dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection of the ipaH gene The invasion plasmid antigen H (ipaH) gene is a multicopy gene found in both the chromosome and plasmid of all Shigella and EIEC strains. The presence of sequences encoding the highly conserved C-terminal domain of IpaH proteins was used as an indicator of Shigella/EIEC (Table  S4 )  [11, 31, [47] [48] [49]. Strains with no ipaH hits are considered 'Not Shigella/EIEC' (Fig. 1 ). As this gene was frequently split between multiple contigs, we applied a k-merization approach with the same options as for rfb and POAC genes. The k-mers used are publicly available from: https://github. com/imanyass/ShigaPass/tree/main/SCRIPT/ShigaPass_ DataBases/IPAH. All the genes used in this study are described in Table  S4.
Evaluation of ShigaPass We compared the performance of ShigaPass version 1. 5. 0 (https://github. com/imanyass/ShigaPass) with that of existing in silico serotyping tools: ShigaTyper version 1. 0. 6 (using short reads) (https://github. com/CFSAN-Biostatistics/shigatyper) [31] , ShigEi-Finder version 1. 2. 0 and its latest version 1. 3. 2 uploaded onto GitHub on 21 April 2022 (using both short reads and assemblies) (https://github. com/LanLab/ShigEiFinder) [11]. The following categories were used in our comparison of the different tools: correct, uncertain, incorrect and none. A correct assignment means that the predicted serotype was the same as the phenotypically determined serotype. An uncertain assignment means that more than one serotype was predicted and that the correct serotype was listed among them or, for ShigEiFinder, that only the cluster level (Shigella clusters Cs1-Cs3, and CSS as defined by Zhang and co-workers  [11] ) was obtained.
Design of ShigaPass: the databases We made use of the variability of different genetic structures to develop ShigaPass, an in silico tool for predicting Shigella serotypes from assembled genomes. Our first target was the rfb gene cluster, which encodes the Shigella serotype-specific lipopolysaccharide O antigen. Forty-four different O-antigen rfb gene clusters, ranging in size from 9 to 17 kb, have been described in Shigella (Table  S3 )  [7]. The E. coli and Shigella rfb sequences have a low GC content and are not well covered in strains sequenced with the Illumina Nextera XT   S2. The results are then compared to ShigaPass meta-profiles, a list of meta-profiles combining the four targets defined for each serotype. ShigaPass predicts the serotype whenever it finds a match between rfb cluster profile and at least two of the three remaining markers within the defined ShigaPass meta-profiles.
A result of 'Shigella spp. ' can indicate genome contamination, poor quality or new emerging serotypes. ShigaPass is available as a standalone version in GitHub (https://github. com/imanyass/ShigaPass/) and as an online version via Galaxy (https://galaxy. pasteur. fr/). rfb, fliC, CRISPR spacers and MLST ST databases can be accessed via the RFB, FLIC, CRISPR and MLST repositories, respectively, in GitHub. (b) Summary of the rfb decision tree and actions taken to differentiate between serotypes possessing identical or highly similar rfb sequences. SF1-5, X and Y subserotypes are identified on the basis of POAC k-mer detection. SB1/SB20 and SB6/SB10 are distinguished on the basis of detection of the galF and tauA genes, respectively. SD3/SD16 and SD2/SDprov. BEDP02-5104 are distinguished on the basis of detection of the additional serotype-specific rfb.
SB, S. boydii; SD, S. dysenteriae; SF, S. flexneri; SS, S. sonnei; SDprov. , S. dysenteriae provisional BEDP 02-5104; FlexSerotype, S. flexneri 1-5, X and Y subserotypes. kit for library preparation, as the transposase-based library is biased against AT-rich sequences  [7, 50]. Genome assemblies may, therefore, generate incomplete rfb data. Consequently, we trimmed the rfb into k-mers of 150 bp in length with a 50 bp sliding window, to facilitate detection with a blast pipeline. The k-mers including IS elements or displaying >90 % coverage and identity between different serotypes were removed. Each k-mer in the database was generally specific for one serotype, as non-specific k-mers were discarded, with some exceptions (see below). Several serotypes have identical or highly similar rfb sequences.
These serotypes include S. boydii 1 and S. boydii 20, S. boydii 6 and S. boydii 10, S. dysenteriae 2 and S. dysenteriae prov. BEDP02-5104, and S. dysenteriae 3 and S. dysenteriae 16  [7]. S. boydii 1 and S. boydii 20 have the same rfb. However, that of S. boydii 20 is unusual in that it begins with an insertion sequence (IS), the galF gene preceding the rfb cluster having been deleted  [7]. We therefore used the galF gene as a marker to differentiate between S. boydii 1 and S. boydii 20 (https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/RFB/galF_SB1. fasta). A comparison between the S. boydii 6 and S. boydii 10 genomes showed that the tauA gene, which encodes a taurine-binding periplasmic protein, was present only in S. boydii 6 genomes. This gene was, therefore, used as a marker of S.
boydii 6 (https:// github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/RFB/taurine_SB6. fasta). S. dysenteriae prov. BEDP 02-5104 carries two different rfb clusters, one of which is chromosomal and identical to that of S. dysenteriae 2, whereas the other is plasmid-borne  [7]. S. dysenteriae 16 also has two rfb regions: a variable 6 kb remnant rfb (identical to that of S. dysenteriae 3) in the normal location of rfb (i. e. located next to the colanic acid biosynthesis gene cluster  [7] ), and a second complete rfb located elsewhere on a genomic island  [7]. These additional rfb regions were used as indicators for S. dysenteriae prov. BEDP 02-5104 and S. dysenteriae 16, respectively. Some S. dysenteriae 16 strains may not retain the genomic island containing the complete and specific rfb.
sonnei strains. There is, therefore, ultimately no portion unique to S. sonnei. The k-mers common to other serotypes were not, therefore, removed. We overcame this problem by maintaining the S. sonnei rfb k-mers in a separate database (https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/RFB/RFB_serogroup_D_150-mers. fasta) and subjecting them to blast search only if no other rfb was detected. The S. flexneri strains from the S3 cluster (serotypes 1-5, X, Y) have the same O-antigen backbone structure. Their serotypes result from modifications to the O-antigen tetrasaccharide repeat conferred by the gtr, oac and opt POAC genes  [2]. Consequently, the detection of these POAC genes can predict S. flexneri 1-5, X and Y serotypes and subserotypes (Table  S5 )  [20, 27, 28].
As these genes have a low GC content, we used the same k-mer strategy as for rfb to construct the database of POAC genes (Table  S4 ). When ShigaPass detects a genome carrying the rfb of S. flexneri 1-5, X and Y, this genome is queried against the k-mer database containing the sequences of the POAC genes. The S. flexneri serotype is then determined on the basis of the correspondence between S. flexneri 1-5, X and Y serotypes and the POAC genes detected (Table  S5 ). Our second target was the seven HK genes of the Achtman MLST scheme. Using the Shigella strains in the development dataset (n=440/575), we identified 67 STs for 433 strains, as shown in Table  1. The ST could not be determined for seven strains due to missing data for one of the MLST alleles (fumC was not called in six strains and adk in one strain).
Most of the STs had been reported before, but some were new. Given that STs are determined by seven genes, this variation is unsurprising, because mutations of any one of these genes can give rise to a new ST. We found that 44 of the 67 STs identified belonged to five ST complexes (the ST147, 148, 152, 243 and 245 complexes) found in the S2, S1, SON, S1 and S3 Shigella clusters (clusters previously defined by Yassine et al. [7] ), respectively. The Shigella S2 cluster contained most of the STs that did not belong to ST complexes. The third target studied was the fliC gene, encoding the H-antigen. In the Shigella development dataset, we identified 59 fliC alleles in total, ranging in length from 1575 to 2013 bp, excluding IS elements. The fliC alleles contained premature stop codons, IS elements or both, due to their cryptic nature.
sonnei, and the ShH2 complex specific for the S3 cluster. Conversely, similar fliC alleles belonging to the ShH3 or ShH4 complexes were also found in Shigella strains of different serotypes. However, these fliC sequence complexes included some fliC variants specific to particular serotypes, such as ShH3 in S. boydii 11, ShH4 in S. boydii 5 and ShH5 in S. boydii 9 (Fig. S1 ). In some strains, the presence of certain IS elements within fliC may also The fourth target was the CRISPR spacers, which are short sequences (22-38 bp) interspersed with short palindromic repeats (direct repeats or DRs) in CRISPR arrays. We identified 22 different spacers arranged in 27 CRISPR profiles in the Shigella development dataset. The number of spacers per profile ranged from one to seven. These CRISPR profiles were highly consistent with the Shigella population structure determined by cgMLST (Table  2 )  [7].
For example, the A-var2 and A-var3 spacers were found only in strains from the Shigella S1 and S3 clusters, respectively. No spacers were detected in the S. dysenteriae 14 (n=1) and S. dysenteriae 17 (n=16) strains, which lacked the entire CRISPR array and had only a remnant iap gene (ending after the first 240 nt for S. dysenteriae 17 and after the first 650 nt for S. dysenteriae 14), followed by IS elements. However, the analysis of a PacBio sequence from S. dysenteriae 14 ATCC 49346 (GenBank accession number CP026832)  [51] , which was not included in this study, revealed the presence of the A-var2 spacer. This finding highlights the possibility of IS-driven alterations at the iap CRISPR locus. These IS-modified CRISPR structures were encountered only in S. dysenteriae 14 and 17. We therefore designed specific k-mers recognizing DNA sequences encompassing the end of the iap gene and the first few nucleotides of the IS.
At least two additional genetic structures are required because the MLST ST may be modified by a single mutation in one of the seven HK genes, and the CRISPR or fliC allele may be affected by IS mobilization. We therefore tested several associations of techniques (Table  S6 ). We found that the identification of the different Shigella serotypes and differentiation between Shigella and non-Shigella were optimal for a combination of MLST, CRISPR, fliC and rfb analyses. The extraction of these four genetic structures from 440 Shigella strains of the development dataset revealed 137 distinct meta-profiles, which we defined as ShigaPass meta-profiles (Table  S7 ) (https:// github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/ShigaPass_meta_profiles_v5. csv).
Design of ShigaPass: the pipeline ShigaPass is a shell script designed to predict Shigella serotypes from assembled genomes. The workflows adopted by ShigaPass are outlined in Fig. STs present in several serotypes are highlighted in bold. PG, phylogenetic group described previously  [2] ; prov. , provisional. Table  1. Continued as multiple copies on both the chromosome and pINV in Shigella and EIEC -is used as an exclusion checkpoint. Strains with no ipaH hits are considered 'Not Shigella/EIEC' and only ipaH-positive strains are subjected to ShigaPass prediction. ShigaPass then uses the reference databases to identify the rfb gene clusters, MLST STs, fliC alleles and CRISPR spacer sequences (Fig. 1 ). Shigella serotype is determined by a match between the results obtained for the four genetic structures and one of the ShigaPass meta-profiles.
(https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/ShigaPass_meta_profiles_v5. csv). ShigaPass can deal with new profile combinations (due to variations of fliC sequences, CRISPR profiles and ST alleles) or cases of missing data, by predicting the serotype whenever it finds a match between the rfb cluster profile and two of the other three genetic structures and a defined meta-profile (in such cases ShigaPass indicates a '75 % match'). When ShigaPass detects the specific rfb of S. flexneri of cluster 3, an analysis of POAC genes is performed to identify S. flexneri 1-5, X and Y and their subserotypes. If multiple rfb hits are detected, the predominant rfb, with the largest number of hits, is identified and a comment   S1 ). All non-Shigella isolates were correctly assigned to either EIEC or 'not Shigella/EIEC'.
All discrepant results for the two datasets are presented in Table  3. Interestingly, we found that some S. flexneri 3b isolates harboured the oac1b variant rather than oac. As both these variants give positive reactions with S. flexneri group 6 antisera, this phenotype was missed with sero-agglutination methods. ShigaPass results were compared to those of ShigaTyper and ShigEiFinder for all the Shigella and non-Shigella isolates in the development dataset (Table  4 , Fig. 2 , Tables  S8  and S9 ). ShigaPass gave the best serotype prediction results. 4 ). The poorer performances of ShigaTyper and ShigEiFinder relative to ShigaPass were mostly due to the inaccurate typing of several S. boydii serotypes and the new and provisional serotypes of S. dysenteriae  (16, 17  and prov. BEDP 02-5014) (Fig.
Evaluation of ShigaPass and other in silico tools with the validation dataset We evaluated the performance of ShigaPass with an independent dataset consisting of 4304 isolates, including 4276 Shigella isolates (85 S. boydii; 49 S. dysenteriae; 2393 S. sonnei; 1616 S. flexneri 1-5, X and Y; 133 S. flexneri 6); 22 EIEC; three E. coli; and three E. albertii strains (Table  S1 ). All these strains were received and sequenced by the FNRC-ESS between 2017 and 2021, within the framework of the French national surveillance programme for Shigella infections and were characterized with biochemical tests and sero-agglutination assays. Eight Shigella strains could not be identified to serotype level in biochemical tests or seroagglutination assays because they were rough or non-serotypable. Two strains were classified as non-serotypable via seroagglutination due to a lack of sera against S.
dysenteriae 14 and S. dysenteriae 17 in our laboratory since 2021. ShigaPass identified these two strains as S. dysenteriae 14 and S. dysenteriae 17, with a 100 % match. ShigaPass also efficiently identified all rough strains (Supplementary Data 1). However, these strains were not included in the analysis of correct assignment rates for in silico tools because it was not possible to compare their assignment with that achieved with laboratory methods. Following analysis of the validation dataset, we updated the meta-profile list with 65 new profiles, mostly reflecting ST variability (37/65, 56. 9%) or new combinations comprising new meta-profiles (https://github. com/imanyass/ShigaPass/blob/main/SCRIPT/ShigaPass_DataBases/ShigaPass_meta_profiles_ v5. csv). There was a 75 % match between these new profiles and the initial meta-profiles (matches for rfb and two of the other three genetic features) so even before the addition of these new profiles, it was still possible to identify genomes containing these new profiles correctly with ShigaPass.
By contrast, 18 Shigella isolates were misidentified as EIEC by ShigaTyper. Eight and 138 Shigella isolates could not be resolved (results output as Shigella/EIEC unclustered) by ShigEiFinder version 1. 3. 2, based on assemblies and short reads, respectively (Tables  S10  and S11 ). The validation dataset included 63 strains incorrectly assigned by ShigaPass (Table  3 ). One S. dysenteriae 16 strain was identified as 'S. dysenteriae 3/S. dysenteriae 16' due to loss of the genomic island containing the second, complete rfb. Three S. sonnei, one S. flexneri 6 and one S. flexneri 2a strains were identified as 'Shigella spp. ' due to the contamination of genomic sequences with at least two different serotypes. The 57 remaining discrepancies were due to differences between the phenotypic and genotypic signatures encountered in S.
DISCUSSION We developed ShigaPass, a new in silico tool for Shigella genoserotyping, based on the examination and characterisation of rfb, fliC and POAC genes, CRISPR loci and MLST STs (Achtman's scheme) from the 440 Shigella genomes of the development dataset. The   5 ). ShigaTyper was designed to predict Shigella serotypes via a mapping approach, using Illumina or Oxford Nanopore reads for only two genes of the O-antigen rfb cluster, wzx and wzy  [31]. The wzx/wzy genes -key components for O-antigen synthesis -are serotype-specific and frequently used in PCR-based assays for serotype identification  [50]. However, with the use of only two genes, genoserotype identification is prone to failure in cases of poor read coverage. ShigaTyper can also differentiate between Shigella and EIEC on the basis of a set of criteria, including detection of the lactose permease (lacY) and lysine decarboxylase Fig.
2. Stacked-bar chart displaying the results (percentage assignment) for each tool for each Shigella serotype in the development dataset. S, ShigaPass; T, ShigaTyper  [31] ; F, ShigEiFinder using SPAdes assemblies  [11] ; R, ShigEiFinder using short reads  [11]. SB, S. boydii; SD, S. dysenteriae; SON, S. sonnei; SF, S. flexneri; EIEC, entero-invasive E. coli; Not, Not Shigella/EIEC. 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 Tables  S8  and S9. For ShigEiFinder, only results for version 1. 3. 2 are displayed. (cadA) genes, in particular. These two metabolic genes are highly variable in Shigella and EIEC, and are, therefore, not optimal for differentiating between species  [10, 31, 52]. By contrast, the MLST used in ShigaPass is a standard tool in population genetics. ShigEiFinder accepts raw reads or assembled genomes, and uses the Shigella O-antigen genes database created by ShigaTyper (based on wzx and wzy genes only) for Shigella prediction, the database of E. coli O-and H-antigen genes of SerotypeFinder for EIEC prediction, and a set of 22 accessory genome genes to detect Shigella/EIEC clusters  [11]. In a previous study, we evaluated the performance of ShigEiFinder version 1.
2. 0. We found that this tool was not optimal for predicting Shigella serotypes, particularly if the prediction was based on short reads  [7]. Here, we evaluated the new version of ShigEiFinder version 1. 3. 2 released in April 2022. Initially, ShigEiFinder (short reads) uses the same coverage threshold (50 %) as ShigaTyper for the detection of wzx/wzy genes, but ShigEiFinder also uses the ratio of the mean mapping depth of wzx/wzy genes to the average mean mapping depth of seven HK genes (O/7HK ratio) to detect and eliminate low-level contamination or suboptimal sequencing data. ShigEiFinder version 1. 3. 2 adjusted the O/7HK ratio from 10 to 1 % (short reads). The combination of POAC genes used to predict S. flexneri serotypes (short reads and assemblies) was also modified. These changes increased the rates of correct Shigella serotype prediction for the validation dataset from 56.
5 ). ShigaPass outperforms ShigaTyper and ShigEiFinder, particularly for the identification of S. boydii and S. dysenteriae serotypes. The phenotypic identification of S. boydii and S. dysenteriae serotypes by conventional serotyping methods is especially challenging and laborious, requiring more than 40 antisera. This highlights the utility of ShigaPass as an in silico tool for the effective surveillance of these serotypes. The better overall performance of ShigaPass than of ShigaTyper and ShigEiFinder can be explained by multiple factors, including: (i) the use of an optimised rfb database covering all Shigella serogroups and serotypes, including, in particular, the new and provisional serotypes; (ii), the use of multiple genetic structures for identifying Shigella serotypes and differentiating between Shigella and EIEC; and (iii) the use of k-mers for the whole rfb cluster (excluding regions common to different serotypes) rather than just the wzx/wzy genes, thereby increasing the chances of serotype identification if the genomic sequences obtained do not cover the rfb cluster very well.
boydii 6 is an extremely rare serotype, and only one S. boydii 6 genome was used in our study. Finally, the lack of inclusion of rfb sequences from the new serotypes (S. dysenteriae 16, 17 and prov. BEDP 02-5104) in the ShigaTyper and ShigEiFinder databases is probably the main reason for their inability to identify these new serotypes. S. flexneri serotyping is laborious and relies on a panel of 12 antisera raised against specific type and group factors. Some new antisera are not commercially available, and both this and the cross-reactivity observed with some serotypes also complicates the serotyping of S. flexneri 1-5, X and Y. Serotype conversion, mediated by prophages and plasmids carrying O-antigen-modifying genes, is frequent and well documented in S. flexneri from the S3 cluster  [7, 20, 27, 53].
flexneri serotypes and subserotypes  [7]. Assembly-and blast-based methods are largely dependent on sequencing and assembly methods and quality, and do not, therefore, necessarily provide unequivocal evidence of the performance of ShigaPass. False-negative results may arise in cases of missing data. The rfb and POAC genes have AT-rich sequences. Most of our sequences were obtained with the Nextera XT kit, which is known to be biased against AT-rich regions. In addition, the sequencing protocol used by our public health multi-pathogen sequencing platform may also be biased against such regions. Overall, this results in assemblies with short contigs and poorer coverage of these AT-rich regions. ShigaPass can overcome this problem with the k-mer strategy. However, we found that the accuracy of serotype prediction was lower when EnteroBase SPAdes assemblies from our validation dataset were used.
This decrease in accuracy led to false-negative results, mainly because the assembly method used by EnteroBase is stringent and removes low-coverage contigs, including those containing the rfb and POAC genes  [33]. It is therefore important, for optimal ShigaPass performance, not to filter out short and low-coverage contigs (especially when using sequencing protocols biased against AT-rich regions). In this study, we used only SPAdes, a widely used assembler, to generate the assemblies, with no evaluation of other assemblers. However, the short reads from our development and validation datasets could be used to evaluate the performance of ShigaPass with different assemblers if necessary. False-positive results may arise due to mixtures of two or more Shigella genomes. In this case, ShigaPass issues a comment mentioning the presence of multiple rfb genes.
CRISPR profiles in the Shigella strains of the development dataset the number of rfb hits is displayed to alert the user. In cases of more than one leading rfb with equal numbers of hits (a situation never observed with either our development or validation set), the rfb is selected randomly. When ShigaPass cannot assign a Shigella serotype, it returns a 'Shigella spp. ' or 'EIEC' result. 'Shigella spp. ' is assigned if the script fails to recognize a matching profile, but does recognize a specific Shigella MLST. This assignment may be encountered if the genome is contaminated, of poor quality, or in cases of new emerging serotypes. 'EIEC' is identified if ipaH is detected but no matching profile is identified. ShigaPass is freely available as a standalone version from GitHub (https://github.