dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic ID conversion. In order to easily allow comparisons between genome versions (ENSEMBL, RefSeq, UCSC, Genecode, Flybase and Wormbase) and not restrict the user to a specific set of gene identifiers, we developed a geneID converter. This function converts on the fly the user's input gene IDs to match the selected resource, without any intervention from the user. The conversion is performed using the latest release of BioMart  52. BioMart data were downloaded and are stored locally for reasons of speed and are regularly updated. Users can download the ID conversion table to control for genes that were dropped due to the automatic ID conversion process. Benchmarking the TF enrichment function of AnnoMiner. Benchmarking dataset. Benchmarking data-sets for testing AnnoMiner's enrichment function in the form of TF regulons were obtained from human single TF perturbation experiments (knock-downs, knockouts, over-expression and chemical inhibition) followed by RNA-seq from Gene Expression Omnibus (GEO  56  , 160 experiments), manually curated and made available from Keenan et al.
43. We retained only experiments targeting TFs for which AnnoMiner is storing at least one high quality TF ChIP-seq dataset for the assembly GRCh38 (hg38), leading to a total of 75 RNA-seq experiments left for benchmarking (benchmarkingSet). Benchmarking metrics. Precision-Recall (PR) and Receiver-Operating Characteristic (ROC) areas under the curves (AUC) for the benchmarkingSet were computed using the R package PRROC  67  , following the benchmarking procedure proposed and applied by  43  and  68. In brief, for each TF, signature gene-sets were derived based on differentially expressed genes of the perturbed TF versus its control and used for TF enrichment analysis. For each dataset, the rank of the perturbed TF was assigned to the positive (foreground (fg)) class, while all other ranks of TFs retrieved for the dataset were assigned to the negative (background (bg)) class; using the PPROC package, precision-recall (PR) and Receiver-Operator Curves (ROC) using continuous interpolation were computed and areas under curve (AUCs) for both were calculated (PR-AUC and ROC-AUC).
As the negative bg class greatly exceeded the positive fg class, we down-sampled the negative class to the same size as the positive class. We repeated this procedure 5000 times. We used the approx R function to linearly interpolate between all points from the 5000 ROC and the 5000 PR curves and calculated the mean ROC and PR AUCs values of all bootstrap runs. Furthermore, we analysed the cumulative distribution of the rank values for each TF across all experiments it was perturbed in, which is not expected to be uniform, unless the TF would rank randomly in all its associated datasets. We then performed the Anderson-Darling test using the goftest R package to test for significant deviations from this distribution, indicating a non-random distribution of the TF. Finally, we computed the percentage of perturbed TFs that were correctly ranked within the first percentile to ensure high ranking of the TF.
All values are available in Table  1. Benchmarking against existing tools. We compared the performance of AnnoMiner's TF enrichment function with the performance of the following web-servers: TFEA. ChIP 42 , ChEA3  43  and EnrichR  44. The same dataset (benchmarkingSet) was used for all web-tools tested. Only datasets which could be effectively ranked by the web-tool under study were used. We used 57 signature gene-sets to test TFEA. ChIP and all the 75 signaturegene sets to test ChEA3. For EnrichR we evaluated the performance metrics on 5 different resources: ARCHS4, ChEA 2016, ENCODE 2015, ENCODE and ChEA Consensus and TRRUST 2019 using 74, 44, 44, 29 and 51 signature gene-sets, respectively. Epigenetic and RNA-seq data used. For analysing STAT3 in acute B-cell lymphomas, we used data from super-series GSE50724  48.
Only peaks which were significantly upregulated in ABC type B-cell lymphoma were selected for AnnoMiner's peak integration (FDR cut-off < = 0. 05; FC > = 1. 25). We used H3K4me3 data from an ABC cell line stored in the ENCODE database (GSE86718_ENCFF763KFL, replicated peaks BED file) for peak integration with significantly upregulated STAT3 peaks. Human genome version hg19 was used for analysis. RNA-seq data (GSE50721) from the same super-series was used for integration with STAT3 and H3K4me3 peaks, whereby we used statistical results provided by the authors of the study. For demonstrating the nearby genes annotation function, we used data from  51  and GEO dataset GSE52974, describing long-range enhancer elements regulating Myc expression for normal facial morphogenesis, mapped to mouse genome version mm9.
For demonstration of the TF & HM enrichment function we used RNA-seq data from a longevity study focusing on daf-16/FoxO mutants in C. elegans ( 53 and GEO dataset GSE72426) and C. elegans genome version ce11/ WBcel245. Analysed RNA-seq data from developing indirect flight muscle (IFM) in Drosophila melanogaster were taken from GSE107247  46. We used the DESeq2 comparisons between time-points 72 h and 30 h after puparium formation (APF) provided by the authors. Genes with log2FC in absolute value greater than 2 and FDR smaller than 0. 01 were considered as differentially expressed. For integration with phenotypic data, we made use of data published on muscle morphogenesis and function in Drosophila 57 and flight muscle growth in Drosophila  47. For integrating gene expression changes of yki knocked-down flight muscles and flight muscles expressing a constitutively active yki compared to wild type, with Yki and Sd protein genome occupancy data, we used BRB-seq data from yki knock-down and yki constitutively active fly strains  47  available from GEO (GSE158957).
The table can be sorted according to different values, downloaded, browsed online, or the gene list can be copied to the clipboard for further analysis (e. g. enrichment analysis). The user can also select a new target region or start a completely new analysis. Significant differential peaks from STAT3 (GEO dataset GSE50723) were uploaded for producing this Figure. For (d), differential expression data were uploaded from the associated GEO dataset GSE50721 from super-series GSE50724.
The user can also select a new target region or start a completely new analysis. Significantly upregulated peaks from STAT3 (GEO dataset GSE50723 from super-series GSE50724), as well as H3K4me3 methylation data from a comparable cell line (GEO dataset GSE86718) were uploaded for producing this Figure; differential expression data were taken from the associated GEO dataset GSE50721 from super-series GSE50724.
Acknowledgements We thank  Friedhelm Pfeiffer ,  Assa Yeroslaviz  and all team members of the  IBDM Computational Biology team  for helpful discussion and critical reading of the manuscript. We thank  Aynur Kaya-Çopur  for sharing data before publication. Fly stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) and the  Vienna Drosophila Resource Center (VDRC)  were used in this study.
Funding This work was supported by the  French National Research Agency  with  ANR  grant  ANR-18-CE45-0016-01  MITO-DYNAMICS awarded to BHH and  FS  and with  ANR-ACHN MUSCLE-FORCES  (F. S. ); by the  excellence initiative Aix-Marseille University A*MIDEX  ( ANR-11-IDEX-0001-02 , F. S. ); the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant  310939  (F. S. ); the  Human Frontiers Science Program (HFSP ,  RGP0052/2018 , F. S. ); the  France-BioImaging infrastructure  supported by the  French National Research Agency  ( ANR-10-INBS-04-01 , Investments for the future); by the " Investissements d' Avenir" French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative of Aix-Marseille University -A*MIDEX"; and by the  Max Planck Society , the  Centre National de la Recherche Scientifique (CNRS) ,  Aix-Marseille University  and the IBDM  UMR 7288.
Data availability All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Availability of all other data, materials and software: The AnnoMiner web-server is freely available at http:// chimb orazo. ibdm. univ-mrs. fr/ AnnoM iner/. The source code of AnnoMiner, as well as all data necessary to reproduce the study are available at https:// gitlab. com/ haber mann_ lab/ annom iner. Details on software provided in this manuscript: Project name: AnnoMiner, Project home page: http:// chimb orazo. ibdm. univ-mrs. fr/ AnnoM iner/, Archived version: https:// gitlab. com/ haber mann_ lab/ annom iner, Operating system(s): Platform independent, Programming language: JavaScript, Java, PHP, MongoDB, Other requirements: none, License: GNU public license, Any restrictions to use by non-academics: none.
(c) When clicking on 'Show Genes!' , the user retrieves a list of associated genes that fulfils the selected overlap criteria with the peaks. The table can be sorted according to different values, downloaded, browsed online, or the gene list can be copied to the clipboard for further analysis (e. g. enrichment analysis). The user can also select a new target region or start a completely new analysis. Significant differential peaks from STAT3 (GEO dataset GSE50723) were uploaded for producing this Figure. For (d), differential expression data were uploaded from the associated GEO dataset GSE50721 from super-series GSE50724.
b 1 Figure 8. 18 Figure 8. The Yorkie and Scalloped transcriptional complex regulates muscle-specific gene expression during flight muscle growth. (a) Using AnnoMiner's peak integration function with ChIP-seq data from Yorkie (modENCODE ENCFF708VEF) and Scalloped (modERN ENCFF664CDO) we found high base-pair coverage of both TFs in the TSS (-2000 to + 500 bp of the Transcription Start Site) of putative target genes. We selected these peaks from both TFs and integrated them with flight muscle specific BRB-seq data ( 47 and GEO dataset GSE158957) isolated from a yorkie loss-of-function muscles at 24 h APF (b) or yorkie gain-of-function muscles at 24 h (c) and 32 h APF (d). The direct Yki/Sd targets Tm1 and Msp300 were downregulated in yorkie loss-offunction at 24 h APF. Only scalloped was identified as upregulated direct target from 24 h yorkie gain of function muscles.
the ONT instrument as they analyze the sequencing data at the end of the run. Herein, we introduce BoardION, an interactive web application for real-time evaluation of ONT sequencing runs. BoardION offers the possibility for sequencing platforms to remotely and simultaneously monitor all their ONT devices (MinION, Mk1C, GridION and Pro-methION). It also allows the comparison of several sequencing experiments to assess the library's preparations or the quality of the input samples. In addition, we compare BoardION and Minotour  [14]  which is currently the only software able to process the data in real-time.
Implementation BoardION is organized in two components, the first one is dedicated to the preprocessing of the files generated by the ONT basecaller (guppy) and the second one is a dynamic web application, the central part of BoardION, which allows the visualization and comparison of sequencing metrics. BoardION can be installed and used directly from the source code or via a docker image containing all the necessary dependencies. Documentation, github and dockerhub repositories, as well as an interactive demo version of BoardION are available at http:// www. genos cope. cns. fr/ board ion/.
Data preprocessing Assessment of an in-progress run is performed by using information contained in the sequencing summary file generated by the guppy basecaller, but this file is not directly read and load by the web application. Indeed, the sequencing summary can be quite large (more than 10 GB) and will become larger in the future with the increase of the throughput. Instead, a preprocessing tool analyzes it periodically to generate lightweight datasets that contain only the metrics of interest. On each launch, the software searches for the sequencing summary files in the input folder and uses the presence of the final_ summary. txt file to detect completed experiments. This preprocessing step, developed in C++, uses the seekg function of the iostream library to directly access and process only newlines.
With these optimizations, statistics can be computed and updated in the web application every minute. For each flowcell, the preprocessing script generates five small files (< 3 MB): four containing statistics calculated every 10 min and one containing the read length distribution. The C++ software also creates a file listing all the flowcells, their status (completed or not) and the latest metrics. The cpu time and memory usage of the preprocessing script are correlated with the number of reads of a run. It takes one minute and 60 Mo on average to process a single run and less than 5 min and 100 Mo on our largest PromethION run (83 M reads).
BoardION web application We developed a web application because it has the advantage of being easily accessible regardless of the user's configuration. Thus, users can remotely and simultaneously monitor all the sequencing runs of their ONT devices. The BoardION web application is based on the R shiny package  [15]  that allows us to describe the content of the interface and the relationships between the elements of the application. It also uses ggplot2  [16]  and plotly R library  [17]  to create dynamic JavaScript graphs. The memory usage of the web application is dependent on the use of the interface, but generally requires less than 400 Mo. The BoardION application computes widely used metrics such as yield, translocation speed, read length and quality. All graphs, dynamically generated, can be exported and saved as images.
Minknow is a great application to monitor a single run, but BoardION is dedicated to the monitoring and the comparison of all the runs performed in a given facility. MinoTour is currently the only platform able to process the ONT sequencing data in real-time. The platform is composed of a database to store sequencing metrics and a web user interface. The interface displays metrics describing run efficiency (yield, read length and quality, rate, pore activity) and alignment to a reference genome (coverage). Users can also setup alerts to be informed when a threshold is reached. We tested mino-Tour to compare its functionalities with BoardION. Installation of minoTour on unix is quite hard for biologists without strong computer skills. MinoTour developers propose to upload data on their server but due to web transfer this solution does not allow a real time analysis of data and is problematic in term of confidentiality.
In contrast, the docker image of BoardION is easy to install and platform independent. We tested minoTour using data provided by the minoTour platform. Unlike BoardION, minoTour offers the possibility to explore the signal of the runs and the coverage obtained on the sequenced genome. However, BoardION has some additional features such as comparison of runs, overview of all runs and customizable plots that allow users to thoroughly explore their sequencing experiments. Furthermore, we did not succeed to upload our own sequencing data on the minoTour server. It seems that minoTour has not been updated since 2017, so, as ONT technology evolves quickly, minoTour seems to be currently unusable on recent ONT sequencing data. Several applications can take advantages of all ONT generated data (assembly, structural variation) but others need only high quality data (identification, SNV, epigenetics).
Acknowledgements The authors are grateful to the  Oxford Nanopore  staff for technical help and for granting Genoscope access to the  Pro-methION Early Access Programme (PEAP). We thank the  France Génomique Infrastructure  (www. france-genom ique. org) for its support. We also thank  Frédérick Gavory ,  Paul Mielle  and  Thomas Mejean  for their insight and support and  Corinne Cruaud  for testing the tool in real conditions.
Availability and requirements Project name: BoardION. Project home page: https:// github. com/ insti tut-de-genom ique/ Board ION. Operating system(s): Platform independent. Programming language: c++, R. Other requirements: R library shiny 1. 4. 0. 2, plotly 4. 9. 2. 1 and ggplot2 3. 3. 1. License: CeCILL. Any restrictions to use by non-academics: no.
Exploiting all the information available in OD growth curves has the potential to be more informative. Top: Minimal inhibitory concentrations (MICs) are measured with a unique time point at 18 hours. Isolates A and B have a high MIC, whereas isolate C has a low MIC. Bottom: Following the optical density (OD) of the cultures during the experiment allows one to unveil that strains A and B behave differently despite having the same MIC. One can qualify A as highly resistant, and B as poorly resistant but tolerant and resilient. Strain C is neither resistant nor tolerant. B. Our approach. A 96-well plate is inoculated with the isolates to be tested, and a range of antibiotic concentrations is applied. The OD is read by an automated plate reader and processed automatically. The data is then fed into the model, for which numerical parameters are estimated, along with their uncertainties, for each isolate.
On the right are pictured schematic representations of the typical response of the cell culture to an initial antibiotic treatment. Population response: When the cell population is attacked by the β-lactam treatment (red dots), cells elongate and eventually lyse, releasing β-lactamase in the environment (blue circles) that degrade the antibiotic thus protecting the surviving cells. B. Comparison of experimental data and the model fit. The isolate ANSES 32139 is treated with different concentrations of cefotaxime (CTX), and optical density and live cell numbers (dots) are measured as described in the Methods section. For cell numbers we also show 95% confidence intervals on the CFU inverse problem estimation (see Methods). Data for OD has been subsampled for clarity. We show in solid lines the output of a model fitted on OD only, and in dashed lines a model simultaneously fitted on OD and cell number.
Data analysis OD blanking. The measured OD of a well is the sum of the OD of the cell culture, and of the OD of the well bottom, which slightly varies among wells of the same plate. For this reason, a constant OD was removed from each well data, such that the initial measured value matched the experimental initial cell dilution. Removal of aberrant time points. Occasionally, an OD reading fails and returns a value much larger than both the previous and the following ones of the same well. As this situation is highly biologically improbable, these outliers were removed before model fitting. Correction of the non-linearity of the measurements. The relation between measured and theoretical OD was studied and measurements were corrected for non-linearities (see Supplementary Text F).
Model fitting Each parameter was restricted to a range of biologically plausible values. Depending on the role of the parameter and the size of the range, a change of variable was applied or not, to perform the search of this parameter in the linear space or in a logarithmic space. All 17 search ranges were then brought back to the interval [0, 10] and it is in this [0, 10] 17 space that the parameter search was performed. The cost function computes the log-likelihood of the data assuming independent Gaussian measurement noise on each point. Special care was taken when searching on mixed datasets involving both OD measurements and cell counts, to balance the contribution of both data types. The integration of the ODE system was done with the solve_ivp method of scipy 31 with the method LSODA , and absolute and relative tolerances set to 10 -9.
B. Comparison of experimental data and the model fit. The isolate ANSES 32139 is treated with different concentrations of cefotaxime (CTX), and optical density and live cell numbers (dots) are measured as described in the Methods section. For cell numbers we also show 95% confidence intervals on the CFU inverse problem estimation (see Methods). Data for OD has been subsampled for clarity. We show in solid lines the output of a model fitted on OD only, and in dashed lines a model simultaneously fitted on OD and cell number. The model also outputs predictions for the average cell length, and the concentration of antibiotic and other chemical species in the culture medium.
Acknowledgements The authors thank  Arthur Carcano  for help with the fitting of a previous version of the model, Anđela Davidović for discussions about the model and help with image analysis,  Sebastian Sosa Carrillo  and  Chetan Aditya  for assistance in the laboratory,  Jean-Yves Madek  for giving access to the isolates  ANSES  and  Thierry Naas  to the isolates CNR,  Jakob Ruess  for comments on the manuscript,  Adriana Chiarelli ,  Nicolas Cabanel  and  Isabelle Rosinski-Chupin  for discussions. This work was supported by  ANR  grants  Seq2DiAg  ( ANR-20-PAMR-0010 ) and  Anoruti  ( ANR-20-PAMR-0001 ), and by the  H2020 Fet-Open COSY-BIO  grant (grant agreement no. 516 766840 ).
Data and code availability All data, plateRider scripts and log files are available on zenodo: 10. 5281/zenodo. 5111026. All scripts needed for model calibration and figure plotting are available as a git repository at the address https://gitlab. inria. fr/InBio/Public/esbl-escape. The custom plate reader driver plateRider is an open source software available at https://gitlab. inria. fr/InBio/Public/platerider.
MAIT cells are activated and cytotoxic in patients with COVID-19. As MAIT cell frequency is reduced in the blood of patients with COVID-19, we analyzed their surface marker expression and their cytokine production. An activated phenotype with an increase of CD69 expression was observed in patients with COVID-19, with a median of 41% CD69 + MAIT cells in IDU patients and 67% in ICU patients, with some reaching 100% (Fig. 2a ). Blood MAIT cells also displayed a significant increase of NK cell-associated activation CD56 + marker compared to controls, which was highest in ICU patients (Fig. 2a ). Accordingly, double-positive CD56 + CD69 + MAIT cell frequency was increased in IDU patients and even more in ICU patients (Fig. 2a ). The frequency of MAIT cells coexpressing CCR6, the receptor for the CCL20 chemokine (a tissue-migrating marker) and survival CD127 marker was decreased, suggesting that low circulating MAIT cell frequency might reflect migration into inflamed infected tissues and/or activation-induced cell death (Extended Data Fig.
3a ). MAIT cell activation in some patients might be associated with secondary infections and presence of bacteria in the blood (Extended Data Fig. 3b ). In contrast to MAIT cells, CD69 + expression on CD8 + T cells remained moderate (median of 10%) in IDU and ICU patients although increased compared to uninfected controls (Extended Data Fig. 3c ). The frequency of CD56 + CD8 + T cells was similar in all three groups of patients. Consequently, the frequency of effector CD56 + CD69 + CD8 + T cells was modestly increased in patients with COVID-19 compared to uninfected controls (Extended Data Fig. 3c ). We next investigated the phenotype of MAIT cells, with unsupervised methods, on nine flow cytometry parameters. We first compared MAIT cells of patients with COVID-19 to those of uninfected controls by multidimensional scaling (MDS) plots, which revealed a progression of severity from uninfected controls to IDU and ICU patients (Fig.