Dataset Information

Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: Neisseria meningitidis as a Proof-of-Concept.

ABSTRACT: Despite being a well-established research method, the use of whole-genome sequencing (WGS) for routine molecular typing and pathogen characterization remains a substantial challenge due to the required bioinformatics resources and/or expertise. Moreover, many national reference laboratories and centers, as well as other laboratories working under a quality system, require extensive validation to demonstrate that employed methods are "fit-for-purpose" and provide high-quality results. A harmonized framework with guidelines for the validation of WGS workflows does currently, however, not exist yet, despite several recent case studies highlighting the urgent need thereof. We present a validation strategy focusing specifically on the exhaustive characterization of the bioinformatics analysis of a WGS workflow designed to replace conventionally employed molecular typing methods for microbial isolates in a representative small-scale laboratory, using the pathogen Neisseria meningitidis as a proof-of-concept. We adapted several classically employed performance metrics specifically toward three different bioinformatics assays: resistance gene characterization (based on the ARG-ANNOT, ResFinder, CARD, and NDARO databases), several commonly employed typing schemas (including, among others, core genome multilocus sequence typing), and serogroup determination. We analyzed a core validation dataset of 67 well-characterized samples typed by means of classical genotypic and/or phenotypic methods that were sequenced in-house, allowing to evaluate repeatability, reproducibility, accuracy, precision, sensitivity, and specificity of the different bioinformatics assays. We also analyzed an extended validation dataset composed of publicly available WGS data for 64 samples by comparing results of the different bioinformatics assays against results obtained from commonly used bioinformatics tools. We demonstrate high performance, with values for all performance metrics >87%, >97%, and >90% for the resistance gene characterization, sequence typing, and serogroup determination assays, respectively, for both validation datasets. Our WGS workflow has been made publicly available as a "push-button" pipeline for Illumina data at https://galaxy.sciensano.be to showcase its implementation for non-profit and/or academic usage. Our validation strategy can be adapted to other WGS workflows for other pathogens of interest and demonstrates the added value and feasibility of employing WGS with the aim of being integrated into routine use in an applied public health setting.

SUBMITTER: Bogaerts B

PROVIDER: S-EPMC6414443 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: <i>Neisseria meningitidis</i> as a Proof-of-Concept.

Bogaerts Bert B Winand Raf R Fu Qiang Q Van Braekel Julien J Ceyssens Pieter-Jan PJ Mattheus Wesley W Bertrand Sophie S De Keersmaecker Sigrid C J SCJ Roosens Nancy H C NHC Vanneste Kevin K

Frontiers in microbiology 20190306

Despite being a well-established research method, the use of whole-genome sequencing (WGS) for routine molecular typing and pathogen characterization remains a substantial challenge due to the required bioinformatics resources and/or expertise. Moreover, many national reference laboratories and centers, as well as other laboratories working under a quality system, require extensive validation to demonstrate that employed methods are "fit-for-purpose" and provide high-quality results. A harmonize ...[more]

PMID: 30894839

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Project description:Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context.

Project description:Neisseria meningitidis is the causative agent of meningococcal sepsis and meningitis. Neisseria polysaccharea is a nonpathogenic species. N. polysaccharea is able to use sucrose to produce amylopectin, a starch-like polysaccharide, which distinguishes it biochemically from the pathogenic species N. meningitidis. The data presented here indicate that this may be an insufficient criterion to distinguish between these two species. The nonencapsulated Neisseria strain 93246 expressed a phenotype of amylopectin production similar to that of N. polysaccharea. However, strain 93246 reacted with N. meningitidis serotype 4 and serosubtype P1.14 monoclonal antibodies and showed the N. meningitidis L1(8) lipo-oligosaccharide immunotype. Further analyses were performed on four genetic loci in strain 93246, and the results were compared with 7 N. meningitidis strains, 13 N. polysaccharea strains, and 2 N. gonorrhoeae strains. Three genetic loci, opcA, siaD, and lgt-1 in strain 93246, were the same as in N. meningitidis. Particularly, the siaD gene encoding polysialyltransferase responsible for biosynthesis of N. meningitidis group B capsule was detected in strain 93246. This siaD gene was inactivated by a frameshift mutation at the poly(C) tract, which makes strain 93246 identical to other nonencapsulated N. meningitidis strains. As expected, the ams gene encoding amylosucrase, responsible for production of amylopectin from sucrose, was detected in strain 93246 and all 13 N. polysaccharea strains but not in N. meningitidis and N. gonorrhoeae strains. These data suggest that strain 93246 is nonencapsulated N. meningitidis but has the ability to produce extracellular amylopectin from sucrose. The gene for amylopectin production in strain 93246 was likely imported from N. polysaccharea by horizontal genetic exchange. Therefore, we conclude that genetic analysis is required to complement the traditional phenotypic classification for the nonencapsulated Neisseria strains.

Dataset Information

Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: Neisseria meningitidis as a Proof-of-Concept.

Publications

Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: <i>Neisseria meningitidis</i> as a Proof-of-Concept.

Similar Datasets

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