Considerations in centralizing whole genome sequencing for microbiology in a public health setting

Arnold, Cath

doi:10.1586/14737159.2016.1164039

Cited by 12 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the clinical and public microbiology community implements high-quality WGS, it would be opportune to consider the available models for the delivery of these services (48). Since their inception, most WGS activities have taken place in reference facilities with rather large supporting infrastructures.…”

Section: Discussionmentioning

confidence: 99%

“…The local-delivery model would also be more responsive to the needs of the target clients and enhance the adoption of WGS across health care systems. Another alternative is a hybrid model with complementary central and local services to balance the need for speed with advanced expertise and resources (48). Two prominent examples of hybrid models in the United States are the FDA GenomeTrakr network for the tracking of foodborne pathogens and the CDC Advanced Molecular Detection (AMD) initiative for the improved surveillance of infectious diseases (49, 50).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

et al. 2017

View full text Add to dashboard Cite

Public health microbiology laboratories (PHLs) are on the cusp of unprecedented improvements in pathogen identification, antibiotic resistance detection, and outbreak investigation by using whole-genome sequencing (WGS). However, considerable challenges remain due to the lack of common standards. Here, we describe the validation of WGS on the Illumina platform for routine use in PHLs according to Clinical Laboratory Improvements Act (CLIA) guidelines for laboratory-developed tests (LDTs). We developed a validation panel comprising 10 Enterobacteriaceae isolates, 5 Gram-positive cocci, 5 Gram-negative nonfermenting species, 9 Mycobacterium tuberculosis isolates, and 5 miscellaneous bacteria. The genome coverage range was 15.71× to 216.4× (average, 79.72×; median, 71.55×); the limit of detection (LOD) for single nucleotide polymorphisms (SNPs) was 60×. The accuracy, reproducibility, and repeatability of base calling were >99.9%. The accuracy of phylogenetic analysis was 100%. The specificity and sensitivity inferred from multilocus sequence typing (MLST) and genome-wide SNP-based phylogenetic assays were 100%. The following objectives were accomplished: (i) the establishment of the performance specifications for WGS applications in PHLs according to CLIA guidelines, (ii) the development of quality assurance and quality control measures, (iii) the development of a reporting format for end users with or without WGS expertise, (iv) the availability of a validation set of microorganisms, and (v) the creation of a modular template for the validation of WGS processes in PHLs. The validation panel, sequencing analytics, and raw sequences could facilitate multilaboratory comparisons of WGS data. Additionally, the WGS performance specifications and modular template are adaptable for the validation of other platforms and reagent kits.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Analysis of WGS data offers unparalleled discriminatory power for comparing bacterial isolates while also providing a wide range of analytical options (e.g., analysis of single nucleotide polymorphisms, gene-by-gene sequence-based typing, and gene content analysis) that facilitate in silico comparisons with legacy data sets (28, 35). Furthermore, WGS-based analysis has become sufficiently cost-effective to allow an increasing number of public health laboratories to focus their efforts on the generation of WGSs for isolates collected through routine surveillance (36), resulting in an explosive growth in the number of isolates being analyzed and the concomitant phasing out of molecular typing methods in the very near future. In this context, the potential utility of the framework proposed here resides in the scalability of a scriptable, systematic approach that allows efficient and automatable computation of epidemiological signals, epidemiological similarity, and epidemiological concordance across very large data sets and the flexibility to support different epidemiological applications.…”

Section: Discussionmentioning

confidence: 99%

The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data

et al. 2017

View full text Add to dashboard Cite

A fundamental assumption in the use and interpretation of microbial subtyping results for public health investigations is that isolates that appear to be related based on molecular subtyping data are expected to share commonalities with respect to their origin, history, and distribution. Critically, there is currently no approach for systematically assessing the underlying epidemiology of subtyping results. Our aim was to develop a method for directly quantifying the similarity between bacterial isolates using basic sampling metadata and to develop a framework for computing the epidemiological concordance of microbial typing results. We have developed an analytical model that summarizes the similarity of bacterial isolates using basic parameters typically provided in sampling records, using a novel framework (EpiQuant) developed in the R environment for statistical computing. We have applied the EpiQuant framework to a data set comprising 654 isolates of the enteric pathogen Campylobacter jejuni from Canadian surveillance data in order to examine the epidemiological concordance of clusters obtained by using two leading C. jejuni subtyping methods. The EpiQuant framework can be used to directly quantify the similarity of bacterial isolates based on basic sample metadata. These results can then be used to assess the concordance between microbial epidemiological and molecular data, facilitating the objective assessment of subtyping method performance and paving the way for the improved application of molecular subtyping data in investigations of infectious disease.

show abstract

“…As the clinical and public microbiology community implements high-quality WGS, it would be opportune to consider the available models for the delivery of these services [54]. Since their inception, most WGS activities have taken place in the reference facilities with rather large supporting infrastructure.…”

Section: Discussionmentioning

confidence: 99%

“…The local delivery model would also be more responsive to the needs of the target client and enhance the adoption of WGS across the healthcare systems. Another alternative is a hybrid model with complimentary central and local services to balance the need for speed with the advanced expertise and resources [54]. Two prominent examples of the hybrid models in the United States are the Food and Drug Administration (FDA) GenomeTrakr network for the tracking of food-borne pathogens, and the CDC Advanced Molecular Detection (AMD) initiative for the improved surveillance of infectious diseases [55, 56].…”

Section: Discussionmentioning

confidence: 99%

Validation and Implementation of CLIA-Compliant Whole Genome Sequencing (WGS) in Public Health Laboratory

Truong²,

Greninger³

et al. 2017

Preprint

View full text Add to dashboard Cite

BackgroundPublic health microbiology laboratories (PHL) are at the cusp of unprecedented improvements in pathogen identification, antibiotic resistance detection, and outbreak investigation by using whole genome sequencing (WGS). However, considerable challenges remain due to the lack of common standards.Objectives1) Establish the performance specifications of WGS applications used in PHL to conform with CLIA (Clinical Laboratory Improvements Act) guidelines for laboratory developed tests (LDT), 2) Develop quality assurance (QA) and quality control (QC) measures, 3) Establish reporting language for end users with or without WGS expertise, 4) Create a validation set of microorganisms to be used for future validations of WGS platforms and multi-laboratory comparisons and, 5) Create modular templates for the validation of different sequencing platforms.MethodsMiSeq Sequencer and Illumina chemistry (Illumina, Inc.) were used to generate genomes for 34 bacterial isolates with genome sizes from 1.8 to 4.7 Mb and wide range of GC content (32.1%-66.1%). A customized CLCbio Genomics Workbench - shell script bioinformatics pipeline was used for the data analysis.ResultsWe developed a validation panel comprising ten Enterobacteriaceae isolates, five gram-positive cocci, five gram-negative non-fermenting species, nine Mycobacterium tuberculosis, and five miscellaneous bacteria; the set represented typical workflow in the PHL. The accuracy of MiSeq platform for individual base calling was >99.9% with similar results shown for reproducibility/repeatability of genome-wide base calling. The accuracy of phylogenetic analysis was 100%. The specificity and sensitivity inferred from MLST and genotyping tests were 100%. A test report format was developed for the end users with and without WGS knowledge.ConclusionWGS was validated for routine use in PHL according to CLIA guidelines for LDTs. The validation panel, sequencing analytics, and raw sequences will be available for future multi-laboratory comparisons of WGS in PHL. Additionally, the WGS performance specifications and modular validation template are likely to be adaptable for the validation of other platforms and reagents kits.

show abstract

Considerations in centralizing whole genome sequencing for microbiology in a public health setting

Cited by 12 publications

References 12 publications

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

Validation and Implementation of Clinical Laboratory Improvements Act-Compliant Whole-Genome Sequencing in the Public Health Microbiology Laboratory

The EpiQuant Framework for Computing Epidemiological Concordance of Microbial Subtyping Data

Validation and Implementation of CLIA-Compliant Whole Genome Sequencing (WGS) in Public Health Laboratory

Contact Info

Product

Resources

About