Practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology

Rossen, John W. A.; Friedrich, Alexander; Moran-Gilad, Jacob

doi:10.1016/j.cmi.2017.11.001

Cited by 212 publications

(186 citation statements)

References 24 publications

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“…The ECDC study reported the requirement for sufficient bioinformatics expertise as one of the important hurdles to a more general implementation of NGS for routine testing 2 . This observation has also been expressed in recent case studies and reviews 7– 11 .…”

Section: Introductionsupporting

confidence: 58%

“…This complex - and dynamic - reality poses a challenge for the implementation of bioinformatics pipelines in regulatory settings, where the demonstration of reliability and reproducibility is crucial (see also 11, 29). Harmonisation approaches must face the variability described above in terms of technologies, strategies, and software used, each with their demonstrated success, limitations and caveats.…”

Section: Introductionmentioning

confidence: 99%

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The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

et al. 2018

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Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms. In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed. NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced. Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process. This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017. Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a “One Health” approach.

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Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

et al. 2018

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“…Whole-genome sequencing (WGS) is emerging as a routine clinical test that could be used to determine the bacterial species, undertake transmission tracking and identify multiple AMR associated mutations and genes in a single assay [8][9][10][11][12][13]. Whilst the initial clinical roll-out of WGS has used bacterial isolates, metagenomics and sequencing direct from clinical samples are future possibilities [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

Doyle

O’Sullivan

Aller

et al. 2019

Preprint

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BackgroundAntimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a 'one-stop' test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results. MethodsWe produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams ('participants') were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime. ResultsIndividual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant. ConclusionsWe found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

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“…Typical workflow of genomic epidemiology may thus necessitate multiple colony picks per sample and the corresponding DNA library preparation and sequencing steps for each of them. Combined, these steps require a significant amount of laboratory effort and time, and lead to increased costs since the price of library preparation is becoming comparable to the cost of sequencing itself (Rossen et al 2018) . This can act as a barrier to more widespread genomic pathogen surveillance even in well-resourced public health laboratories, and prevent application of genomic epidemiology altogether in poorer settings.…”

Section: Introductionmentioning

confidence: 99%

Genomic Epidemiology with Mixed Samples

Mäklin

Kallonen

Alanko

et al. 2020

Preprint

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Genomic epidemiology is an established tool for investigation of outbreaks of infectious diseases and wider public health applications. It traces transmission of pathogens based on whole-genome sequencing of colony picks from culture plates enriching the target organism(s). In this article, we introduce the mGEMS pipeline for performing genomic epidemiology directly with plate sweeps representing mixed samples of the target pathogen in a culture plate, skipping the colony pick step entirely. By requiring only a single culturing and library preparation step per analyzed sample, we address several key issues in the current approach relating to its cost, practical application and sensitivity. Our pipeline significantly improves upon the state-of-the-art in analysing mixed short-read sequencing data from bacteria, reaching accuracy levels in downstream analyses closely resembling colony pick sequencing data that allow reliable SNP calling and subsequent phylogenetic analyses. The fundamental novel parts enabling these analyses are the mGEMS read binner for probabilistic assignments of sequencing reads and the high-throughput exact pseudoaligner Themisto. In conjunction with recent advances in probabilistic modelling of mixed bacterial samples and genome assembly techniques, these tools form the mGEMS pipeline. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting for the three major pathogens Enterococcus faecalis , Escherichia coli and Staphylococcus aureus . Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

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Practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology

Cited by 212 publications

References 24 publications

The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

Genomic Epidemiology with Mixed Samples

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