Dataset Information

ShigaPass: an in silico tool predicting Shigella serotypes from whole-genome sequencing assemblies.

ABSTRACT: Shigella is one of the commonest causes of diarrhoea worldwide and a major public health problem. Shigella serotyping is based on a standardized scheme that splits Shigella strains into four serogroups and 60 serotypes on the basis of biochemical tests and O-antigen structures. This conventional serotyping method is laborious, time-consuming, impossible to automate, and requires a high level of expertise. Whole-genome sequencing (WGS) is becoming more affordable and is now used for routine surveillance, opening up possibilities for the development of much-needed accurate rapid typing methods. Here, we describe ShigaPass, a new in silico tool for predicting Shigella serotypes from WGS assemblies on the basis of rfb gene cluster DNA sequences, phage and plasmid-encoded O-antigen modification genes, seven housekeeping genes (EnteroBase's MLST scheme), fliC alleles and clustered regularly interspaced short palindromic repeats (CRISPR) spacers. Using 4879 genomes, including 4716 reference strains and clinical isolates of Shigella characterized with a panel of biochemical tests and serotyped by slide agglutination, we show here that ShigaPass outperforms all existing in silico tools, particularly for the identification of Shigella boydii and Shigella dysenteriae serotypes, with a correct serotype assignment rate of 98.5 % and a sensitivity rate (i.e. ability to make any prediction) of 100 %.

SUBMITTER: Yassine I

PROVIDER: S-EPMC10132075 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

ShigaPass: an in silico tool predicting Shigella serotypes from whole-genome sequencing assemblies.

Yassine Iman I Hansen Elisabeth E EE Lefèvre Sophie S Ruckly Corinne C Carle Isabelle I Lejay-Collin Monique M Fabre Laetitia L Rafei Rayane R Pardos de la Gandara Maria M Daboussi Fouad F Shahin Ahmad A Weill François-Xavier FX

Microbial genomics 20230301 3

Shigella is one of the commonest causes of diarrhoea worldwide and a major public health problem. Shigella serotyping is based on a standardized scheme that splits Shigella strains into four serogroups and 60 serotypes on the basis of biochemical tests and O-antigen structures. This conventional serotyping method is laborious, time-consuming, impossible to automate, and requires a high level of expertise. Whole-genome sequencing (WGS) is becoming more affordable and is now u ...[more]

PMID: 36951906

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Project description:Bacteria of the genus Shigella, consisting of 4 species and >50 serotypes, cause shigellosis, a foodborne disease of significant morbidity, mortality, and economic loss worldwide. Classical Shigella identification based on selective media and serology is tedious, time-consuming, expensive, and not always accurate. A molecular diagnostic assay does not distinguish Shigella at the species level or from enteroinvasive Escherichia coli (EIEC). We inspected genomic sequences from 221 Shigella isolates and observed low concordance rates between conventional designation and molecular serotyping: 86.4% and 80.5% at the species and serotype levels, respectively. Serotype determinants for 6 additional serotypes were identified. Examination of differentiation gene markers commonly perceived as characteristic hallmarks in Shigella showed high variability among different serotypes. Using this information, we developed ShigaTyper, an automated workflow that utilizes limited computational resources to accurately and rapidly determine 59 Shigella serotypes using Illumina paired-end whole-genome sequencing (WGS) reads. Shigella serotype determinants and species-specific diagnostic markers were first identified through read alignment to an in-house curated reference sequence database. Relying on sequence hits that passed a threshold level of coverage and accuracy, serotype could be unambiguously predicted within 1 min for an average-size WGS sample of ∼500 MB. Validation with WGS data from 380 isolates showed an accuracy rate of 98.2%. This pipeline is the first step toward building a comprehensive WGS-based analysis pipeline of Shigella spp. in a field laboratory setting, where speed is essential and resources need to be more cost-effectively dedicated.IMPORTANCEShigella causes diarrheal disease with serious public health implications. However, conventional Shigella identification methods are laborious and time-consuming and can be erroneous due to the high similarity between Shigella and enteroinvasive Escherichia coli (EIEC) and cross-reactivity between serotyping antisera. Further, serotype interpretation is complicated for inexperienced users. To develop an easier method with higher accuracy based on whole-genome sequencing (WGS) for Shigella serotyping, we systematically examined genomic information of Shigella isolates from 53 serotypes to define rules for differentiation and serotyping. We created ShigaTyper, an automated pipeline that accurately and rapidly excludes non-Shigella isolates and identifies 59 Shigella serotypes using Illumina paired-end WGS reads. A serotype can be unambiguously predicted at a data processing speed of 538 MB/min with 98.2% accuracy from a regular laptop. Once it is installed, training in bioinformatics analysis and Shigella genetics is not required. This pipeline is particularly useful to general microbiologists in field laboratories.

Project description:BackgroundWith the rapid increase in genome sequencing projects for non-model organisms, numerous genome assemblies are currently in progress or available as drafts, but not made available as satisfactory, usable genomes. Data quality assessment of genome assemblies is gaining importance not only for people who perform the assembly/re-assembly processes, but also for those who attempt to use assemblies as maps in downstream analyses. Recent studies of the quality control, quality evaluation/ assessment of genome assemblies have focused on either quality control of reads before assemblies or evaluation of the assemblies with respect to their contiguity and correctness. However, correctness assessment depends on a reference and is not applicable for de novo assembly projects. Hence, development of methods providing both post-assembly and pre-assembly quality assessment reports for examining the quality/correctness of de novo assemblies and the input reads is worth studying.ResultsWe present SQUAT, an efficient tool for both pre-assembly and post-assembly quality assessment of de novo genome assemblies. The pre-assembly module of SQUAT computes quality statistics of reads and presents the analysis in a well-designed interface to visualize the distribution of high- and poor-quality reads in a portable HTML report. The post-assembly module of SQUAT provides read mapping analytics in an HTML format. We categorized reads into several groups including uniquely mapped reads, multiply mapped, unmapped reads; for uniquely mapped reads, we further categorized them into perfectly matched, with substitutions, containing clips, and the others. We carefully defined the poorly mapped (PM) reads into several groups to prevent the underestimation of unmapped reads; indeed, a high PM% would be a sign of a poor assembly that requires researchers' attention for further examination or improvements before using the assembly. Finally, we evaluate SQUAT with six datasets, including the genome assemblies for eel, worm, mushroom, and three bacteria. The results show that SQUAT reports provide useful information with details for assessing the quality of assemblies and reads.AvailabilityThe SQUAT software with links to both its docker image and the on-line manual is freely available at https://github.com/luke831215/SQUAT .

Dataset Information

ShigaPass: an in silico tool predicting Shigella serotypes from whole-genome sequencing assemblies.

Publications

ShigaPass: an <i>in silico</i> tool predicting <i>Shigella</i> serotypes from whole-genome sequencing assemblies.

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