dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the same dataset of 493 Shigella and E. coli genomes to infer two SNVbased ML trees: one based on 92,688 SNV sites (including recombinant sites) and the second on 5129 recombination-free SNVs. We compared these SNV-based clusterings (with strong bootstrap support) to clusters inferred by cgMLST HC. We found that the clustering of Shigella populations by HC2000 and by HC1100 were congruent with both core-genome SNV phylogenies (Fig. 5  and Supplementary Fig. 3 ). Even though cgMLST allelic distances (based on allelic changes counted as single genetic events, regardless of the number of SNVs involved) are reliable indicators of population structure, especially for routine surveillance tasks, SNVs are generally preferred for more detailed studies including long-term evolutionary dynamics.
This likely accounts for the topological differences in the deep structures of cgSNV and cgMLST phylogenetic trees (Figs. 1  and 5  and Supplementary Fig. 3 ). Since the focus of this study was the use of cgMLST for routine surveillance of Shigella infections, resolution of these ancestral differences was not investigated further. For confirmation of the robustness of the population structure of Shigella established by cgMLST analysis of our reference datasets, we also applied cgMLST to 3870 clinical Shigella isolates received by the FNRC-ESS between 2017 and 2020 (the "routine" dataset), in the framework of the French national surveillance programme for Shigella infections (see the methods section "Strain selection and typing"). All these isolates were characterised phenotypically, on the basis of biochemical reactions and serotyping.
2% (3713/3861), 55. 9% (2157/3861) and 80. 8% (3120/3861) of the strains were correctly assigned by ShigaTyper, ShigEiFinder (short reads) and ShigEiFinder (assemblies), respectively. ShigEiFinder performed less well than ShigaTyper with this routine dataset (Fig. 9  and Supplementary Table  7 ). In particular, most of the results obtained with short reads were "uncertain" (i. e. , assignment to a Shigella cluster, but not to a serotype). ShigEiFinder uses 22 accessory genome (non-O antigen) genes to assign Shigella genomes to eight clusters, with the serotype assignment based on the Shigella serotype-specific wzx/wzy genes (and O-antigen modification genes for S. flexneri). The inferior performance of ShigEiFinder can be explained by the low read coverage of the AT-rich rfb gene cluster for the "routine" genomes.
SS a is a chromosomal remnant rfb from S. sonnei. A b in superscript indicates that the cluster is located on a plasmid. A 1 in superscript indicates that the gene may be truncated by an IS1 in some strains. based on a cgSNV analysis, contained two or more of these serotypes. This serotyping method does not reflect the genetic relatedness between Shigella isolates, and has a number of other disadvantages, including being time-consuming, having intraand interspecies cross-reactivity, not allowing for typing of rough strains and new serotypes  14, 25. Therefore, modern laboratory surveillance of Shigella infections should now be based on phylogenetically relevant methods rather than simply on molecular or in silico serotyping  10, [14] [15] [16]. The cgMLST HC analysis provides, in a single step, a wide range of fixed clustering levels, from HC0 (no allelic differences allowed) to HC2350 (maximum of 2350 allelic differences allowed), with a standard and stable nomenclature.
Methods Strain selection and typing. In total, 4187 Shigella reference strains and clinical isolates were studied (Supplementary Data 1). Two datasets were used. The first datasetthe "reference" datasetconsisted of 317 Shigella reference strains covering all the known serotypesincluding provisional serotypesfrom various geographic locations (53 countries on four continents) and time periods (1913   Stacked-bar chart displaying the results (percentage assignment) per tool for each Shigella serotype. E, the SeroPred tool implemented in EnteroBase 18 ; T, ShigaTyper  14  ; R, ShigEiFinder  16  using short reads; F, ShigEiFinder using SPAdes assemblies. SB S. boydii, SD S. dysenteriae, SON S. sonnei, SF S. flexneri. A correct assignment means that the predicted serotype was the same as the phenotypically determined serotype.
An uncertain assignment means that the correct serotype was listed among others or for ShigEiFinder that only the cluster level (Cs1 to Cs3) was obtained. An incorrect assignment means that the predicted serotype was different from the phenotypically determined serotype. "None" means that no serotype was predicted. More detailed results can be found in Supplementary Table  7. E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F E T R F Prevention, Atlanta, USA; the Tokyo Metropolitan Research Laboratory of Public Health, Tokyo, Japan; and the International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka. This "reference" dataset included 44 S. sonnei from four different lineages 33 , 16 S. dysenteriae type 1 (ref.
34 ) and 98 S. flexneri serotypes 1-5, X and Y belonging to the seven phylogenetic groups (PGs) described previously  2, 5. The second datasetthe "routine" datasetconsisted of 3870 clinical isolates received and sequenced by the FNRC-ESS between 2017 and 2020 in the framework of the French national surveillance programme for Shigella infections. This programme is based on a voluntary laboratory-based network consisting of ~1000 clinical laboratories located in mainland France and its overseas territories in South America, the Caribbean and Indian Ocean, which send ~1000-1200 Shigella isolates to the FNRC-ESS each year (only 600 in 2020, due to the COVID pandemic). This surveillance system has been estimated to detect 50-60% of laboratory-confirmed Shigella infections in France  35. Between 2017 and 2020, 3942 clinical isolates were received and sequenced at the FNRC-ESS, and we included the 3870 genomes that passed the quality control criteria of EnteroBase.
All these strains and isolates were thoroughly characterised with a panel of biochemical tests and serotyped by slide agglutination assays according to standard protocols, as previously described  36. Additional typing sera against KIVI 162 and SH-105 were provided by the International Centre for Diarrhoeal Disease Research, and the Public Health Agency of Canada, respectively. DNA extraction and sequencing. Total DNA was extracted with the Wizard Genomic DNA Kit (Promega, Madison, WI, USA), the Maxwell 16-cell DNA purification kit (Promega) or the MagNA Pure DNA isolation kit (Roche Molecular Systems, Indianapolis, IN, USA), in accordance with the manufacturer's recommendations. The 4187 strains and isolates were sequenced with different Illumina platforms. FqCleanER version 3. 0 (https://gitlab.
pasteur. fr/GIPhy/fqCleanER) was used to eliminate adaptor sequences  37  , correct sequencing errors  38  and discard lowquality reads. Assemblies were generated with SPAdes 39 version 3. 15. Other studied genomes. With the aim of capturing the broadest possible diversity of Shigella populations, we searched the E. coli/Shigella database in EnteroBase  18  , and selected 81 additional Shigella genomes ("reference+" dataset) not originating from the Institut Pasteur (Supplementary Methods section "Other studied genomes"). We included 27 enteroinvasive E. coli (EIEC) and 68 E. coli strains from the E. coli reference (ECOR) collection (Supplementary Methods section "Other studied genomes"), to place our Shigella genomes in the phylogenetic context of the broader diversity of E. coli. We also used the closed PacBio sequences available for all Shigella serotypes and described by Kim and coworkers  40  , to study the genetic organisation of the rfb gene cluster or various operons described in the "Gene analyses" section.
However, these closed genomes were not included in the cgMLST analysis, as they were not edited with short reads and the numerous indels in the homopolymers therefore altered the allelic distances (Supplementary Table  1 ). Characterisation of the O-antigen gene clusters. The Shigella O-antigen biosynthetic gene (rfb) cluster was analysed after extraction of the region between the housekeeping genes galF (encoding UTP-glucose-1-phosphate uridylyltransferase) and gnd (encoding 6-phosphogluconate dehydrogenase), which are known to flank the rfb cluster  28. Newly identified rfb clusters were annotated on the basis of a previously annotated closely matched E. coli cluster in the NCBI BLASTn nucleotide collection (nr/nt) database (100% coverage and at least 99% identity) or with ORFfinder (https://www.
ncbi. nlm. nih. gov/orffinder/) when no matching cluster was found in the NCBI BLAST database (https://blast. ncbi. nlm. nih. gov/Blast. cgi). The GenBank accession codes of all the Shigella rfb clusters are listed in Supplementary Table  2. We also used three tools for in silico serotyping: SeroPred, the serotype prediction tool implemented in EnteroBase 18 , ShigaTyper 14 (using short reads), and ShigEiFinder 16 (using both short reads and assemblies). Phylogenetic analyses. We used the Escherichia/Shigella cgMLST scheme ("cgMLST V1") implemented in EnteroBase  18  to study our genomic datasets. This scheme is based on 2513 single-copy orthologous genes found in the soft-core genome of a representative set of Escherichia and Shigella genomes  18. The cgMLST sequence types (cgSTs) consist of a combination of up to 2513 integers, each representing a sequence variant (allele) of a gene, accounting for missing data (gene missing, allele not called due to short-read misassemblies).
e. , with the smallest HC number) in order to ensure the long-term stability of the nomenclature. Genomes with >3% of missing data were also processed separately by pHierCC to minimise the impact of missing data on clustering  19. We also performed cgSNV analysis to assess the phylogenetic relationships of 398 Shigella (317 from the "reference" dataset and 81 from the "reference+" dataset) and 95 E. coli (68 ECOR and 27 EIEC) strains. An Escherichia fergusonii genome (RHB19-C05, GenBank accession no. GCF_013892435. 1) was used as an outgroup for the cgSNV analysis. The paired-end reads and simulated paired-end reads were mapped onto the reference genome of E. coli K12-MG1655 (GenBank accession no. NC_000913. 3) with Snippy version 4. 6 (https://github. com/ tseemann/snippy). We used Snippy default settings, except-mincov 4-minfrac 0.
Acknowledgements This study was supported by the  Institut Pasteur ;  Santé publique France ; the  Fondation Le Roch-Les Mousquetaires. I. Y. is funded by a fellowship from  Association AZM and Saadeh , and the  Lebanese Association for Scientific Research (LASeR). E. E. H. was funded by a  Fulbright Research Award. N. R. T. is funded by the  Wellcome Trust  (grant  206194 ). For the purpose of Open Access , the authors have applied a CC BY public copyright licence to any Author-Accepted Manuscript version arising from this submission. We thank all corresponding laboratories for sending isolates to the  French National Reference  Centre for E. coli,  Shigella , and Salmonella. We also thank  N. Strockbine ,  K. Talukder ,  L. Peterson  and  S. Matsushita  for sending some Shigella provisional serotypes, and the sequencing teams at the  Institut Pasteur (P2M-Plateforme de Microbiologie Mutualisée)  and the  Wellcome Sanger Institute  for sequencing the samples.
Data availability Short-read sequence data were submitted to EnteroBase (https://enterobase. warwick. ac. uk/) and to the European Nucleotide Archive (ENA, https://www. ebi. ac. uk/ena/) under study numbers PRJEB44801, PRJEB2846, and PRJEB2128. Other whole-genome sequences analysed during the study are available from ENA (https://www. ebi. ac. uk/ena/), NCBI RefSeq (https://www. ncbi. nlm. nih. gov/refseq/), DDBJ (https://www. ddbj. nig. ac. jp/indexe. html), and GenBank (https://www. ncbi. nlm. nih. gov/genbank/). All the accession numbers of the genomes used in this study are listed in Supplementary Data 1. The GrapeTree of 493 Shigella and E. coli reference genomes is publicly available from EnteroBase (http:// enterobase. warwick. ac. uk/ms_tree?tree_id=55118) and from Microreact (https:// microreact.
org/project/kP4HJjriDvAfTS4Ed3Avx8/01568b6f). The nucleotide sequences of the Shigella rfb clusters were submitted to GenBank (https://www. ncbi. nlm. nih. gov/ genbank/) under accession numbers MZ286364-MZ286394, MZ303046, MF322747-MF322752, and MF322754. The accession numbers for the individual rfb sequences are given in Supplementary Table  2.
The combination of long read genetic and phased methylation information from ONT reads can further be used to detect aberrant expression patterns, such as allelic expression imbalance or gene fusion events at greater level of detail. In the future, deep coverage and highly accurate long-read data will be needed to achieve the complete de novo assembly of cancer genomes, especially in the context of intra-tumour heterogeneity, contamination of normal cells, and large numbers of complex rearrangements. The proposed long-read methods enabled us to describe a new complex DNA rearrangement pattern, termed templated insertion thread, consisting predominantly of short segments (<1kbp) that are copied and (self-)concatenated into amplified, highly repetitive somatic sequences of up to 50kbp in size.
We did not identify any additional medulloblastoma samples with templated insertion threads in the PCAWG short read dataset, which is perhaps explained by the relatively low portion of medulloblastoma samples contained in the PCAWG cohort exhibiting chromothripsis (~12%)  56. One note of caution is that discovery of high-complexity regions as seen in templated insertion threads using shortreads is obscured by somatic SV calling pipelines because multiple distinct SVs co-occur at the same SV breakpoint leading to algorithmic clustering and SV merging issues. This is contrary to long reads that have the capability to fully resolve the complex structure and composition of structural rearrangements in cancer genomes. While rayas can overcome this issue in part, it is likely that short read WGS masks additional cases of templated insertion threads, especially where they involve short (<1kb) templated insertion units or repeat-rich DNA, given the relatively poor sensitivity of Illumina reads for calling such SVs  13.
The long-read data also enabled investigation of the association of complex SVs and telomeric repeats, an analysis that revealed the fusion of telomeres with chromosomes that underwent chromothripsis. Some of these events were captured in a single long ONT read connecting a telomere to various SV rearrangements, reminiscent of SV mutations stabilized by independent telomere fusions. The assignment of telomeric repeats to chromosomal haplotypes also highlighted the need for continuous reference improvements, as some of these events could only be unambiguously resolved using the new CHM13 telomere-to-telomere (T2T) assembly  31. A comparable analysis on short-read data failed to resolve the telomere-associated complex rearrangements, and only three out of the five SV to telomere junctions showed confident telomeric repeat motifs in an unmapped mate or a soft-clipped read, which underscores the critical need for long-read sequencing to investigate telomere-associated structural rearrangements, which are considered a key cancer mutational process in association with telomere crisis  28.
Bcftools was used to normalize and left-align indels. Copy-number segmentation employed Delly's cnv mode  63  with the GRCh38 mappability map and the DNAcopy 64 package of the Bioconductor project (Figure  S3 ). Structural variants were called using delly  63  in a paired tumor-normal fashion to distinguish germline and somatic SVs. All command-line tools were installed using bioconda  65.
Long-read alignment and variant calling Long reads from Nanopore sequencing were basecalled with guppy version 4. 0. 14 using the high accuracy model for PromethION (r9. 4. 1_450bps_hac_prom). Resulting FASTQ files were aligned to the human reference genome (GRCh38) using minimap2 66 using the '--ax map-ont' option and otherwise default parameters. The median long-read coverage was 15x for the blood and relapse sample and 30x for the primary tumor. The median read length was 4,480bp, 4,993bp and 5,678bp for the blood, primary tumor and relapse sample, respectively. The estimated sequencing error rate of the aligned data using Alfred's qc mode  60  was estimated to be 8. 4% for the blood sample and 6. 8%-6. 9% for the tumor samples. Structural variants (SVs) from the long-read data were called using Nanovar 67 , Sniffles  68  and Delly  63.
All remaining connected components are written to a BED file with a unique component id. For each component, all genomic segments and edges are outputted and the results can be visualized as a graph (Figure  S11 ). Using this approach we identified two templated insertion threads in the primary tumor. In addition, a single additional putative instance of this pattern was detected in the Illumina data of the relapse but not in the ONT data from the same sample; this putative event showed much lower split-read support (5 compared to >>100 for the primary tumor templated insertion threads) and an unexpected density of variant calls, suggesting that it may be caused by a mapping artifact or a collapsed repeat rather than a templated insertion thread. A simple threshold for the minimum split-read support (i.
Telomere analysis of derivative chromosomal segments. As part of our long-read analysis toolbox for cancer genomics, termed lorax, we also developed a method that identifies telomeric motifs, repeats of TTAGGG, TGAGGG, TCAGGG, TTGGGG or their reverse complement, in error-prone ONT reads and applied this method to the long read data of the primary tumor and the relapse sample. As suggested previously  78  , we start by precomputing all possible strand-specific 18-mer telomere motifs, scan all long-reads for exact motif matches and count their occurence. We then search for distal non-telomeric alignments of these reads and intersect reads that show both a telomeric repeat and a unique alignment outside a telomere region of a minimum length of 1kbp. We use the control genome to filter out likely mapping artifacts due to incomplete reference sequences by masking alignments from the control genome that show both a telomeric repeat and a unique alignment outside a telomere region.
Differential methylation testing. In order to find genomic regions with differential methylation between samples, we used the software package PycoMeth  34. PycoMeth aggregates methylation likelihood ratios reported by Nanopolish over predefined regions, computes a read-level methylation rate from thresholded loglikelihood ratios (threshold 2. 0) and then performs a Wilcoxon rank-sum test (for 2-sample comparison) or Kruskal Wallis test (for more than two samples) for methylation rates across samples. P-values were then adjusted for multiple testing using independent hypothesis  79  , using a weight based on the variance of methylation rates, and the Benjamini-Hochberg method  80. Regions with FDR<=0. 05 are reported as differentially methylated regions (DMRs). Candidate regions for differential methylation testing are selected based on two different segmentation methods: 1) sequence segmentation and 2) methylome segmentation.
Sequence segmentation uses PycoMeth's CGI Finder module, which determines CpG islands based on local CG-density. For methylome segmentation PycoMeth Meth_Seg, a de novo methylome segmentation method which implements a bayesian changepoint-detection algorithm, is used to determine regions with consistent methylation rate from the read-level methylation predictions. For ASM analysis, PycoMeth Meth_Seg was provided with haplotype information to perform a haplotype-aware segmentation. We investigated differentially methylated regions between the primary tumor and the relapse sample, as well as between all three samples by applying PycoMeth Meth_Comp using both candidate region approaches with the parameter using the parameter -hypothesis bs_diff in order to test for difference in read-level methylation rate per segment.
RNA alignment and expression quantification Gene-expression quantification was performed in line with the GTEx standards. In short, we (re)processed the RAW expression data by first aligning the reads to the human reference genome, build 38, using STAR in two step mapping per sample. The mapping was performed in two modes. One for the allele specific expression, using a custom reference genome, replacing the homozygous SNP variants with the relevant genotype of the sample, and supplying a VCF with heterozygous variants when mapping in STAR, used for allele specific expression and gene fusion detection. Second, for the differential expression and splicing analyses we remapped the samples to the standard genome. Gene information was taken from ENSEMBL (v101) and gene-expression quantification was performed using RNASeQC, in line with the GTEx consortium expression quantification.
Reference RNA expression datasets and differential expression For comparative expression analysis we leverage data from the ALS consortium (SRP151960)  83  and GTEx cerebellum expression data. The data from the ALS consortium were reprocessed as done for the two medulloblastoma samples, see above, and the GTEx data  84  was used as is. This data was leveraged both for direct comparison of expression levels, and for correction of the gene expression levels. The first five principal components (PCs) were calculated on the combined ALS and GTEx dataset. The medulloblastoma samples were projected into this same PC space, using the rotation information, and the first five PCs were regressed out from the expression levels of all samples, medulloblastoma, GTEx and ALS. Next we used a Z-score transformation on both the raw and corrected expression of the reference samples and placed the two medulloblastoma samples in these distributions.
Gene fusion and validation using DNA long reads. Potential gene fusions were detected from RNA sequencing data using Arriba 87 (V2. 0. 0). The SVs called from both short and long read data were used to inform Arriba, and we included the provided blacklist, other settings were left at defaults. After identification of the gene fusion pairs we setout to validate these using the long read DNA data. First, we check for individual read support from ONT reads with chimeric alignments mapping to both genes. Fusion pairs involving long intergenic non-coding RNA genes, which are characterized by long introns of on average 10kbps length  88  , or fusion containing large intronic insertions, however often do not have individual genomic reads spanning exons of both genes. In order to additionally validate such fusions with large insertions, for which no single ONT read spans the fusion pairs, we devised a graph-based method to suggest the most plausible gene fusion reconstruction.
Acknowledgements We thank  Frauke Devens ,  Kim Judge  as well as DKFZ and EMBL IT and sequencing core facilities for excellent technical support. The present contribution is supported by the  Helmholtz Association  under the joint research school " HIDSS4Health -Helmholtz Information and Data Science School for Health ". A. E. received funding from the  DFG  (project number  460595631 ) and from the  Wilhelm Sander Foundation  (project number  2020. 115. 1 ). J. O. K. received funding from the  BMBF  ( 031L0184C ) and from the  NIH  ( 1R01HG010169-01  and  2U24HG007497-05 ).