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Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept

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Abstract
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.
Keywords
LISTERIA-MONOCYTOGENES, LEGIONELLA-PNEUMOPHILA, PHYLOGENETIC ANALYSIS, GENETIC DIVERSITY, TYPING SYSTEMS, OUTBREAK, RECOMBINATION, WGS, IMPLEMENTATION, PATHOGENS, Neisseria meningitidis, whole genome sequencing, public health, subtyping, data analysis, benchmarking, cgMLST, SNP

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MLA
Saltykova, Assia, et al. “Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing : Neisseria Meningitidis as a Proof of Concept.” FRONTIERS IN MICROBIOLOGY, vol. 10, 2019.
APA
Saltykova, A., Mattheus, W., Bertrand, S., Roosens, N. H., Marchal, K., & De Keersmaecker, S. C. (2019). Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept. FRONTIERS IN MICROBIOLOGY, 10.
Chicago author-date
Saltykova, Assia, Wesley Mattheus, Sophie Bertrand, Nancy HC Roosens, Kathleen Marchal, and Sigrid CJ De Keersmaecker. 2019. “Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing : Neisseria Meningitidis as a Proof of Concept.” FRONTIERS IN MICROBIOLOGY 10.
Chicago author-date (all authors)
Saltykova, Assia, Wesley Mattheus, Sophie Bertrand, Nancy HC Roosens, Kathleen Marchal, and Sigrid CJ De Keersmaecker. 2019. “Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing : Neisseria Meningitidis as a Proof of Concept.” FRONTIERS IN MICROBIOLOGY 10.
Vancouver
1.
Saltykova A, Mattheus W, Bertrand S, Roosens NH, Marchal K, De Keersmaecker SC. Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept. FRONTIERS IN MICROBIOLOGY. 2019;10.
IEEE
[1]
A. Saltykova, W. Mattheus, S. Bertrand, N. H. Roosens, K. Marchal, and S. C. De Keersmaecker, “Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept,” FRONTIERS IN MICROBIOLOGY, vol. 10, 2019.
@article{8640905,
  abstract     = {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.},
  articleno    = {2897},
  author       = {Saltykova, Assia and Mattheus, Wesley and Bertrand, Sophie and Roosens, Nancy HC and Marchal, Kathleen and De Keersmaecker, Sigrid CJ},
  issn         = {1664-302X},
  journal      = {FRONTIERS IN MICROBIOLOGY},
  keywords     = {LISTERIA-MONOCYTOGENES,LEGIONELLA-PNEUMOPHILA,PHYLOGENETIC ANALYSIS,GENETIC DIVERSITY,TYPING SYSTEMS,OUTBREAK,RECOMBINATION,WGS,IMPLEMENTATION,PATHOGENS,Neisseria meningitidis,whole genome sequencing,public health,subtyping,data analysis,benchmarking,cgMLST,SNP},
  language     = {eng},
  pages        = {24},
  title        = {Detailed evaluation of data analysis tools for subtyping of bacterial isolates based on whole genome sequencing : Neisseria meningitidis as a proof of concept},
  url          = {http://dx.doi.org/10.3389/fmicb.2019.02897},
  volume       = {10},
  year         = {2019},
}

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