dataesr/scientific-paragraphs-categorization huggingface dataset:
Allelic Differences at the Coregenome Scale (cgMLST) Allelic differences were computed with BioNumerics v. 7. 6. 3 software (Applied Maths, Sint-Martens-Latem, Belgium) using a combination of assembly-free and assembly-based allele calling. A similarity threshold of ≥85% was used for assembly-based calls and gapped alignments were allowed. The cgMLST Salmonella scheme integrated within the software consists of a total of 3 002 loci. The cgMLST was restricted to ≥80% homology in ≥95% of the isolates  (Vincent et al. , 2018). The matrices of pairwise allele differences were obtained with a scaling factor of 1 and a limit of differences ≤200. The alleles displaying discrepancy between the assembly-free and assembly-based analyses were excluded. Finally, the allelic differences were computed on 2 620 and 2 723 loci for S.
, 2016). Based on these draft genomes, pangenomes of both genome datasets were constructed with Roary  (Page et al. , 2015)  setting 95% of identity for blastp and a strict definition of the coregenome (i. e. , 100% of isolates with coregenes); the paralogs were kept for downstream analyses. The matrices of pairwise gene differences were produced with an in-house Python script called 'roary_to_pairwise. '.
kmer Differences Using the genome assemblies obtained as described above, an in-house Python workflow called QuickPhylo was developed in order to produce matrices of pairwise kmer differences based on a form of locality-sensitive hashing called MinHash  (Indyk and Motwani, 1998)  implemented in Mash  (Ondov et al. , 2016). More precisely, Mash is run for each genome against a sketch including all the studied genomes, and the shared hashes produced are retained to create matrices of pairwise kmer differences, setting Mash with 1 000 selected kmers of 15 bases in order to perform a fast (i. e. , 1 000 kmers in the sketch) and discriminant computing (i. e. , smallest bounded error with kmers of 15 bases according to simulated data representative of the genome size of S. enterica), respectively.
Allelic Difference at the Pangenome Scale (wgMLST) Allelic differences were computed according to the wgMLST scheme with the BioNumerics v. 7. 6. 3 software (Applied Maths, Sint-Martens-Latem, Belgium) as explained above. The wgMLST Salmonella scheme integrated within the software consists of a total of 15 874 loci. The matrices of pairwise allele differences were computed on 3 530 and 3 698 loci for S. Typhimurium and its monophasic variant S. 1,4,[5],12:i:-, respectively.
e. , D in Figure  2A ) regarding the considered genomic features (i. e. , SNPs-1, SNPs-2, accessory genes, kmers, cgMLST, or wgMLST alleles). The third step is a computation step that divides each pairwise difference matrix of interest into two lists of pairwise differences, which are then compared by three non-parametric tests based on the R script 'matrix2association' (i. e. , p n in Figure  2A ). The first list corresponds to pairwise differences existing across genomes known to be involved in the outbreak. The second list corresponds to pairwise differences existing between these outbreak genomes and the tested genome. With the hypothesis that the tested genome is related to the outbreak of interest (i. e. , null hypothesis H0: absence of significant differences), this script estimates statistical differences between these two lists of pairwise differences.
Both lists are compared with the three non-parametric tests in order to assign (i. e. , H0 conserved), or not (i. e. , H0 rejected), the tested genome to the outbreak of interest (Figure  2B ). The non-parametric two-sample WS, KS, and KW [i. e. , R Stats package (R Development Core  Team, 2015) ] tests assess the statistical differences of median values, distributions and mean ranks, respectively (Figure  2B ). These non-parametric tests were selected because the distributions and equality of variances were not known. In practice, two groups of outbreak (i. e. , positive control: C+) and non-outbreak (i. e. , negative control: C-) controls were formed for each outbreak and tested in a pairwise manner using these non-parametric tests against a third group representative of samples involved in the studied FBOs (i.
Phylogenomic Inference Phylogenomic inferences were performed by maximum likelihood based on pseudogenomes produced by the iVARCall2 workflow and the general time-reversible (GTR) model implemented in the RaxML program  (Stamatakis, 2014). In addition to the nucleotide substitution model (GTR) and the secondary structure 16-state model, models describing rate variation among sites were also applied. Gamma distribution (G) and convergences of the phylogenomic inferences were checked based on rapid bootstrap analysis  (Stamatakis et al. , 2008). The phylogenomic inferences and annotations were graphically represented with ggtree R package  (Yu et al. , 2017). FIGURE 2 | Workflows implementing non-parametric tests to assess statistical differences of pairwise differences of genomic features at the core (i.
e. , approaches 'SNPs-1,' 'SNPs-2,' and 'cgMLST'), accessory (i. e. , approaches 'genes') and pangenome (i. e. , approaches 'kmers' and 'wgMLST') scales in order to investigate food poisoning outbreaks (A) and statistical approaches implementing non-parametric tests (i. e. , WS, KS, and KW) comparing pairwise differences of genomic features during a foodborne outbreak (FBO) investigation (B). The approaches 'SNPs-1,' 'SNPs-2,' 'genes,' 'kmers,' 'cgMLST,' and 'wgMLST' were performed with the workflows iVARCall2 with or without ClonalFrameML, ARTWork-Roary, ARTWork-QuickPhylo, and BioNumerics (Applied Maths), respectively. The 'SNPs' approaches were performed including (i. e. , 'SNPs-1') or excluding (i. e. , 'SNPs-2') SNPs from recombination events identified with ClonalFrameML. The R script 'matrix2association' estimates statistical differences between two lists of pairwise differences existing across genomes known to be involved in a studied outbreak (i.
Assessment of Genomic Data Quality The results of non-parametric approaches are supported by the good quality of the mapping and assembly of pseudo-and draft-genomes (Supplementary Data S3A and Supplementary Table  S2 ). The presence of exogenous DNA was assessed based on the cumulated size of scaffolds, GC content, genome fraction, gene content as well as the logarithmic and hyperbolic forms of the curves representing the new and conserved genes according to the sizes of genome datasets, respectively (Supplementary Data S3 and Supplementary Table  S2 ). One sample of S. Typhimurium 10CEB498SAL appeared as potentially contaminated (i. e. , total length of 6. 26 Mb) and was deliberately not excluded from the study in order to demonstrate that the non-parametric approaches applied on pairwise differences of genomic features provide robust results independently of all the other tested genomes.
In summary, both genome datasets of S. Typhimurium and S. 1,4,[5],12:i:presented similar pangenome constitutions (Table  1 ). Evaluation of the Considered Genomic Features to Distinguish Outbreak (i. e. , Positive Control: C+ and Non-outbreak (i. e. , Negative Control: C-) Controls To assess the value of the non-parametric statistical approaches for FBO analysis, we built four datasets corresponding to four FBOs that took place in France between 2010 and 2014 (Figure  1  and Supplementary Data S2). For each FBO, our approach consisted of building two groups of C+ and C-isolated in the same period of time and comparing them to a set of strains involved in FBOs of interest (TS) (Figure  1 ). For each genomic feature (i. e. , 'SNPs-1, ' 'SNP-2, ' 'genes, ' 'kmers, ' 'cgMLST, ' and 'wgMLST'), matrices of pairwise differences were obtained including all isolates, and the R script 'matrix2association' was run to extract lists of pairwise distances and evaluate the genetic relatedness (Figure  2 ) existing between genomes from TS and these genomes against each tested genome from the C+ and C-(Figure  3 ).
e. , 345) were successfully assigned as C+ and C-with the SNP-based FIGURE 3 | Negative common logarithms of p-values from non-parametric tests: Kolmogorov-Smirnov assessing statistical differences of pairwise differences of genomic features at the core (i. e. , approaches 'SNPs' and 'cgMLST'), accessory (i. e. , approaches 'genes') and pangenome (i. e. , approaches 'kmers' and 'wgMLST') scales in order to investigate food poisoning outbreaks of 192 S. Typhimurium (i. e. , outbreaks #1 and #2; n = 66) and S. 1,4,[5],12:i:-(i. e. , outbreaks #3 and #4; n = 126). The 'SNPs' approaches were performed including (i. e. , 'SNPs-1') or excluding (i. e. , 'SNPs-2') SNPs from recombination events identified with ClonalFrameML. The R script 'matrix2association' estimates statistical differences between two lists of pairwise differences existing across all genomes known to be involved in a studied outbreak (i.
Effect of the Recombination Events on the SNP-Based Non-parametric Approaches Homologous recombination events may increase the number of SNPs in the impacted genomic regions. This phenomenon may thus shift the distributions of pairwise SNP differences and hinder the non-parametric comparisons of pairwise SNP differences. In order to assess the impact of homologous recombination events, we performed the SNP-based nonparametric approaches including (i. e. , 'SNP-1') or excluding (i. e. , 'SNP-2') the recombination events. Overall, 13 and four recombination events were detected with ClonalFrameML  (Didelot and Wilson, 2015)  across genomes of S. Typhimurium (i. e. , ranging from 404 to 1 194 bp) and its monophasic variant (i. e. , ranging from 532 to 14 597 bp), respectively. Starting with SNP datasets of 4 818 for S.
Typhimurium and 3 204 for its monophasic variant, the exclusion of recombination events led to datasets of 4 797 and 3 154 SNPs. The SNP-based non-parametric approaches provide an accurate assignment of all tested genomes (i. e. , 345) to C+ and C-with either SNPs-1 or SNPs-2 genomic features (Supplementary Data S4). In summary, the method was not impacted by the presence of recombination events in our dataset of genomes.
While running the R script 'matrix2association, ' the non-parametric test outcome is almost instantaneous. For instance, our Linux network is constituted of 43TB for storage and has 240 threads distributed across five servers for computing power. This Linux network allows the execution of assembly (i. e. , ARTWork) and variant calling (iVARCall2) for 96 Salmonella genomes in around 400 min. Both assembly and variant calling represent a similar duration of execution. Our results also show that the non-parametric tests cannot confidently distinguish between outbreak C+ and C-controls when the 'genes, ' 'kmers, ' or 'wgMLST' genomic features were used, while the 'cgMLST' genomic feature combined with the non-parametric tests allowed for the accurate assignation of controls when TS contained at least six genomes (i.
e. , outbreak#2, #3, and #4). By contrast, the SNP genomic features joined to these non-parametric tests were successful even when TS contains only four genomes (outbreak#1), as was also supported by several trials of randomly selected genomes in TS (Supplementary Data S5). This lower discriminatory power of the cgMLST compared to the SNP genomic feature might be due to the fact that the SNP genomic feature includes intergenic and intragenic core variants  (Felten et al. , 2017) , whereas the cgMLST only integrates core alleles defined from coding sequences  (Pearce et al. , 2018). To date, our conclusions are supported by the datasets of samples tested in this study. Other datasets will have to be analyzed in order to generalize the method we adopted in this study. It is therefore important that genomic outbreak reference datasets grow in volume and diversity  (Timme et al.
e. , evolution rate and/or outbreak duration) because it estimates the statistical differences of pairwise differences existing between genomes from the outbreak test set [TS against TS] and pairwise differences existing between these genomes and a tested genome (i. e. ,  [TS against C+]  or [TS against C-]). That is, these parameters are sampled, represented and considered within the dataset. If both lists of pairwise differences increase proportionally because of the evolutionary rate or FBO duration (i. e. , [TS against TS] and [TS against C+]), the non-parametric test would be able to correctly conserve (i. e. , absence of differences: [TS against TS] e. , differences of median values), KS (i. e. , differences in distributions) and KW (i. e. , differences of mean ranks) aiming to access statistical differences of pairwise SNP differences (i.
e. , approaches 'SNPs-1' including recombination events). The R script 'matrix2association' estimates statistical differences between two lists of pairwise differences existing across all genomes known to be involved in a studied outbreak (i. e. , outbreak test set: TS) and between these genomes and a tested genome (i. e. , outbreak control C+ or non-outbreak control C-) in order to assign (i. e. , absence of statistical differences: H0 conserved), or not (i. e. , presence of statistical differences: H0 rejected), this tested genome to the outbreak of interest. The SNPs were identified by the workflow 'iVARCall2' against the reference genome S. Typhimurium LT2 (NCBI NC_003197. 1). The produced pseudogenomes (4,857,432 bp) were inferred using the program 'RaxML' based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model.
, 2011). This recommendation also applies to the non-parametric approach; if recombination events only appear in a C+ genome the likelihood of wrong assignment to Cwould increase, and the method could fail to assign this sample to the TS. Although the Salmonella datasets and statistical approaches used in the present study are relatively insensitive to recombination events, we recommend that recombination events from the SNP dataset are excluded to avoid theoretically spurious assignments.
What's Next? Although transmission dynamics of several outbreaks were successfully solved thanks to high-resolution genomics, the contemporary challenge is to describe ongoing outbreaks in real time based on genomic epidemiology and to lead safety authorities and public health decision makers to consider the implementation of automated and integrated genomic systems  (Tang and Gardy, 2014). Many initiatives are moving in this direction. The Pathogen detection browser of the GenomeTrakr international genomic reference database of foodborne pathogens from food and environmental isolates provides a cluster analysis on a daily basis  (Timme et al. , 2019) , as it is also the case for PulseNet International network dedicated to laboratory-based surveillance for food-borne pathogens  (Nadon et al.
e. , differences of mean ranks) assessing statistical differences of pairwise differences of genomic features at the core (i. e. , approaches 'SNPs-1,' 'SNPs-2,' and 'cgMLST'), accessory (i. e. , approaches 'genes') and pangenome (i. e. , approaches 'kmers' and 'wgMLST') scales in order to investigate food poisoning outbreaks of 192 S. Typhimurium (i. e. , outbreaks #1 and #2; n = 66) and S. 1,4,[5],12:i:-(i. e. , outbreaks #3 and #4; n = 126). The 'SNP' approaches were performed including (i. e. , 'SNPs-1') or excluding (i. e. , 'SNPs-2') SNPs from recombination events identified with ClonalFrameML. The R script 'matrix2association' estimates statistical differences between two lists of pairwise differences existing across all genomes known to be involved in a studied outbreak (i. e. , outbreak tested set: TS) and between these genomes and a tested genome (i.
e. , outbreak control C+ or non-outbreak control C-) in order to assign (i. e. , absence of statistical differences: H0 conserved), or not (i. e. , presence of statistical differences: H0 rejected), this tested genome to the outbreak of interest. The SNPs were identified by the workflow 'iVARCall2' against the reference genome S. Typhimurium LT2 (NCBI NC_003197. 1). The produced pseudogenomes (4,857,432 bp) were inferred using the program 'RaxML' based on a bootstrap analysis and search for best-scoring Maximum Likelihood tree with General Time-Reversible model of substitution and the secondary structure 16-state model. Bootstraps higher than 80% are represented at each node.
ACKNOWLEDGMENTS We thank  Pierre-Yves Letournel  and  Thomas Texier  ( ANSES ) for providing high-performance computing resources. We also thank the  National Reference Center for Salmonella (NRC) , the  ANSES Salmonella Network  and the  "Direction générale de l 'alimentation" as part of the  French Ministry of Agriculture, Food and Forestry  who provided genomic data and isolates.
DATA AVAILABILITY STATEMENT The new scripts developed from the present study for genomic and phylogenomic analyses can be found in the following repositories: https://github. com/afelten-Anses/ARtWORK; https://github. com/afelten-Anses/QuickPhylo. The statistical approach developed is available in the https://github. com/ lguillier/matrix2association repository. The previously developed script called iVARCall2  (Felten et al. , 2017)  can be downloaded from the repository https://github. com/afelten-Anses/VARtools/tree/master/iVARCall2 and the corresponding open source package is now available in Conda (https://anaconda. org/itsmeludo/repo and https://conda. io/docs/). Sequencing data is available in the BioProject PRJEB30613 of the European Nucleotide Archive (ENA) (https://www. ebi. ac. uk/ena/data/view/PRJEB30613).
ZINB-WaVE  [5]  and scVI  [6]  instead both rely on a zero-inflated negative binomial (ZINB) distribution to model the observed counts. Under the ZINB distribution, zeros can be attributed to either the limited sampling effects (NB) or to "surprising" zeros (ZI) which are not accounted for by NB. Indeed, it has previously demonstrated that under the scVI model, zeros attributable to the NB component better reflect the limitation in mRNA capture efficiency whereas the ZI component has a stronger association with the extent of read alignment errors  [6]. A recent study  [7]  however questioned the universality of zero-inflation added to NB models for scRNA-seq data. This analysis was primarily based on datasets of "negative controls" (e. g. , ERCC spike-ins) -namely exogenous transcripts that are constitutively expressed, thus reducing the contribution of biological factors as a cause for zero measurements.
On simulated datasets, AutoZI outperforms other approaches in identifying zero-inflation (Section 4). On real datasets, AutoZI labels as zero-inflated only a small fraction of the negative controls (spike ins and control RNAs) while doing so for larger fractions of the endogenous genes, although this fraction tends to decrease with technical improvements in library preparation protocols (Section 5). In an application of AutoZI to mouse embryonic stem cells (mESC), we find that it is capable of distinguishing genes with a likely bursty promoter kinetics, labeling them as ZI. Such results suggest that : (i) negative binomial may not be globally appropriate for scRNA-seq data and that zero-inflation might be required to ensure a good fit for a significant fraction of the genes, and (ii) the patterns of genes detected as zero-inflated may be interpreted from both biological and technical perspectives.
Performance benchmarks on simulated datasets We chose τ dropout = 0. 01 in all our experiments, as this value brought stable and balanced results on simulated datasets. As a consequence of AutoZI's lower flexibility, we usually observed lower marginal log-likelihoods for AutoZI than for scVI. In this manuscript, we therefore focus on the performance of AutoZI at detecting zero-inflated genes. The ground truth for zero-inflation is unknown and not available for real datasets. Indeed, this is an open research topic. Consequently, we turned to simulations using Poisson log-normal distributions with added zero-inflation as well as Symsim  [8] , a realistic simulator for scRNA-seq data relying on Beta-Poisson distributions. For both simulation frameworks, we benchmarked AutoZI against two decision rules based on maximum likelihood estimations: Uni-MLE-Pi and Uni-MLE-LRT.
The former fits a univariate ZINB distribution to all genes and uses the fitted zero-inflation weight based on an arbitrary threshold to make a decision. The latter fits both a univariate ZINB and NB distribution and uses the differences in marginal likelihood ∆ = LL ZINB -LL NB to perform decision. Examples of thresholds include AIC (resp. BIC) for selecting ZINB if and only if ∆ ≥ 1 (resp. ∆ ≥ log(N ) /2, where N is the size of the dataset). For AutoZI, we used the default hyperparameters from the scVI model and used the natural decision threshold at 0. 5, denoted as "AutoZI default". Poisson log-normal datasets Each synthetic dataset contains 12,000 cells, 50 genes, and two cell types. Each cell has its gene expression drawn from a Poisson log-normal distribution whose mean and covariance matrix depend only on the cell type.
A fraction λ of the genes are selected uniformly to be applied a Bernoulli mask with probability 0. 1. We aggregate the results for λ ∈ {0, 0. 25, 0. 5, 0. 75, 1} and report the ROC curves in Figure  1. The AutoZI model has a near-one AUC and has a similar albeit slightly lower performance. In particular, AutoZI's default decision rule leads to true positive (ZINB) and negative (NB) rates both superior to 94% whereas the AIC and the BIC do not yield false positives but their true positive rate is lower (85. 5%), showing a bias in favor of the NB distribution. The Uni-MLE-Pi baseline procedure performs worse than these two models. Beta-Poisson datasets For a more biologically relevant simulation framework, we used known kinetic models of stochastic gene expression such as the Beta-Poisson model.
SymSim  [8]  provides a natural way of sampling data from such models and adding technical noise. SymSim first randomly samples the promoter on rate (k on ), off rate (k off ) and synthesis rate (s) for each gene, and then generates simulated "true" counts using a Beta-Poisson distribution. As a simplification, we label the genes with kinetic k on , k off < 1 as bimodal, k on 1 as unimodal with a non-zero mode (UNZ) and the rest as unimodal zero (UZ). The real regimes might depend additionally on s and kon /k off but are negligible as a first analysis. True counts are then converted to observed counts by simulating processes such as capture, amplification and fragmentation (with UMIs in these datasets). We create a dataset of 100 genes with a bimodal distribution and another dataset of 100 genes with a UNZ distribution.
Both datasets are subsampled multiple times (n bimodal genes and 100 -n UNZ genes, for n ∈ {0, 25, 50, 75, 100}) to create a sequence of datasets of 100 genes and 3,000 cells from a single cell type. We expect UNZ genes to be non zero-inflated, as these can be easily modeled by a NB distribution. Conversely, we expect genes that are bimodal in their true counts to be zero-inflated in their observed counts, due to limited sensitivity. For simplicity, we did not focus on UZ genes since their ground-truth category is not clear. We aggregate the results and report the ROC curves in Figure  2. AutoZI has the best area under the curve and its default decision rule classifies more than 96% of bimodal genes as ZINB and more than 99% of the UNZ genes as NB. Such results show that zero-inflation in our model might be suited to distinguish regimes of bimodality for kinetics of gene expression.