dataesr/scientific-paragraphs-categorization huggingface dataset:
2018] ) were scanned by RNAMotif with descriptors of all 16 TA families. Hits that spanned the coordinates of the ORFs reported by Fozo et al. on the same DNA strand were extracted (by means of the "intersectbed" utility from the BEDTools 2. 24. 0 package  [Quinlan and Hall 2010] ). Among those, hits that contained ORFs that were in-frame with the ORFs of Fozo et al. (ORFs may not always be identical and could differ slightly in length in some cases depending on the start codon used) and whose ORF and total mRNA length matched the typical range of ORF and mRNA length for a given family were retrieved, whether or not the family matched that reported by Fozo and coworkers. Antitoxin asRNAs were localized using RNASurface 1. 1  (Soldatov et al. 2014) , which predicts sequence regions that are structurally more stable than the rest of the genome by local folding of segments up to a predefined size.
Genome sequences were scanned with RNASurface (run with options "--winmin 50 --winmax 300 -z -2 -d 500") to identify structured segments of length 50-300 nt that have a z-score lower than -2 (corresponding to a false-positive rate of 5%  [Soldatov et al. 2014] ). For a given TA family, RNASurface segments whose length corresponded to the typical asRNA length for that family (±20%) were retrieved. Putative promoter -10 boxes were predicted by searching (by means of the PERL module Regexp::Exhaustive) for the presence of the sequence motif TANNNT (where N means any nucleotide) in a window covering the region -20 to -6 upstream of the identified mRNAs and asRNAs. In cases where multiple motifs were present, the 3 ′ -most (i. e. , the closest to the RNA start) was selected. In order to find the pair of mRNA and asRNA corresponding to the same TA locus, RNAMotif and RNASurface results were intersected (using "intersectbed" from BEDTools) based on the defined genetic organization of the various T1TA families.
2012; Sayed et al. 2012; Pinel-Marie et al. 2014; Wen and Fozo 2014; Arnion et al. 2017). For families where the two RNAs do not overlap, a max. distance of 300 nt between the RNAs was allowed (except for the DinQ/AgrB family, see below), and for families where the two RNAs do overlap, min. and max. limits were set for the length of the overlap region based on known examples (Supplemental Table  S1 ). In T1TA systems the mRNA and asRNA share a complementary region of interaction. In families with overlapping RNAs this region generally corresponds to the overlap region, but for the Fst/RNAII and SprA1/SprA1as families interaction occurs outside the overlap sequence  (Weaver et al. 2009; Sayed et al. 2012 ). The software IntaRNA 3. 1. 0. 2  (Mann et al. 2017 ) was used to find mRNA-asRNA pairs that share a complementary region at the expected location for these two families and to identify regions of interaction for families with nonoverlapping RNAs (the interaction region was set to be at least 15 nt long).
Database implementation T1TAdb is implemented as a relational database in PostgreSQL 10. 13 (https://www. postgresql. org/). The database schema was designed following the five rules of normalization (http://www. barrywise. com/2008/01/database-normalization-and-design-tech niques/) to avoid data redundancy and inconsistent dependency among tables. The graphical web interface was developed using the PERL Catalyst framework (http://www. catalystframework. org/) to control and manage connections and SQL requests to the database. Graphical design of the web pages was done in dynamic and responsive HTML, JavaScript, and Cascading Style Sheets (CSS) (https://www. w3schools. com/), along with the PERL Template Toolkit 2. 26 templating system (http://www. template-toolkit. org/). The T1TAdb website is run via the Apache HTTP 2.
4. 6 server (https ://httpd. apache. org/) under the Linux CentOS 7. 8 operating system. Secondary structures of RNA were predicted using MFOLD 3. 6  (Zuker 1989 (Zuker , 2003)) , and annotated diagrams highlighting the location of specific motifs (start/stop codon, SD sequence, interaction region) were generated with VARNA 3. 93  (Darty et al. 2009 ). Secondary structures of toxin peptides were predicted using PSIPRED 4. 02  (McGuffin et al. 2000)  run with PSI-BLAST 2. 2. 26 against the UniRef90 protein sequence database (https://www. uniprot. org/ help/uniref) and drawn with POLYVIEW-2D  (Porollo et al. 2004 ). Hydrophobicity plots were computed with ProtScale  (Gasteiger et al. 2005). Interactive genomic maps in SVG format showing the localizations of TA loci were drawn using CGView  (Stothard and Wishart 2005).
The embeddable JavaScript/CSS version of the IGV browser  (Robinson et al. 2011 ; https://github. com/igvteam/igv. js/wiki) was used to provide an interactive visualization of the genomic context flanking TA loci, and SVG images of the genomic context were generated by means of the Gviz package (https://bioconductor. org/ packages/release/bioc/html/Gviz. html) in R 3. 5. 3 (R Core Team 2019; https://www. r-project. org/). Sequence similarity searches in T1TAdb are done with BLAST+ 2. 2. 31  (Camacho et al. 2009) , and multiple sequence alignments are computed by MAFFT 7. 407  (Katoh and Standley 2013). For peptide sequences MAFFT is run with the method "mafft-linsi" and the option "--localpair," whereas for RNA sequences MAFFT is run with the method "mafft-xinsi" and the option "--scarnapair" to incorporate structure information and produce a structural alignment  (Katoh and Toh 2008).
Alignments are visualized using the embeddable MSAViewer  (Yachdav et al. 2016) , which is part of the BioJS JavaScript tools  (Yachdav et al. 2015). In addition, for RNA alignments, a consensus structure is predicted by means of RNAalifold from the ViennaRNA 2. 1. 9 package  (Bernhart et al. 2008; Lorenz et al. 2011) , and a covariation analysis is conducted using R-chie  (Lai et al. 2012). Other bioinformatic software such as EMBOSS 6. 6. 0  (Rice et al. 2000) , BioPERL  (Stajich et al. 2002) , and FASTX-Toolkit 0. 0. 14 (http://hannonlab. cshl. edu/ fastx_toolkit/) were also used to generate and/or process the data included in T1TAdb.
ACKNOWLEDGMENTS This work was supported by  Institut National de la Santé et de la Recherche Médicale  ( INSERM U1212 ),  Centre National de la Recherche Scientifique  ( CNRS UMR5320 ),  Bordeaux University , and  Agence Nationale de la Recherche  ( ANR-12-BSV5-0025-Bactox1 ,  ANR-12-BSV6-0007-asSUPYCO ). The web server hosting T1TAdb is provided by the  ODS Web Hosting service of CNRS. We thank  Dr. Stéphane Thore  and  Dr. Sébastien Fribourg  for providing additional computing power. Finally, we thank all present and past members of the  Darfeuille laboratory  for fruitful discussions on this project and  Anthony Bugaut ,  Isabelle Iost ,  Anaïs Le Rhun ,  Simon Bonabal , and  Olga Soutourina  for critical comments on the manuscript. Database of type I toxin-antitoxin systems www. rnajournal.
Background Point-Of-Care (POC) diagnosis of life-threatening community-acquired meningitis currently relies on multiplexed RT-PCR assays, that lack genotyping and antibiotic susceptibility profiling. We assessed the usefulness of real-time metagenomics (RTM) directly applied to the cerebrospinal fluid (CSF) for the identification, typing and susceptibility profiling of pathogens responsible for community-acquired meningitis. Methods A series of 52 CSF samples from patients suspected of having community-acquired meningitis, were investigated at POC by direct RTM in parallel to routine real-time multiplex PCR (RT-PCR) and bacterial culture, for the detection of pathogens. RTM-generated sequences were blasted in real-time against an in-house database incorporating the panel of 12 most prevalent pathogens and against NCBI using EPI2ME online software, for pathogen identification.
In-silico antibiogram and genotype prediction were determined using the ResFinder bio-tool and MLST online software. Findings Over eight months, routine multiplex RT-PCR yielded 49/52 positive CSFs, including 21 Streptococcus pneumoniae, nine Neisseria meningitidis, eight Haemophilus influenzae, three Streptococcus agalactiae, three Herpesvirus-1, two Listeria monocytogenes, and one each of Escherichia coli, Staphylococcus aureus and Varicella-Zoster Virus. Parallel RTM agreed with the results of 47/52 CSFs and revealed two discordant multiplex RT-PCR false positives, one H. influenzae and one S. pneumoniae. Both multiplex RT-PCR and RTM agreed on the negativity of three CSFs. While multiplex RT-PCR routinely took 90 min, RTM took 120 min, although the pipeline analysis detected the pathogen genome after 20 min of sequencing in 33 CSF samples; and after two hours in 14 additional CSFs; yielding > 50% genome coverage in 19 CSFs.
Ethics As per French legislation, no specific patient consent was required. The analysis of biological samples obtained in the medical care context was considered as non-interventional research (article L1221-1. 1 of the French Public Health Code), requiring only the nonopposition of the patient during sampling (article L1211-2 of the French Public Health Code). All data were generated as part of routine laboratory work at the Assistance Publique-Hôpitaux de Marseille and Nîmes university hospital, in the context of the routine clinical management of patients suspected of having community-acquired meningitis. No specific clinical sampling was performed for this study and RTM was applied to anonymised left-over CSF samples for which the age and sex of patient were anonymously collected, following a standard routine laboratory protocol including multiplex real-time PCR, which was carried out in full respect of the French law regarding clinical research.
Routine microbial diagnosis In the POC laboratory, all CSF samples were routinely examined to count white and red blood cells directly using NucleoView NC-3000 equipment and Nucleo-View/ChemoMetec software (ChemoMetec Nucleo-Counter, Allerod, Denmark). In parallel, 200 µL samples of CSF were used for multiplex RT-PCR diagnosis (BioFire FilmArray Ò , bioM erieux, Marcy-l' Etoile, France) as previously described. 5 Further in the core laboratory, 200 µL of CSF was inoculated on chocolate agar PolyViteX (bioM erieux) and Columbia agar enriched with a 5% sheep's blood (bioM erieux) medium incubated at 37 °C under 5% CO 2 for five days, and on Columbia agar enriched with 5% sheep blood (bioM erieux) under anaerobic conditions for ten days at 37 °C for bacterial culture and in-vitro antibiogram in the standard bacteriology laboratories.
Metagenomic handling was performed in an 1800 cm 2 workspace. For library sequencing, four MinION instruments were attached in parallel to a powerful computer equipped with minimum Windows 10 or a Linux version (16. 04 LTS) operating system, an i7 processor, RAM 8Gb, USB 3 port, with and enough disk space to store the data (» 1 Tb) (Lenovo, China), and an internet connection. MinION-Sequencer reading, and data storage were performed using Minknow Oxford Nanopore software version (8. 3. 1). In addition, Oxford Nanopore EPI2ME software was installed for real-time data analysis.
In-silico data analysis Pathogen genome identification. Pathogen genome sequences were detected in real-time using an in-house pipeline. First, total MinION data were aligned against an in-house database constructed in reference to Biofire FilmArray Ò panel (Appendix 6), including complete genome sequences for each one of N. meningitidis, S. pneumoniae, H. influenzae, S. agalactiae, Escherichia coli, Staphylococcus aureus and Listeria monocytogenes (100 sequences per pathogen), VZV (148 sequences), HSV-1 (51 sequences), HSV-2 (33 sequences), Parechovirus (200 sequences) and Enterovirus (300 sequences) using a blast nucleotide command line with default parameters on the IHU server. This analysis was interpreted as positive when the number of sequences for any specific pathogen was 2. Further, detection was queried against NCBI GenBank database to increase probability pathogen genome detection using Taxonomer (https:// www.
taxonomer. com) and Oxford Nanopore EPI2ME online software (Figures  1, 2 ). To confirm real-time identification, reads quality control was performed using FastQC online platform and assembled by "Canu assembler" tool (Version 2. 1. 1) on Galaxy Europe online software (https://usegalaxy. eu/) and generated contigs were then aligned by Blastn against NCBI GenBank database. The identified hit-blast strain, defined by maximum sequence similarity, was used as a reference sequence for mapping of the total MinION reads using CLC Genomics Workbench software version 21. 0. 3 (Qiagen) with default parameters. Consensus sequences were extracted in fasta files for further analysis (Figure  2 ). The in-silico prediction of antibiotic resistance-encoding genes was carried out using ResFinder (https://cge.
cbs. dtu. dk/ services/ResFinder/) and Resistance Gene Identification (https://card. mcmaster. ca/analyze/rgi) online software using total pathogen reads and hit-blast strains identified by Blastn against NCBI GenBank database after assembly of the MinION reads, in reference to antibiotics routinely assayed in-vitro (Appendix 1). In-silico prediction of antibiotic resistance was performed only in the case genome coverage was 1%. Pathogen genotyping. Microbial genotypes were predicted using multi-locus sequence typing tools (MLST) on PubMLST (https://pubmlst. org/organisms), based on total specific sequences data and hit-blast stains identified by blast against NCBI GenBank database. Virus genotyping was performed directly on ViPR online database (https://www. viprbrc. org/) (Figure  2 ). Genotyping was in-silico predicted only in the case genome coverage was 1%.
General data CSF samples collected from 52 patients prospectively investigated in this study included 24 CSF samples at the IHU M editerran ee Infection POC laboratory in Marseille and 28 CSF samples at the bacteriology and hygiene laboratory at Nîmes University Hospital. These 52 CSF samples were collected from 24 female patients and 28 male patients, aged between 0 and 90 years (median, 38 years old), investigated between December 2020 and July 2021 (Table  1 , Appendix 3). 1 , Table  2 ). Viral cases were identified directly by blast against the in-house database and EPI2ME online software and confirmed by specific RT-PCR. False negative S. pneumoniae (sample 31) was confirmed by negative Illumina pair-end metagenomics, as previously described,  16  may be due to failed DNA extraction and/ or the limitation of RTM to detect low pathogen levels in the CSF (>35 Ct).
5, 7 Increased sensitivity could be achieved by improving DNA extraction through an adapted automatic library preparation protocol for low pathogen levels, including microbial genome enrichment and human genome depletion. 7, 23, 27 Also, the enlargement of the microbial panel included in the in-house database and its combination into one protocol DNA and RNA RTM is needed. This enlargement would allow for the one-shot detection of most pathogens responsible for community-acquired meningitis and meningoencephalitis, especially RNA viruses, the most frequently encountered causative agents of meningitis  28  and non-routinely diagnosed bacterial meningitis at the POC laboratory. 19.
e) Antibiotic resistance and pathogen genotyping were in-silico predicted using the ResFinder online database (https://cge. cbs. dtu. dk/services/ResFinder-4. 1/), and MLST online database (https://pubmlst. org/organisms). Abbreviations: CSF: cerebrospinal fluid. WBC: white blood cell. RBC: red blood cell. RTM: real-time metagenomics. MLST: Multi-Locus Sequence Typing.
org/organisms) for bacteria genotyping and on ViPR online database (https://www. viprbrc. org/) for virus genotyping. The in-silico antibiogram was predicted on ResFinder (https://cge. cbs. dtu. dk/services/ResFinder/) and Resistance Gene Identification (https://card. mcmaster. ca/ analyze/rgi) platforms using the total MinION reads and the generated fasta sequences with default settings. *In-silico antibiogram and genotype were derived from genome sequence only in the case of > 1% genome coverage.
Acknowledgements Madjid Morsli  is a PhD student supported by the  Fondation M editerran ee Infection. This work (lab material and reagents) was supported by the  French Government  under the " Investissements d'Avenir  "(Investments in the Future) programme managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research)  [reference: M editerran ee Infection  10-IAHU-03 ]. This work was also supported by the Fondation M editerran ee Infection at the  IHU M editerran ee Infection and bacteriology and hygiene laboratory at Nîmes University Hospital.
d) Real-time genome identification was performed directly by blast of the MinION generated data against an in-house database, then against the NCBI database using EPI2ME and Taxonomer online software. e) Antibiotic resistance and pathogen genotyping were in-silico predicted using the ResFinder online database (https://cge. cbs. dtu. dk/services/ResFinder-4. 1/), and MLST online database (https://pubmlst. org/organisms). Abbreviations: CSF: cerebrospinal fluid. WBC: white blood cell. RBC: red blood cell. RTM: real-time metagenomics. MLST: Multi-Locus Sequence Typing.
pneumoniae, three N. meningitidis, two H. influenzae, two L. monocytogenes and one S. aureus, while the other 26 CSFs detected positive for bacteria by RT-PCR were culture-negative. In-vitro antibiotic investigation yielded 16 susceptible bacteria (nine S. pneumoniae, three N. meningitidis) yielded 16 S. pneumoniae, seven N. meningitidis, six H. influenzae, one S. agalactiae and one VZV (median number of reads = 23), resulting from a blast analysis of MinION data against the in-house database and EPI2ME online analysis. A total of 47/52 CSF samples were detected as positive after two hours (median number of reads of 456. 5). Genomic data analysis showed 19/47 (40. 4%) of positive cases with >50% genome coverage including eight S. pneumoniae, five N. meningitidis, four H. influenzae, one each of S.
In this study, the performances of label-based SWATH-MS, SRM and PRM to accurately quantify ten candidate biomarkers of beef meat tenderness or marbling were evaluated, in a cohort of 64 bovine muscle tissues expected to cover a wide biological range of these traits. Limits of quantification, dynamic range and quantification performances were assessed. Moreover, a novel method to estimate protein amounts from SWATH-MS data in a label-free manner was introduced. The combination of this quantitative information enabled gaining insights into muscle type effect on the candidate biomarkers of beef meat qualities and muscle metabolism.
[27]. Eleven samples were randomly chosen as representative matrix for method development, external quality control and generating a spectral library necessary for SWATH-MS data interpretation. The matrix pool was prepared in tube-gel and fractionated by SDS-PAGE as described in Supporting Information. For absolute quantification, a concentration-balanced mixture of 20 accurately quantified SIL peptides (Spike Tides™ TL, JPT Peptide Technologies, Berlin, Germany) was spiked in each sample. Retention time standards (iRT; Biognosys, Schlieren, Switzerland) were additionally spiked in samples analysed in DDA and SWATH-MS modes.
Data analysis Spectral library generation. Identifications were performed using Mascot (version 2. 5. 1; Matrix Science, London, UK). The spectral library (SL) was generated in Skyline  [28]  (version 3. 7. 1. 11099) by importing Mascot result files and fixing a cut-off score of 0. 95. Details are given in Supporting Information. Selection of 10 candidate biomarkers and proteotypic peptides. Ten candidate biomarkers of tenderness or marbling were selected according to the criteria defined in Bonnet et al. [2]. The list of the targeted proteins and peptides is reported in Supporting Information Table  S3. Label-based quantification data processing. PRM, SRM and SWATH-MS data were processed with Skyline using appropriate settings. Chromatographic peaks were investigated to manually adjust peak integration boundaries and remove interfered transitions (at least three transitions were kept for each precursor).
For each method, peptides' limit of detection (LOD) and limits of quantification (LOQ) were determined using 8-points calibration curves. After ensuring that peptides are within their linear range, the ratios between endogen and SIL peptides were used to determine the mol amount of endogenous peptides. Results are reported in fmol/µg of muscular protein hereafter to limit bias towards the chromatographic system. Details are given in Supporting Information. Label-free quantification data processing. SWATH-MS data was processed with Skyline using appropriate settings. Validated proteotypic peptides were extracted using the same parameters as for label-based quantification. Details are given in Supporting Information. Peaks were reintegrated using the target decoy approach of the mProphet peak-scoring model  [29]  , and a q-value was assigned to each peak.
Only precursors with a q-value below 0. 01 were kept, and peptide intensity was obtained by summing all precursor intensities. Quantity prediction was performed using monotone spline smoothing  [30]. Monotone spline smoothing combines I-spline regression analysis and non negative least squares estimation to ensure monotonicity. Parameters of the regression models were estimated using the Lawson-Hanson algorithm for non-negative least square estimation. I-spline analysis was conducted using iSpline function from the "splines2" package  [31]  and non negative least squares models were fitted using the nnls function from the "nnls" package  [32]  in R 3. 6. 2 software. In addition panel plots were created using the "lattice" package  [33]. No prediction were computed nor derived for intensity values lying outside of the observed intensity range.
Added value of SWATH-MS for accurate label-free quantification In order to take advantage of the exhaustive nature of the SWATH-MS acquisition mode, quantification values were estimated for all proteins present in the samples, and not only for the 10 proteins of interest. In this regard, Schubert et al. [17]  and He et al. [18]  proposed strategies to estimate proteomewide accurate quantification on SWATH-MS/DIA, namely by assuming a linear correlation between summed MS2 intensities and accurate concentrations of anchor proteins, and by relying on the TPA algorithm  [37, 38]. Here, an I-spline regression model was fitted for each sample using data from labelbased quantification. The log-transformed peptide quantity was chosen as the dependant variable and the log-transformed peptide intensity as the predictor (Fig.
Concluding remarks Only a handful of comparisons of three targeted proteomics methods can be found in the literature  [5, 40, 41]. In this study, we have conducted a comparison of SRM, PRM and SWATH-MS assays developed and applied on a sample cohort of bovine muscle tissues. The first intention was to evaluate the performances of the three different assays for the accurate quantification of ten candidate biomarkers of beef meat tenderness or marbling using stable isotope-labelled standards. Targeted assays were performed on different couplings (microLC-TSQ Vantage for SRM, nanoLC-Q-Exactive Plus for PRM, and microLC-TripleTOF 6600 for SWATH-MS). By normalising fmol amount by the quantity of injected material and performing data analysis with Skyline, we assumed to have reasonably limited bias toward instrumentation to perform a fair comparison of the three quantification methods.