable informations on samples, patients and experiments.
The whole platform enables an autonomous usage in an immunology or hematology lab. This platform has a much wider spectrum than the original Vidjil algorithm described previously  [17] , which only consisted of a C++ program on the command-line to analyse high-throughput sequencing reads on TRγ and IgH loci. The algorithm now processes all immunoglobulin and T-cell receptor human loci, as well as some incomplete or unusual rearrangements. Moreover, we now offer a web application, which displays the results, stores the data and runs the analyses using several software: The platform proposes indeed several ways to analyze data with complementary software (at the moment: IMGT/V-QUEST, IgBlast, Blast, MiXCR). To our knowledge, Vidjil is the first open-source RepSeq platfrom enabling this autonomous usage, from raw sequence files to analysis, annotation and storage (Fig  1 ).
Design and Implementation The Vidjil platform can run any RepSeq program thats outputs V(D)J clonotypes from input data. Even if the platform was initially designed for the Vidjil algorithm, it does not rely on a specific algorithm: It includes other software, as for example MiXCR (see in the clinical data analysis, below). The following sections describe both the updated algorithm and the client and server sides of the web application. Vidjil is developed with systematic testing (more than 2,000 tests targeting all components, algorithm, web application client and server), continuous integration and regular releases (see S2 File).
Repertoire Sequencing (RepSeq) analysis software generally take as input a set of reads and process this set analyzing V(D)J recombinations and gathering them into clonotypes while computing statistics on the repertoire. Some of these software further include pre-processing as well as visualization capabilities. Finally, some more specialized software focus on specific aspects of RepSeq studies. The originality of the Vidjil platform is to propose a complete pipeline for the end-user, starting from the raw reads to the interactive analysis. The Vidjil web application currently runs the Vidjil algorithm, MiXCR, PEAR, and has links to IMGT/V-QUEST and IgBlast. Further software integration is planned. Note that IgGalaxy is also built on a pipeline concept that allows to pipe several software.
A key feature of the Vidjil web application is the sample, patient and experiment database, accessible from the web application client, that allows a daily clinical or research use without bioinformatics knowledge. Moreover, the client of the Vidjil web application can also be used independently to interact with the results of a RepSeq analysis. doi:10. 1371/journal. pone. 0166126. g001 The algorithm was initially applied on TRγ and IgH loci  [17]. It was extended to have an as complete as possible analysis of lymphoblast and lymphocyte sequences arising from all stages of the human hematopoiesis. Indeed, the algorithm now analyzes reads recombined from all immunoglobulin (IgH, Igλ, Igκ) and T-cell receptor human loci (TRα, TRβ, TRγ, TRδ), as well as some incomplete or unusual rearrangements (Dh/Jh, Dδ2/Dδ3, KDE-Intron, mixed TRα-TRδ recombinations), by looking for k-mers corresponding from given "left" (5') and "right" (3') regions.
Some incomplete recombinations start from a D gene. As D genes are very short (8 to 37 bp), there may be not enough k-mers to detect them, especially when the recombination added mutations or deletions. An improvement was to include neighbor regions of the germline genes: for example, Dδ2-Jδ recombinations usually contain some sequence upstream of the Dδ2 gene. The germline genes are taken from IMGT/GENE-DB  [18] , that are free for academic use. Other genomic regions (KDE, Intron, neigbhbor genes) are obtained by direct queries to GenBank (ncbi. nlm. nih. gov). The program can be configured to look for other recombinations and can work with any germline file or combination of germline files, through configuration in the germlines. data file described in the documentation, for instance to analyze sequences from other species.
Web Application for Interaction with RepSeq Data The Vidjil web application, developed in Javascript with jQuery and d3. js, is made for the visualization, inspection and analysis of clonotypes and their tracking along the time in a MRD setup or in a immunological study. The web application visualizes data processed by the Vidjil algorithm or any RepSeq clonotype gathering software as soon as they output a compliant JSON format (documented on vidjil. org/doc). This gives some modularity to users if they need to combine Vidjil results with other data, coming from either personal analysis or other software or scripts. The main components of the visualization are a list of clonotypes, a plot with either a grid view or a bar view, a window with the sequences and, when there are several samples, a graph (Fig  2 ).
Server with Sample and Patient/Experiment Database A sample database links the web application and the algorithmic part, allowing users to upload sequence files and manage their jobs directly from the web application. When uploading files, the user can choose some predefined preprocess to be launch on her data. At the moment we offer to merge paired-end reads using PEAR  [24]. A server, implemented in Python with the web2py framework (web2py. com), queues the job requests, allowing many jobs to be scheduled without overloading the server. The publicly available L4 dataset is a diagnosis sample of an acute lymphoblastic leukemia patient (dataset and interactive visualization at vidjil. org/data). The color denotes productivity, magenta means the sequence is not productive, while light green is.
The user can also export data to browse them offline, without any access to the server. Server administration and maintenance. The Vidjil server is a full-stack environment, making use of many tools to ensure the system is healthy and suitable for use in a professional environment. Some of the notable features are: monitoring, regular backing-up of data, notifications to users for maintenances and upgrades. There are also many tools aimed at keeping a maintainable piece of software that provides satisfactory usage. The aforementioned tests are a part of these tools. Installing the server in a hospital or a research lab does not require huge facilities (see usage or installation instructions in S1 File). In fact, disk storage to store input data (as for any highthroughput sequencing experiment) is the main constraint in our environment.
Clinical Data Analysis We now illustrate what the Vidjil platform brought in RepSeq data analysis through two clinical usages of the application. Both the raw FASTQ files and the interactive visualization can be accessed at vidjil. org/data. Patient L3 (5 samples). This dataset (patient 063 from  [25] ) contains one diagnosis sample and four follow-up samples of a patient with acute lymphoblastic leukemia (ALL). The patient received stem cell transplantation at day D183, and unfortunately relapsed at the end of the studied period. The libraries were prepared with PCR BIOMED-2 primers on the TRγ and IgH loci, and sequenced on an Ion Personal Genome Machine with an Ion 318 Chip Kit  [25]. Results were analyzed on the web application using either Vidjil and MiXCR algorithm. Using the Vidjil web application with either algorithm enables to identify TRγ and IgH clonotypes and track them through the time, annotating specific clonotypes.
This dataset (patient 125 from  [26] ) contains one diagnosis sample coming from an ALL patient. The library was prepared with BIOMED-2 and custom TRδ, Igκ, TRγ primers, and sequenced on an Ion Personal Genome Machine with an Ion 318 Chip Kit. This patient was included in a systematic comparison on 125 patients, some of which having incomplete Dδ2-Dδ3 and Intron-KDE recombinations  [26]. This dataset shows both the ability of the algorithm to process these incomplete recombinations and the ability of the web application to display them. Moreover, the ability to process data with several analysis software inside the platform gives more confidence in the results. For example, IMGT/V-QUEST currently does not process Dd2-Dd3 and Intron-KDE recombinations, and thus do not process a large part of these clonotypes.
Usage of the Web Application The public test server was opened in October 2014. 40 labs of 11 different countries submitted almost 14 billion sequences in 9,266 sequence files, with an average of 1. 5M reads per file. Data mostly came from Illumina Mi-Seq and Ion Torrent sequencers. Paired-end data were entered either separately, or after processing by software such as PEAR  [24]  and pRESTO  [27]. The Vidjil algorithm works on reads coming from either amplicon-based or capture-based deep sequencing strategy. While DNA-Seq sequencing with specific V(D)J primers usually lead to more than 95% analyzed sequences, capture with many probes or RNA-Seq strategies usually lead to datasets with less than 0. 1% V(D)J recombinations. Analysis times are compatible with daily research or clinical work.
Acknowledgments We are very grateful to  Martin Figeac ,  Claude Preudhomme  and the people in the hematology laboratory of the Lille hospital. We thank  IMGT/V-QUEST  and  MiXCR developers  for their help in enabling interoperability and optimizing parameters. We thank all users of Vidjil for their feedback, that helped us improve both the algorithm and the web application, and the anonymous reviewers for their constructive comments on an earlier version of this manuscript. Finally, we thank the  EuroClonality-NGS consortium  for productive discussions.
05). Moreover, it was noted that basal circulating levels of both calcium and phosphorous were higher in Tg compared to WT littermate mice, which could be attributed to reduced renal function (Figure  5G-H ; p ≤ 0. 05). Together, these data indicate that DT treatment produces deleterious effects in multiple tissues in both Tg and WT littermates, suggesting that any contribution of osteocyte ablation is likely complicated in these mice by such "off-target" changes. In addition to kidney and lymphoid tissues, we also evaluated histopathological changes in liver, muscle, brain and lung, all of which appeared histologically normal in both WT and Tg mice. Acute tubular necrosis was seen in the kidney in Tg mice by three days of DT treatment; similar, but less severe changes were observed in DT-treated WT littermates (Figure  5A, B, D, E ).
Calcium and Phosphorus Measurements Calcium and phosphorus levels were measured in the serum of 4 groups of 10-week-old male mice (Groups 1 and 2 contained WT, and Group 3 and 4 were Tg mice). WT mice (Groups 1 and 2) were treated by intraperitoneal administration with either: (1) phosphate-buffered saline (PBS) (untreated WT (n = 3)) or (2) 50 µg/kg DT (n = 6) (ref D0564, Sigma, treated WT); and Tg mice (Groups 3 and 4): (3) PBS treated Tg (n = 4) or 50 µg/kg DT (n = 3) treated. Seven days after the intraperitoneal injection, blood was collected from the eye and centrifuged. Serum was processed for the measurement of calcium and phosphorus using Architect C8000 automat (Abbott, Lake Forest, IL, USA). Data are expressed in mmol/L.
TUNEL Staining Apoptosis was evaluated 7 days following single DT injection using the Dead-End TUNEL kit (Promega, Madison, WI, USA) in six 1 mm regions of cortical bone situated 5 mm below the growth plate using ImageJ software (National Institute of Health USA). Briefly, paraffin sections were de-waxed, and re-hydrated then slides were incubated in 0. 85% NaCl for 5 min at room temperature and washed 3 times in PBS. Sections were post-fixed in 4% PFA (paraformaldehyde) for 15 min at room then permeabilised by treatment with 20 µg/mL proteinase K for 15 min at room temperature. The tissue was equilibrated in equilibration buffer at room temperature for 10 min before being labelled with terminal deoxynucleotidyl transferase (TdT) reaction mix for 1 h at 37 • C. The reaction was stopped by immersing the slides into 2× SSC buffer for 15 min.
Quantitative PCR For qPCR, qPCRBIO SyGreen 1-Step Lo-ROX (PCR Biosystems, London, UK) was used to perform qPCR with the absolute quantification method. Standards for each gene of interest were generated, and the copy number of genes of interest was quantified using the Bio-Rad CFX manager software; then, the results were normalised to GAPDH, which showed no significant difference when comparing each selected Tg tissue to its WT counterpart. qPCR reaction primers with amplification efficiency between 90% and 110% and an R2 standard curve value between 0. 99 and 1. 00 were considered acceptable. Primers' specificity was demonstrated with a single peak of the melting curve with a specific temperature. reaction buffer contained, 5 µL of 2× qPCRBIO SyGreen 1-Step Mix, 0. 4 µL forward primer, 0.
Statistical Analysis Data were checked for a normal distribution using the D'Agostino and Pearson omnibus normality test. To compare between two groups, Student's unpaired t-test was used. In comparisons between more than two groups, one-way analysis of variances (ANOVA) was used with a Tukey post hoc comparison. All statistical analyses were performed using GraphPad Prism 6 (GraphPad Software, Inc. , San Diego, CA, USA), and the results are expressed as the mean ± the standard error of the mean (SEM). p < 0. 05 was considered to be significant.
Acknowledgments: This was supported by  Arthritis Research UK  ( 18768  and  20581 ),  Biotechnology and Biological Sciences Research Council (BBSRC)  ( BB/I014608/1 ) and by a  Saudi-Arabian Government  funded PhD Studentship awarded to  Ahmed Al-Jazzar ,  Cheryl L. Scudamore  and  Andrew A. Pitsillides.
Of the 94 patients included in our study, 78 required treatment, according to the IWCLL2008 criteria for active disease, and 24 died during follow-up. Median time to first treatment in the entire cohort was short (36 months; Supplementary Tables  2  and 3 ). By receiver operating characteristic analysis we found that 1000 ng/ml of sCD163 was the best threshold to segregate patients into two groups. sCD163 high patients had significantly shorter TFS (logrank P = 0. 04, 30 versus 42 months; Figure  1f , Supplementary Table  2b ), but also shorter OS (logrank P = 0. 03; Figure  1g , Supplementary Table  3 ). These last data are important, as most patients requiring therapy received modern immune-chemotherapy regimens (Supplementary Table  2b ). On the other hand, sCD68 (high if 421 ng/ml) and sHMGB1 (high if 4227 ng/ml) were not associated with outcomes (Supplementary Figures  2  and 3 ).
ACKNOWLEDGEMENTS This work was partly funded by the  LABEX  project (Investissements d'Avenir  ANR11-PHUC001 ). F Boissard 1 , C Laurent 1,2 , AG Ramsay 3 , A Quillet-Mary 1 ,  J-J Fournié  1 , M Poupot 1,4 and L Ysebaert 1,2,4.
Genome structure analyses The spotted gar karyotype was determined from caudal fin fibroblast cell cultures established as described for zebrafish  108  (Supplementary Note 8. 1). Conserved synteny analyses between gar, tetrapods (human, chicken) and teleosts (Supplementary Note 8. 2) were performed with 1) Circos plots  109  based on orthology relations from Ensembl75 and as described in Supplementary Note 13. 1); 2) the Synteny Database 94 after integration of the gar genome assembly (Ensembl74 version); and 3) comparative synteny maps derived as described in refs. 16, 110.
miRNA annotation and analysis Gar miRNAs were studied in silico (Supplementary Note 11. 1) by blasting teleost and tetrapod miRNAs from miRBase  74, [111] [112] [113]  against the gar genome assembly and confirmed with RNAfold 114 (see also ref. 72  ). miRNA annotation and analyses based on sequencing data of gar miRNAs (Supplementary Note 11. 2) was performed as described for zebrafish  73  by utilizing small RNA-seq data from adult brain, heart, testis, and ovary tissue, which were processed and annotated with Prost! 115 according to miRNA gene nomenclature guidelines  116  ; miRNA orthologies based on conserved synteny were established using Ensembl  117  , the Synteny Database  94  and Genomicus  118, 119.
Comparative gene expression analyses Curated lists of TGD ohnologs and TGD singletons of zebrafish and medaka and their gar (co-)orthologs were generated by integrating phylogenetic information from EnsemblCompara GeneTrees 93 (Ensembl74) and conserved synteny data from the Synteny Database  94  (Supplementary Note 13. 1). For all three species, RNA-seq reads from the PhyloFish transcriptomes (Supplementary Note 3. 2,13. 2) were mapped against the longest Ensembl reference coding sequence of each gene with BWA-Bowtie  128, 129  , counted with SAMtools  130  and normalized for each gene across the 11 tissues using DESeq  131. The correlation of expression patterns and relative levels of expression between each zebrafish/ medaka gene and its gar ortholog and of singletons, ohnolog 1, ohnolog 2, and 'ohnolog pairs' were determined using R  132.
Node support is shown as posterior probability and bootstrap support from maximum likelihood analysis (Supplementary Fig. 6 ). The tree shows the monophyly and slow evolution of Holostei (gar plus bowfin) compared to their sister lineage, the teleosts (Teleostei). See also Supplementary File 1 and Source Dataset 1. a) The spotted gar karyotype consists of macro-and microchromosomes (see Supplementary Fig. 7  for chromosome annotations). b) Circos plot  109  showing conserved synteny of gar (colored, left) vs. human (black, right) chromosomes. c) Gar vs. chicken shows strong conservation of genomes for 450 million years and one-to-one synteny conservation for many entire chromosomes, particularly microchromosomes (e. g. , Loc13 and Gga14; Loc23 and Gga11, etc. ). d) Assembled chromosome lengths (in megabases, Mb) for gar and chicken chromosomes with one-to-one conserved synteny are highly correlated (R 2 = 0.
Multiple examples of such pre-TGD chromosome fusions explain the absence of microchromosomes in teleosts. See Supplementary Note 8. 2 and Source Dataset 2 for details. Orthologies (gray vertical bars) among lobe-finned vertebrates (e. g. , human, coelacanth) and teleosts (e. g. , zebrafish) had previously been limited to Odam and Spp1. Gar connects lineages through orthologs of genes previously known only from either teleosts (scpp1, scpp3 genes, scpp5, scpp7, scpp9) or lobe-finned vertebrates (enam, ambn, dmp1, dsppl1, ibsp, mepe). Further putative orthologies supported by only short stretches of sequence similarity ('?') connect gar enam, ambn, and lpq14 with zebrafish fa93e10, scpp6, and scpp8, respectively; gar lpq1 and Scpppq4 in coelacanth; and gar lpq5 with Amtn in lobefinned vertebrates.
Arrows in human and zebrafish indicate intra-chromosomal rearrangements separating originally clustered genes into distant chromosomal locations (distance in megabases, Mb). Conserved synteny analysis of the gar scpp gene cluster on LG2 suggests that the scpp gene regions on zebrafish chromosomes 10 and 5 are derived from the TGD (Supplementary Note 10, Supplementary Fig. 26 ). b) The gar 'conserved synteny bridge' (Supplementary Note 11. 2) infers that the miRNA cluster of mir731 and mir462 on gar LG4 and zebrafish chromosome 8 and a miRNA-free region on zebrafish chromosome 2 are TGD ohnologous to the mammalian Mir425-191 cluster. c) Gar newly connects through synteny zebrafish TGD ohnologs mir135c-1 and mir135c-2 with mammalian Mir135B. See Source Dataset 3 for genomic locations in a-c.
ACKNOWLEDGEMENTS We thank the  Broad Institute Genomics Platform  for constructing and sequencing gar DNA and RNA libraries and  Jason Turner-Maier  for the gar transcriptome assembly. We thank the teams of the  Bayousphere Research Lab (Nicholls State University ) and the  University of Oregon Fish Facility  for gar work and husbandry. We thank  John Westlund  for design of species illustrations. Generation of gar sequences and assemblies by the  Broad Institute of the Massachusetts Institute of Technology (MIT)  and  Harvard University  was supported by  NIH/NHGRI  grant  U54 HG03067. This work was further supported by  NIH  grants  R01 OD011116  (alias  R01 RR020833 ) and  R24 OD01119004  (J. H. P. );  Feodor Lynen Fellowship ,  Alexander von Humboldt Foundation  and  Volkswagen Foundation Initiative Evolutionary Biology , grant  I/84 815  (I.
B. );  NIH  Grant  T32 HD055164  and  NSF   Doctoral Dissertation Improvement Grant   1311436  (A. R. G);  Uehara Memorial Foundation Research Fellowship 2013, Japan Society for the Promotion of Science Postdoctoral Research Fellowship   2012-127 , and the  Marine Biological Laboratory Research Award 2014  (T. N. );  Brazilian National Council for Scientific and Technological Development (CNPq)  Grants  402754/2012-3  and  477658/2012-1  (I. S. ); The Brinson Foundation  and the  University of Chicago Biological Sciences Division  (N. H. S. );  NSF  grant  BCS0725227 (K. K. ) ; Call "ARISTEIA I"  of the National Strategic Reference Framework 2007-2013 (SPARCOMP, # 36 ),  Ministry of Education and Religious Affairs of Greece  (T. M. );  ANR  grant  ANR-10-GENM-017  (PhyloFish; J. B. );  Wellcome Trust  (grants  WT095908  and  WT098051 ) and the  European Molecular Biology Laboratory  (D.
TBK1 aggregation was associated with the nuclear translocation of IRF3 (Fig. 3c ) and these aggregates do not co-localize with the autophagosomes (data not shown). Interestingly, 4 hours after poly(I:C) transfection, TBK1 accumulated at the centrosome, which is surrounded by the Golgi apparatus next to the nucleus (Additional file 3B, C). Fig. 2  The active form of TBK1 localizes at the Golgi apparatus after TLR3 stimulation. a HEK293T cells stably expressing HA-TLR3 were either left untreated or stimulated with poly(I:C) (10 μg/mL) for 30 or 60 minutes. Cells were then fractionated as described in Additional file 1A, and samples were analyzed by immunoblotting with antibodies against the indicated proteins. EEA1, kinectin, LAMP2, GAPDH, syntaxin-6, and VDAC served as loading and purity controls for endosomes, the endoplasmic reticulum, lysosomes, the cytosol, the Golgi apparatus, and mitochondria, respectively.
GAPDH was used as a loading control. b HEK293T cells were transfected with either an IFNβ promoter reporter or an NF-κB reporter, together with the Renilla luciferase gene as an internal control. In parallel, the cells were also transfected with an Ev or with plasmids encoding myc-tagged WT TBK1 (WT), TBK1 K38M (K38M), or TBK1 K30R/K401R (K30R/K401R). Luciferase assays were performed 24 h after transfection and the results were normalized against Renilla luciferase activity. The data shown are means ± SD from three independent experiments (analysis of variance and comparison with WT TBK1 in Student's t test). RLU, relative luminescence units. c Immunoblotting analysis of TBK1 -/-MEFs reconstituted with WT TBK1, TBK1 K38M (K38M), or TBK1 K30R/K401R (K30R/K401R). As controls, TBK1 +/+ and TBK1 -/-MEFs are shown.