dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural Characterizations Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES, Crealins, Lyon, France) analysis was performed to determine the effective cerium concentration, x eff , in the nanopowders. Powder X-ray diffraction patterns have been recorded on a Siemens D8 Advance diffractometer (λ Cu 1. 54056 Å). Le Bail fits were carried using WinPLOTR software. Transmission electron microscopy (TEM) images were recorded using a Philips CM300 microscope, operating at 300 kV and equipped with a TemCam F416 TVIPS camera. Scanning Transmission electron microscopy (STEM) analyses were performed using a Cs-corrected FEI-Titan Themis microscope, operating at 200 kV and allowing high angle annular dark field (HAADF) imaging and subsequent energydispersive X-ray (EDX) chemical mapping.
Pre-defined dilutions of K562 cells in HL60 cells were then prepared and freeze dried in 3-ml glass ampoules to contain 9 9 10 6 cells. Samples were dried for 24 h. Prior to distribution, to establish that the manufactured samples accurately represented the target values, and to ensure sample quality and homogeneity, a minimum of three selected samples were subjected to (phenol chloroform) RNA extraction, complementary DNA (cDNA) synthesis and BCR-ABL1 quantification. This was performed using the Qiagen Ipsogen BCR-ABL1 Mbcr kit (Qiagen, Hilden, Germany) and the Applied Biosystems 7500 RQ-PCR machine (Thermo Fisher) and the QXDX BCR-ABL %IS kit (Bio-Rad, Hercules, CA, USA) and QX200 Auto DG system (Bio-Rad). Data analysis was performed in line with UK recommendations. 17 Sample quality was defined as RNA OD260/280 ratio of between 1Á8 and 2Á2 and ABL1 levels >100 000/replicate.
All participants in the study were asked to extract RNA and perform reverse transcription using their standard in-house protocol and test the samples with the QXDX BCR-ABL %IS kit, using either the QX200 auto or manual DG system. Results from the QXDX BCR-ABL %IS kit are reported on the IS by using an assay specific conversion factor determined by comparing the assay to an IS reference value. Samples A-G used two reaction wells. Samples H-J used four reaction wells. Participants were asked to independently analyse RTddPCR data using the QuantaSoft (version 1. 7. 4) and QXDx Reporter Tool software. Where participants were currently reporting BCR-ABL1 quantification results using RTqPCR, all study samples were run using local methodology for processing and analysis for comparison to the RTddPCR results (n = 11).
Final BCR-ABL IS levels for each sample were then calculated and reported independently by each participant (along with methodological information) using a dedicated online data entry page. Method averages were calculated using an arithmetic mean and significance calculated using a two-tailed ttest. A P < 0Á05 was defined as statistically significant. Methodological variance was calculated using coefficient of variation (CV) with an F-test for two-sample variance used to calculate significance in Microsoft Excel. A P < 0Á05 was defined as statistically significant. Assay linearity was calculated using a simple linear regression (R 2 ) in GraphPad Prism (version 8Á3. 1). R 2 > 0Á99 was deemed satisfactory. A Fisher r-to-z transformation was used to create a z value that was used to assess the significance of difference between the two correlation coefficients.
Overall, 20/23 laboratories had a satisfactory assay linearity with R 2 > 0Á99. In total, 11 participants performed both RTddPCR and RTqPCR. When the average percentage BCR-ABL IS results reported by participants using RTqPCR was compared to the average percentage BCR-ABL IS result for participants using RTddPCR, the results were seen to be comparable, with no statistically significant difference demonstrated (Table  II ). There was no statistically significant difference seen in the median ABL1 control gene levels calculated from participant's RTddPCR data compared to RTqPCR (Table  SIV ; Table  SV ). When detections rates were compared for participants who performed both RTddPCR and RTqPCR, all participants detected BCR-ABL1 in all samples down to MR 4Á0 (0Á01% BCR-ABL IS )-, using both techniques.
This reproduces the improved accuracy of RTddPCR shown in single-centre studies,  [12] [13] [14] [15]  while additionally demonstrating the robustness of the approach when applied to a diverse range of laboratories (research, clinical, translational) in a wide variety of countries. However, with only 11 laboratories testing both RTqPCR and RTddPCR, this data will need to be replicated in a larger dataset using a more diverse range of digital PCR approaches. The impact RNA extraction and cDNA synthesis diversity on RTddPCR variability was not assessed in the present study and should be the focus of future standardisation projects. The cost of Bio-Rad's QXDX BCR-ABL %IS kit is similar to the automated Cepheid Xpert â cartridge-based systems, but is more expensive than conventional laboratory developed RTqPCR tests, especially when the extra wells for deepresponse detection are considered.
Acknowledgments This work was supported by an unrestricted educational grant from  Bio-Rad  who additionally provided RTddPCR reagents. The supporters did not have any role in the study design or analysis of data or writing in the present work. We thank  Adam Corner  and the team at  Bio-Rad  for technical advice provided over the course of the study. Stuart Scott designed the research study, analysed the data and wrote the paper. Ashley Cartwright analysed the data and reviewed the manuscript. Sebastian Francis  wrote the clinical aspects of the discussion and reviewed the manuscript. Andrew Chantry and Liam Whitby reviewed the manuscript. Prasanthi Bhagavatula  tested and analysed the study samples. All other participants tested and analysed the study samples and reviewed the manuscript.
The recent years have seen the rise of pangenomes as comparative genomic tools to better understand the evolution of gene content among microbial genomes in close phylogenetic groups such as species. While the core or persistent genome is often well-known as it includes essential or ubiquitous genes, the variable genome is usually less characterized and includes many genes with unknown functions even among the most studied organisms. It gathers important genes for strain adaptation that are acquired by horizontal gene transfer. Here, we introduce panModule, an original method to identify conserved modules in pangenome graphs built from thousands of microbial genomes. These modules correspond to synteny blocks composed of consecutive genes that are conserved in a subset of the compared strains.
Identifying conserved modules can provide insights on genes involved in the same functional processes, and as such is a very helpful tool to facilitate the understanding of genomic regions with complex evolutionary histories. The panModule method was benchmarked on a curated dataset of conserved modules in Escherichia coli genomes. Its use was illustrated through a study of a high pathogenicity island in Klebsiella pneumoniae that allowed a better understanding of this region. panModule is freely available and accessible through the PPanGGOLiN software suite (https://github. com/labgem/PPanGGOLiN).
Introduction Lately, the data deluge provided by NGS has given access to over a million of prokaryotic genome sequences in public data banks, as well as a wealth of genomes reconstructed from environmental data, such as metagenome assembled genomes (MAGs) or single-cell assembled genomes (SAGs). Consequently, many bacterial species of interest now have hundreds to several thousands of genomes publicly available. It represents a fantastic opportunity to understand the evolution of prokaryotic genomes, and more specifically to study gene flow within a given species. However, processing such a huge amount of data for comparative genomics analyses is becoming a real challenge and requires new bioinformatics solutions. In particular, many new methods rely on the conceptual framework of the pangenome, which corresponds to the entire gene repertoire of a taxonomic group  [1].
Other approaches that do not rely on synteny conservation but on the cooccurrence or coevolution of genes in a pangenome to infer functional associations exist as well. Pantagruel  [23]  or Coinfinder  [24]  are recent examples designed for bacterial pangenomes. However, their goal is to infer associations or dissociations between pairs of genes, and they do not attempt to refine those relations into groups of colocalized genes directly. In this article, we propose a new method called panModule to detect modules in GIs based on the pangenome graph representation of PPanGGOLiN  [5]. In this graph, nodes represent gene families and edges represent genetic contiguity. In PPanGGOLiN, families are classified by a statistical model into a tri-partition scheme as introduced by  [25] : (i) the persistent genome, which corresponds to genes that are present in most individuals of the studied clade, (ii) the shell genome, which groups genes that are conserved between some individuals of the group but not most and (iii) the cloud genome, which corresponds to genes that are rare within the population and found only in one or a few individuals.
To predict modules, the panModule algorithm detects sets of co-occurring and colocalized genes in the variable part of the pangenome graph, composed of shell and cloud families. The predictions of panModule were evaluated at the species level using a curated dataset of modules in 12 genomes of Escherichia coli previously analyzed  [15]. We then illustrate our approach by predicting modules in a set of complete Klebsiella pneumoniae genomes and comparing those predictions with formerly studied modules in a high pathogenicity island (HPI) of a strain of medical interest  [17]. The panModule method is integrated in the PPanGGOLiN software suite (https://github. com/labgem/PPanGGOLiN). Modules can be predicted on pangenomes made of thousands of genomes and be analyzed along with the GIs and integration spot results from the previously published panRGP method  [26].
Module detection algorithm The panModule method uses the pangenome graph representation of PPanG-GOLiN  [5]  in which nodes correspond to homologous gene families (classified into persistent, shell and cloud partitions) and edges represent genetic contiguity (two families are linked in the graph if they contain genes that are neighbors in the genomes). Modules are defined as non-overlapping sets of cooccurring and colocalized variable gene families (i. e. , shell and cloud families) that correspond to connected components in the pangenome graph. The graph algorithm behind panModule is inspired from a previously published method  [27]  that merges information from two or more graphs in a multigraph and then detects common connected components. These components correspond to conserved synteny groups, where the input graphs are genomes.
In panModule, we do not use a multigraph representation but directly the pangenome graph to detect synteny conservation and thus modules in the variable genome. First, each annotated genome sequence (i. e. complete genome sequences or contigs) is read to obtain a genome graph, which is a linear graph of genes that is cyclic in case of complete sequences of circular plasmids or chromosomes. Then, the pangenome graph G(V, E) is built from all genome graphs where V is a set of vertices corresponding to gene families and E is a set of edges representing genetic contiguity in the genome graphs between those gene families. An edge e i,j is added between each pair of gene families (v i , v j ) if their corresponding genes are separated by less than t genes on a genome graph. For t 1, it corresponds to a transitive closure applied to the genome graphs and allows to connect two families even if their genes are not directly adjacent.
After this filtering step, each connected component is extracted using a modified Breadth-First Search (BFS) algorithm. A connected component is considered as a predicted module if it contains at least 3 nodes made of the shell, cloud or multigenic persistent families according to PPanGGOLiN classification  [5]. Indeed, modules containing non-multigenic persistent families are not considered because they are not part of the variable genome and generally correspond to long syntenic regions that are conserved in almost all compared genomes without any or only a few rearrangement events.
Reference dataset and genome collections To evaluate panModule predictions, we used a reference dataset of modules based on the expert annotation of 12 E. coli complete genomes originally published in  [15]. These 12 strains are from different phylogroups (i. e. A, B1, B2 and D) and are commensal or ExPEC (Extra-intestinal Pathogenic Escherichia coli ). Their genomes have been curated with the MicroScope platform  [28]  and their GIs have been divided into modules according to both synteny conservation and functional annotation of the genes. This dataset, named EcoliRef, contains a total of 165 modules that are present in at least 2, and not more than 10 of the 12 genomes, for a total of 793 occurrences in 461 GIs. GIs and modules are described by their genomic coordinates on the 12 E. coli chromosome sequences and were classified into 7 functional categories (Supplementary Data file 'benchmark/reference modules.
tsv'). Module prediction was run on 4 collections of E. coli genomes. The first one corresponds to the 12 genomes of the EcoliRef reference dataset. The second one contains all 1671 E. coli genomes classified as 'Complete' or 'Chromosome' in NCBI RefSeq  [29]  (downloaded the 1st of March 2021 and listed in Supplementary Data file 'benchmark/EcoliComplete genomes. list'). This dataset will be thereafter called EcoliComplete and includes the 12 genomes of EcoliRef. The third collection, named EcoliContigs, includes 1659 unfinished genomes plus the 12 genomes of EcoliRef. All E. coli genomes classified as 'Contigs' or 'Scaffold' in NCBI RefSeq (downloaded the 22nd of June 2021) were compared to all EcoliComplete genomes (apart from the 12 genomes of EcoliRef) with Mash (version 2. 1. 1, parameters: -s 5000, default for the others)  [30]  to iteratively pick out the closest genome in contigs (Supplementary Data file 'benchmark/EcoliContigs genomes.
list'). As such, we get an equivalent bias in genome composition between both datasets to compare them properly and thus evaluate the impact of using genomes in contigs for module prediction. The last collection, named EcoliMAGs, contains 1 416 MAGs classified as 'Escherichia coli ' from the study of Pasolli et al. [31]  plus the 12 genomes of EcoliRef. MAGs were annotated using bakta (version 1. 0. 3, default parameters)  [32]. As this dataset is much smaller than NCBI RefSeq, we chose not to apply the same filters as for the EcoliContigs dataset and just kept all MAGs. Therefore, we have here a collection of incomplete and fragmented E. coli genomes whose diversity is different from the two previous ones. Indeed, it contains potentially fewer pathogenic strains because MAGs were obtained from metagenomic samples not involving patients with an E.
coli infections. To illustrate the use of panModule on another species, a dataset containing 566 complete genomes classified as 's Klebsiella pneumoniae' in GTDB (release 06-RS202)  [33]  was downloaded on the 3rd of May 2021 from GenBank and NCBI RefSeq (listed in Supplementary Data file 'Klebsiella pneumoniae/Klebsiella pneumoniae genomes. list'). Then the modules were predicted using the method described in 2. 1 with parameters t = 4, m = 2 and s = 0. 86. The GIs of interest were identified by searching for those with positions overlapping with KPHPI208 in Klebsiella pneumoniae 1084. Annotations considered in the analysis are those from the downloaded file of NCBI RefSeq. Genomic region illustrations were obtained using an online version of the CGView software  [34]  available at https://beta.
Module prediction and benchmark procedure The panModule method was run on the four E. coli genome collections using PPanGGOLiN software version 1. 2. 0 with default parameters to obtain homologous gene families and pangenome graphs. Module prediction was evaluated at the genome level using the reference modules from the 12 genomes of the EcoliRef dataset. In a GI of a given genome, a set of genes is considered to be part of a predicted module if their corresponding families are members of the same module. The benchmark uses genomic positions and processes GIs one by one with the following assumptions: (i) pairs of genes located between the genomic positions of a reference module are considered as positive relations (ii) pairs of genes that do not belong to the same reference module but are in the same GI are considered as negative relations.
Thus, pairs of genes in a GI that are in the same predicted module and the same reference module are True Positives (TP). Pairs of genes that are in different predicted modules or not in a predicted module but in the same reference module are False Negatives (FN). Pairs of genes that are in the same predicted module but in different reference modules or not in a reference module are False Positives (FP). Pairs of genes that are in different predicted modules or not in a predicted module and in different reference modules or not in a reference module are True Negatives (TN). To evaluate module prediction, Matthew's correlation coefficient (MCC), F1-score, accuracy, precision and recall values were computed for the 4 E. coli genome collections as follows: In order to determine the best values for the s, m and t parameters of panModule, we evaluated the predictions on the EcoliRef dataset using a set of realistic value combinations and chose the one that gives the best MCC (Supplementary Data file 'benchmark/EcoliScope benchmark metrics.
Benchmark results To evaluate panModule, we ran the benchmark as described in the Materials and Methods section to see how it performed against a curated set of functional modules. The best set of parameters was estimated on the EcoliRef dataset, and applied on the other datasets as such: m = 2, t = 4 and s = 0. 86. A summary of the benchmark results for each dataset with these parameters is available in Table  1. It is possible that other parameters yield better results, which we will discuss with the case of the EcoliMAGs dataset. The figure  1  represents the modules predicted with the different datasets on a plastic region of the genome of Escherichia coli 536 with the set of parameters previously mentioned. Overall, the modules detected by our approach in the EcoliRef dataset (i. e. the same set of genomes that were used for the expert annotation of modules) compare favorably to the reference modules with MCC and F1 score values of 0.
63 and 0. 66, respectively. The precision and accuracy values (0. 96 and 0. 85, respectively) are particularly high. This indicates that most of the genes belonging to a predicted module are found together in a reference module (True Positives) and that few genes are wrongly associated (False Positives). Similarly, most of the genes in a GI that do not belong to a same or any reference module are also not grouped into a module by our method (True Negatives). On the other hand, the recall value (0. 51) is much lower, indicating that while we do recover proper modules a non-negligible number of the genes belonging to a reference module are not covered by our predictions (False Negatives). In summary, using the EcoliRef dataset, the modules predicted by panModule For each E. coli dataset, the number of genomes and pangenome gene families is given along with the number of families in the shell.
Benchmark results are provided with two additional metrics (gene coverage and module coverage) corresponding to the percentage of genes and modules of the reference dataset that are associated to a predicted module. The benchmark was made using s = 0. 86 except for the last column where s = 0. 49. The number of predicted modules, their gene families and the percentage of shell families in modules are also provided. are frequently included in or equal to the reference modules and rarely overlap multiple reference modules, but some reference modules may be missed or be incomplete. Those missing genes represent about 30% of the genes associated to a reference module, and about 20% of the reference modules are not covered by any prediction (see Table  1  and Figure  1  as an illustration). Both EcoliComplete and EcoliContigs datasets show equivalent results to the EcoliRef dataset but with slightly lower metrics overall, with the recall being the most impacted (i.
e. 0. 43 and 0. 45, respectively, versus 0. 51 for the Ecol-iRef dataset). It is further amplified when looking at the EcoliMAGs dataset whose metrics are much lower, especially in terms of recall, where it reaches a staggering value of 0. 19, meaning that most of the reference modules are not predicted. Figure  1  clearly displays the sparsity of predictions in that specific dataset. However, it still keeps an acceptable precision and accuracy with 0. 90 and 0. 76, respectively, indicating that predicted modules even with the most incomplete and fragmented genome datasets are often actual modules. Indeed, the method parameters estimated on the EcoliRef dataset are likely too stringent to analyze MAGs as many modules may be incomplete or split on several contigs. We looked for the Jaccard similarity (s) parameter providing the best MCC using the same values for t and m (Supplementary Data file 'benchmark/EcoliMAGs benchmark metrics.
tsv'). The best value of s is 0. 49, and gives a MCC value of 0. 56 which is equivalent to the EcoliComplete and EcoliContigs datasets. Even using such a low s value, accuracy and precision remain high. Those results indicate that our method is applicable on very incomplete and fragmented genome datasets such as MAGs using a relaxed Jaccard similarity threshold. This benchmark shows that panModule is able to correctly predict modules with a very good accuracy and precision even with incomplete genomes. It should be noticed that this validation is based only on a limited subset of curated data made of 12 E. coli genomes of which 6 are from the B2 phylogroup. Therefore, this reference dataset does not capture the overall diversity of E. coli. Nevertheless, panModule can modularize a large fraction of the shell pangenome of large and diverse genome datasets as well (e.
Analysis of the KPHPI208 genomic island in Klebsiella pneumoniae To illustrate the potential of panModule on other species, we chose to reanalyze the KPHPI208 GI of Klebsiella pneumoniae 1084. This 208-kb GI inserted at the asn-tRNA loci was described to be composed of 8 genomic modules (GM1 to GM8) using comparative genomics  [17]. First, we used panRGP to predict GIs in K. pneumoniae 1084 from a pangenome made of 566 genomes. Two predicted GIs correspond to KPHPI208. The first detected GI starts at 1,744,478 bp and stops at 1,906,798 bp while the second one starts at 1,920,068 bp and stops at 1,952,189 bp. The two GIs cover the majority of the region, with the remaining portion being persistent genes between the two islands. In the KPHPI208 region made of 135 genes, panModule predicted 9 modules including a total of 98 genes.