dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression analysis We used RNA-seq datasets from the PhyloFish database  (Pasquier et al. 2016) , which provides transcriptomes in fish for the following tissues: bones, brain, embryo, gills, heart, intestine, kidney, liver, muscle, ovary and testis. We used kallisto  (Bray et al. 2016)  with default parameters to quantify transcript abundances for the full set of Ensembl transcripts in zebrafish and medaka. We summed Transcripts per Million (TPM) values of alternative transcripts to obtain a quantification of the expression of their corresponding gene. Finally, TPM values were quantile normalized within each species to obtain equivalent distributions of gene expression levels across tissues  (Bolstad et al. 2003).
Objectives: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. Methods: The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms.
3] [14]  In 2012, we published ResFinder,  15  the first online bioinformatics tool aimed at users without specialized bioinformatic skills, which provides detection of AMR genes in WGS data submitted through a web server. The ResFinder tool has been widely used, and has so far executed more than 400 000 jobs from more than 32 000 IP addresses in over 100 countries. ResFinder was expanded with PointFinder, a tool that detects chromosomal point mutations mediating resistance to selected antimicrobial agents in a few selected bacterial species  16  and recently extended to include Mycobacterium tuberculosis. 17 ere we describe the development and evaluation of a new and more advanced tool, ResFinder 4. 0, which, in addition to the detection of AMR genes and chromosomal gene mutations, generates in silico antibiograms.
ResFinder 4. 0 databases ResFinder 4. 0 contains four databases including AMR genes (ResFinder), chromosomal gene mutations mediating AMR (PointFinder), translation of genotypes into phenotypes and species-specific panels for in silico antibiograms. The databases of ResFinder  15  and PointFinder  16  were reviewed by experts and, when necessary, entries were removed or added. Furthermore, the PointFinder database was extended to include chromosomal gene mutations leading to ampicillin resistance in Enterococcus faecium, ciprofloxacin resistance in E. faecium and Enterococcus faecalis, and resistance to cefoxitin, chloramphenicol, ciprofloxacin, fusidic acid, linezolid, mupirocin, quinupristin-dalfopristin, rifampicin and trimethoprim in Staphylococcus aureus. The genotype-to-phenotype tables were created by experts, by using additional databases (www.
bldb. eu for b-lactam resistance genes,  18  http://faculty. washington. edu/marilynr/ for tetracycline as well as macrolide, lincosamide, streptogramin and oxazolidinone resistance genes) and by performing extensive literature searches. In the genotype-to-phenotype tables, the ResFinder and PointFinder entries have been associated with an AMR phenotype both at the antimicrobial class and at the antimicrobial compound level. A selection of antimicrobial compounds within each class was made to include antimicrobial agents important for clinical and surveillance purposes for the different bacterial species included (Table  S1 , available as Supplementary data at JAC Online). The genotype-to-phenotype tables also include: (i) the PubMed ID of relevant literature describing the respective AMR determinants and phenotypes, when available; (ii) the mechanism of resistance by which each AMR determinant functions; and (iii) notes, which may contain different information such as warnings on variable expression levels (inducible resistance, cryptic genes in some species, etc.
19 revious versions of ResFinder were written in Perl, whereas ResFinder 4. 0 was rewritten in Python 3. The ResFinder software has not previously been able to process read data (FASTQ) directly but relied on an assembly step. ResFinder 4. 0 has implemented KMA,  20  which aligns reads directly to the databases without the need for assembly. Like all previous versions, ResFinder 4. 0 is released as open source under the Apache 2. 0 license and is available at: https://bitbucket. org/genomicepidemiology/resfinder/.
Datasets for validation ResFinder 4. 0 was validated with datasets consisting of MIC values (BMD or Etest, Table  1 ) and WGS data (Illumina sequencing) of Escherichia coli, Salmonella spp. , Campylobacter jejuni, E. faecium, E. faecalis and S. aureus of different origins (Table  1 ). These datasets represent a convenience sample. Phenotypic AST results were interpreted using the EUCAST epidemiological cut-off values (ECOFFs) to categorize isolates as WT (MIC ECOFF) and non-WT (MIC >ECOFF) (www. eucast. org). Exceptions were: (i) one S. aureus dataset for which phenotypic AST was performed by disc diffusion and interpreted by EUCAST clinical breakpoints (Table  1 ); and (ii) one E. coli dataset that consisted of Illumina WGS data only and MIC values were available for the data provider but not for the ResFinder 4.
0 developers, thus providing a blind test of the tool performance (Table  1 ). WGS data were obtained as raw reads and processed through a quality control (QC) pipeline as described here: https://bitbucket. org/genomicepidemiology/ foodqcpipeline/. In brief, reads were trimmed using bbduk2 (https://jgi. doe. gov/data-and-tools/bbtools/) to a phred score of 20, reads less than 50 bp were discarded, adapters were trimmed away and a draft de novo assembly was created using SPAdes. 21 From the assemblies, contigs below 500 bp were discarded. The most important parameters that were used to assess quality of sequencing data were: number of bases left after trimming, N50, number of contigs and total size of assembly. QC parameters used as guidelines were: read depth of at least 25%, N50 of >30 000 bp and a limit on the number of contigs to <500.
WGS data (FASTQ) were used as input for ResFinder 4. 0 using default parameters (80% identity over 60% of the length of the target gene) and also for SNP-based phylogenetic analysis as previously described  22  to verify the genetic diversity of the validation datasets. SNP analysis was not performed for the Salmonella spp. dataset whose diversity was already described previously. 23 The ResFinder 4. 0 output was analysed to define AMR genotypes, i. e. patterns of resistance determinants observed for each antimicrobial, in each dataset. Genotype-phenotype concordance was defined as presence or absence of a genetic determinant of resistance to a specific antimicrobial agent in non-WT (nWT) or WT isolates, respectively. Genotype-phenotype discordance was defined either as presence of a relevant AMR determinant in WT isolates or as absence of a relevant AMR determinant in nWT isolates.
All discordances were individually analysed. Sequence data that did not derive from previous studies (Table  1 ) have been deposited at NCBI (E. coli dataset from Germany: PRJNA616452; E. faecium dataset from Germany: PRJNA625631; E. faecium dataset from Belgium: PRJNA552025; S. aureus dataset from Belgium: PRJNA615176) and in the European Nucleotide Archive (S. aureus dataset from Denmark: PRJEB37586).
ResFinder 4. 0 databases The ResFinder 4. 0 databases validated in this study include 2690 AMR genes (ResFinder) and 266 resistance-mediating mutations in 39 selected genes (PointFinder), a genotype-to-phenotype translation database that includes 57 antimicrobial compounds (Table  S1 ) and in silico antimicrobial panels for eight species (Table  S2 ). Of note, a few antimicrobial agents included in the genotype-tophenotype translation database do not appear in the in silico antimicrobial panels. Thus the database can be used for any bacterial species and is not restricted to the species having assigned in silico antimicrobial panels. Furthermore, the in silico antimicrobial panels are limited to the antimicrobial agents for which the tool can actually provide an output. Hence, for example, antimicrobial agents for which the genetic bases of resistance have not been fully elucidated (e.
g. daptomycin in Enterococcus spp. , among others) are not included. The most updated versions of the databases are publicly available in BitBucket (https://bitbucket. org/genomicepidemiology/resfinder_db and https://bitbucket. org/genomicepidemiology/pointfinder_db). The databases are continuously updated relying both on users' feedback and on curators'/developers' own research. Importantly, the BitBucket repository allows access to outdated versions of the databases, which is particularly useful in case ResFinder 4. 0 is used within quality-assured procedures.
Datasets for ResFinder 4. 0 validation The SNP distances across strains used for ResFinder 4. 0 validation were in the range 1-48 102 for E. coli from Denmark and the UK, 1-48 066 for E. coli from Germany, 1-20 134 for C. jejuni, 1-7131 for E. faecium 15-17 067 for E. faecalis and 1-25 424 for S. aureus (Table  S3 ). The proportion of WT and nWT isolates in each dataset is presented in Tables  2  and 3.
Concordance between genotypic and phenotypic AST E. coli For the dataset deriving from Danish AMR surveillance in animals and meat, the overall genotype-phenotype concordance resulting from 1520 observations encompassing 16 antimicrobial agents was 97%, ranging from 71. 6% for cefepime to 100% for most antimicrobial agents (Table  2 ). ResFinder 4. 0 detected between 2 and 14 genotypes of resistance to most antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 99. 6% (586/588) of cases in which AMR determinants were detected and in 95. 3% (889/932) of cases in which no resistance determinant was detected. Discordances were caused by detection of sul2 in an isolate with the sulfamethoxazole MIC well below the ECOFF, and bla CTX-M-1 in an isolate with the ceftazidime MIC on the ECOFF (Figure  1 ).
Both genes were detected with a reliable read depth (see the   Discussion). Additional discordances were caused by lack of detection of AMR determinants in isolates with nWT phenotype for cefepime, cefoxitin, ciprofloxacin, ertapenem and nalidixic acid. Most of these isolates had the respective MIC one step dilution above the ECOFF (Figure  1  and Table  S5 ). For the dataset deriving from AMR surveillance in animals in the UK, the overall genotype-phenotype concordance for 890 observations encompassing nine antimicrobial agents was 98%, ranging from 90. 9% for nalidixic acid to 100% for colistin, gentamicin, imipenem and meropenem (Table  2 ). ResFinder 4. 0 detected between 2 and 17 genotypes of resistance to most antimicrobial agents, in addition to several cases in which no AMR determinant was detected (Table  S4 ).
Most (60%) of the (fluoro)quinolone nWT isolates had MICs one step dilution above the ECOFF (Figure  1  and Table  S5 ). For the dataset used for the blind test that comprised human and animal origin isolates from clinical and surveillance samples, the overall genotype-phenotype concordance resulting from 2559 observations encompassing 13 antimicrobial agents was 95. 3%, ranging from 54. 6% for ertapenem to 100% for ampicillin, cefepime, imipenem and meropenem (Table  2 ). ResFinder 4. 0 detected between 1 and 37 genotypes of resistance to most antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 99. 7% (1649/1654) of cases in which AMR determinants were detected and in 87. 3% (790/905) of cases in which no resistance determinant was detected (Table  S4 ).
Salmonella spp The overall genotype-phenotype concordance for 7489 observations encompassing seven antimicrobial agents was 98. 8%, ranging from 97. 0% for ciprofloxacin to 99. 7% for chloramphenicol (Table  2 ). ResFinder 4. 0 detected between 3 and 25 genotypes of resistance to the different antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 96. 3% (1220/1266) of cases in which AMR determinants were detected and in 99. 2% (6179/6223) of cases in which no resistance determinant was detected. Discordances were mainly due to detection of aac(6 0 )-Ib-cr with <100% identity to the database entry in ciprofloxacin WT isolates, whereas in the remaining cases the genotype-phenotype discordance was mainly observed in isolates with MIC values on the ECOFF (Figure  1 ).
E. faecium Of the two E. faecium datasets, the one from Germany included 363 observations encompassing eight antimicrobial agents and the one from Belgium included 159 observations encompassing five antimicrobial agents. In the first dataset, the overall genotype-phenotype concordance was 92. 8%, ranging from 64% for chloramphenicol to 100% for ampicillin, ciprofloxacin, erythromycin, gentamicin and vancomycin (Table  3 ). In this dataset, ResFinder 4. 0 detected between 1 and 11 genotypes for resistance to all antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 91. 0% (223/245) of cases in which AMR determinants were detected and in 96. 6% (114/118) of cases in which no resistance determinant was detected.
Most discordances were due to detection of poxtA, cat and fexB with <100% identity to the database entries and often with low read depth (Figure  1 ). Furthermore, tet(M) was detected with reliable read depth in an isolate with the tetracycline MIC on the ECOFF (Figure  1 ). Additional discordances were due to lack of detection of tetracycline and linezolid resistance determinants in isolates with the respective MICs 1-and 3-fold dilution above the ECOFF, respectively (Figure  1  and Table  S5 ). In the second dataset, the overall genotype-phenotype concordance was 96. 2%, ranging from 75% for gentamicin to 100% for ampicillin and ciprofloxacin (Table  3 ). In this dataset, ResFinder 4. 0 detected between two and six genotypes for resistance to all antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ).
E. faecalis For the E. faecalis dataset, the overall genotype-phenotype concordance resulting from 235 observations encompassing five antimicrobial agents was 97%, ranging from 96% for erythromycin and linezolid to 98% for tetracycline and vancomycin (Table  3 ). ResFinder 4. 0 detected between 1 and 17 genotypes of resistance to all antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 98. 6% (140/142) of cases in which AMR determinants were detected and in 94. 6% (88/93) of cases in which no resistance determinant was detected. Discordances were represented by isolates having erm(B) (n = 1) and tet(M) (n = 1) with 100% and <100% identity to the ResFinder 4. 0 database, respectively, and an MIC distant from the ECOFF (Figure  1 ).
S. aureus Of the two S. aureus datasets, the one from Belgium included 504 observations encompassing eight antimicrobial agents, whereas the one from Denmark included 598 observations encompassing six antimicrobial agents. For the first dataset, the overall genotype-phenotype concordance was 96%, ranging from 76. 2% for tetracycline to 100% for cefoxitin, ciprofloxacin, gentamicin, linezolid and vancomycin (Table  3 ). In this dataset, ResFinder 4. 0 detected between one and two genotypes of resistance to most antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 95. 1% (293/308) of cases in which AMR determinants were detected and in 97. 4% (191/196) of cases in which no resistance determinant was detected.
Most discordances were linked to detection of tet(M) with <100% identity to the database entry and in isolates with MICs on the ECOFF (Figure  1 ). Additional discordances were due to lack of detection of AMR determinants in isolates with clindamycin and erythromycin MICs notably higher than the ECOFF (Figure  1  and Table  S5 ). For the second dataset, the overall genotype-phenotype concordance was 97. 3%, ranging from 93. 9% for gentamicin to 100% for cefoxitin (Table  3 ). In this dataset, ResFinder 4. 0 detected between one and nine genotypes of resistance to most antimicrobial agents besides several cases in which no AMR determinant was detected (Table  S4 ). The predicted and observed AMR phenotypes matched in 94. 4% (205/217) of cases in which AMR determinants were detected and in 98.
Discussion We developed and implemented a freely accessible web server, a database and bioinformatics pipeline for detecting AMR genes and mutations and providing in silico prediction of expected phenotypes. ResFinder 4. 0 was validated for six bacterial species of major public health relevance and has been evaluated for M. tuberculosis previously. 17 ResFinder 4. 0 can also be used to predict phenotypes for any additional bacterial species by users with profound knowledge of AMR. Excellent concordance between genotypically predicted and phenotypically detected phenotypes was found for most antimicrobial-species combinations (Tables  2  and 3 ). In the vast majority of cases in which the automatically evaluated genotypephenotype concordance was <95%, we could identify issues related to ECOFFs and/or read depth of WGS data (Figure  1 ), indicating that phenotypic tests and suboptimal sequence quality, respectively, were likely sources of discrepancies.
12 Genotype-phenotype discordance might have also occurred due to inducible genes. For example, the S. aureus dataset from Denmark included erm(A)-positive isolates phenotypically classified as clindamycin susceptible (Tables  S4  and S5 ). erm(A) can be inducible, and mutations in the translational attenuator can occur very rapidly, leading to phenotypic resistance to all lincosamides and ketolides. 28 lso, occurrence of genes with <100% identity and/or coverage (in length) compared with the ResFinder 4. 0 database entries might have caused some genotype-phenotype discordances since genes that underwent mutations, insertions or deletions might have become silent. In addition, there are genes for which an nWT phenotype is linked to specific sequences only and will not be expressed if a mutation appears.
For example, only specific mutations in aac(6 0 )-Ib mediate fluoroquinolone resistance in E. coli and Salmonella,  29  which explains why aac(6')-Ib-cr with <100% identity to the database entry is detected in ciprofloxacin WT Salmonella spp. isolates (Table  S5 ). Nonetheless, in our datasets, the prediction of a resistance phenotype matched the measured nWT phenotype even in most of the cases presenting an AMR gene with <100% identity and/or coverage (in length) compared with the database entries (Tables  S4  and S5 ). From a surveillance perspective, detection of potentially silent genes is still more informative than the reporting of a WT phenotype. As silencing mutations can be transient,  30  also from a clinical perspective the finding of AMR genes may be even more relevant than the observed in vitro phenotype as it may be used as an early warning to avoid initiating inappropriate therapies.
In addition, the current turnaround time is an issue for most technologies. However, while this is the case for established clinical microbiological laboratories, it should also be mentioned that for laboratories in low-and middleincome countries that do not already have established laboratories it could potentially be an advantage to install a workflow based on WGS. 40 esFinder 4. 0 can be used with raw reads and with assembled sequences as inputs, and it performs database searches using Kmer-based mapping and BLAST, respectively. In this work, we presented the results obtained by inputting raw reads. As different processing of the same input sequences has been shown to lead to different results,  41  we also evaluated genotype-phenotype concordance using assembled sequences as input (data not shown).
Across 13 140 observations, which correspond to all datasets excluding the E. coli for the blind test, there were only 29 (0. 2%) cases in which results differed using FASTQ (raw reads) and FASTA (assembled sequences) files of the same sequence (data not shown). In most (20/29) cases, AMR genes were detected using FASTQ but not detected using FASTA, whereas the opposite applied in the remaining cases. In 14 and 15 cases, the phenotype predicted using the FASTQ and FASTA inputs was in agreement with the observed phenotype, respectively (data not shown). Based on this, it appears that the input file format does not affect the reliability of ResFinder 4. 0 results and, given that raw reads are processed considerably faster compared with assemblies, FASTQ files should be considered the preferable input for ResFinder 4.
0. The datasets available to us did not include an even distribution of WT and nWT phenotypes to all antimicrobial agents. This limitation highlights a need for making not only WGS data but also phenotypic test data as well as the bacterial isolates publicly available for those developing bioinformatics tools. 42 n conclusion, we implemented and evaluated ResFinder 4. 0, a bioinformatics tool whose unique feature is to provide a phenotype for each antimicrobial resistance gene detected and easily interpretable in silico antibiograms for eight bacterial species including both Gram-positive and Gram-negative bacteria of major public health relevance. The tool will be extended to allow for input of Nanopore sequence data and to include antibiograms of additional bacterial species. ResFinder 4.
Acknowledgements We thank  Kathrine Valentini Jensen  for her skilful assistance in formatting the databases. We also thank  Carola Fleige  and  Christine Gu  ¨nther from the  Robert Koch Institute (Germany) ,  G. Mourand  from  Anses Ploufragan (France) , and  Edyta Denis ,  Iwona Kania  and  Magdalena Staniak  from the  National Veterinary Research Institute (Poland)  for their excellent technical assistance in laboratory analyses.
Funding This work was supported by the  European Union   Horizon 2020 research and innovation programme  under grant agreement  643476  to the  COMPARE  project (http://www. compare-europe. eu),and The  Novo Nordisk Foundation  ( NNF16OC0021856 ) -Global Surveillance of Antimicrobial Resistance. The work by  L. Falgenhauer  and T. Chakraborty was supported by grants to the  German Center of Infection Research (DZIF) , and the  Zoonoses Network 'ESBL and fluoroquinolone resistance in Enterobacteriaceae (RESET)' Consortium  through the  German Federal Ministry of Education and Research (BMBF ; grant numbers  8000 701-3 [HZI], 01KI1313G ,  TI06. 001 ,  8032808811 ,  8032808818  and  8032808820  to T. C. ). Data for the E. faecium (dataset from Germany) and E. faecalis isolates were generated as part of a research project called  InfectControl2020-IRMRESS  funded by the  German BMBF  (grant no.
DK, Denmark; DE, Germany; FIN, Finland; FR, France; LU, Luxembourg; PL, Poland; PT, Portugal; BE, Belgium. a Dataset for blind test of ResFinder 4. 0 performance. Cefepime, chloramphenicol, ertapenem and nalidixic acid susceptibility testing were performed by Etest in a subset of isolates. All remaining AST were performed by BMD. b Phenotypic AST results were obtained by Etest. c Phenotypic AST results were obtained by disc diffusion.