Table 3 Whole-genome sequencing in outbreak investigations: opportunities and challenges
From: Genomics and outbreak investigation: from sequence to consequence
Feature | Opportunities | Challenges |
|---|---|---|
Sequence generation | Provision of data on a timescale that allows clinical interventions Costs now comparable to those of other clinically relevant expenditure (such as of antibiotic treatment or bed occupancy) Use now comparable to that of other automated laboratory systems Delivers far richer data than any previous method Potential for open-ended one-size-fits-all culture-independent workflow | Chasing a moving target: difficult to devise stable and agreed standard operating procedures in the face of relentless technical innovation Proof needed that WGS cost-effective across a range of clinical applications Difficulties in predicting phenotype from genotype Still sufficiently technically demanding to require input of skilled staff Resistance to adoption of potentially disruptive technology |
Data handling | Provides portable, digital, library-based approach | Large datasets require significant hardware for storage and analysis Need for standardized, robust, user-friendly analysis pipelines Issues over data storage, ownership and presentation need to be resolved Integration with healthcare informatics systems to allow easy communication with clinicians |
Epidemiological analysis | WGS provides highest possible resolution Potential to link pathogen discovery, biology and evolution with phylogeny and epidemiology to facilitate iterative hypothesis generation, testing and refinement | Need to move beyond SNP typing of draft genomes of colony-purified isolates to embrace full range of genome variation, including within-patient variation Better integration with conventional epidemiology required to place data in context and evaluate hypothesized routes of transmissions Acquiring clinical metadata often remains a bottleneck |