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• W2892078430 abstract "In principle, WGS can provide highly relevant information for clinical microbiology in near-real-time, from phenotype testing to tracking outbreaks. However, despite this promise, the uptake of WGS in the clinic has been limited to date, and future implementation is likely to be a slow process. The increasing information provided by WGS can cause conflict with traditional microbiological concepts and typing schemes. Decreasing raw sequencing costs have not translated into decreasing total costs for bacterial genomes, which have stabilised. Existing research pipelines are not suitable for the clinic, and bespoke clinical pipelines should be developed. Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections (e.g., contaminated sinks) and reconstruction of transmission chains (i.e., who infected whom) could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions. Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections (e.g., contaminated sinks) and reconstruction of transmission chains (i.e., who infected whom) could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions. Thanks to progress in high-throughput sequencing technologies over the last two decades, generating microbial genomes is now considered neither particularly challenging nor expensive. As a result, whole-genome sequencing (WGS) (see Glossary) has been championed as the obvious and inevitable future of diagnostics in multiple reviews and opinion pieces dating back to 2010 [1Pallen M.J. et al.High-throughput sequencing and clinical microbiology: progress, opportunities and challenges.Curr. Opin. Microbiol. 2010; 13: 625-631Crossref PubMed Scopus (107) Google Scholar, 2Didelot X. et al.Transforming clinical microbiology with bacterial genome sequencing.Nat. Rev. Genet. 2012; 13: 601-612Crossref PubMed Scopus (507) Google Scholar, 3Koser C.U. et al.Routine use of microbial whole genome sequencing in diagnostic and public health microbiology.PLoS Pathog. 2012; 8: 9Crossref Scopus (374) Google Scholar, 4Fricke W.F. Rasko D.A. Bacterial genome sequencing in the clinic: bioinformatic challenges and solutions.Nat. Rev. Genet. 2014; 15: 49-55Crossref PubMed Scopus (110) Google Scholar]. Despite enthusiasm in the community, WGS diagnostics has not yet been widely adopted in clinical microbiology, which may seem at odds with the current suite of applications for which WGS has huge potential, and which are already widely used in the academic literature. Common applications of WGS in diagnostic microbiology include isolate characterization, antimicrobial resistance (AMR) profiling, and establishing the sources of recurrent infections and between-patient transmissions. All of these have obvious clinical relevance and provide case studies where WGS could, in principle, provide additional information and even replace the knowledge obtained through standard clinical microbiology techniques. This review reiterates the potential of WGS for clinical microbiology, but also its current limitations, and suggests possible solutions to some of the main bottlenecks to routine implementation. In particular, we argue that applying existing WGS pipelines developed for fundamental research is unlikely to produce the fast and robust tools required, and that new dedicated approaches are needed for WGS in the clinic. At the most basic level, WGS can be used to characterize a clinical isolate, informing on the likely species and/or subtype and allowing phylogenetic placement of a given sequence relative to an existing set of isolates. WGS-based strain identification gives a far superior resolution compared to genetic marker-based approaches such as multilocus sequence typing (MLST) and can be used when standard techniques such as pulsed-field gel electrophoresis (PFGE), variable-number tandem repeat (VNTR) profiling, and MALDI-TOF are unable to accurately distinguish lineages [5Neville S.A. et al.Utility of matrix-assisted laser desorption ionization-time of flight mass spectrometry following introduction for routine laboratory bacterial identification.J. Clin. Microbiol. 2011; 49: 2980-2984Crossref PubMed Scopus (156) Google Scholar]. WGS-informed strain identification could be of particular significance for bacteria with large accessory genomes, which encompass many of the clinically most problematic bacteria, where much of the relevant genetic diversity is driven by differences in the accessory genome on the chromosome and/or plasmid carriage. Somewhat ironically, the extremely rich information of WGS data, with every genome being unique, generates problems of its own. Clinical microbiology tends to rely on often largely ad hoc taxonomical nomenclature, such as biochemical serovars for Salmonella enterica or mycobacterial interspersed repetitive units (MIRUs) for Mycobacterium tuberculosis. While the rich information contained in WGS should in principle allow superseding traditional taxonomic classifications [6Eldholm V. et al.Armed conflict and population displacement as drivers of the evolution and dispersal of Mycobacterium tuberculosis.Proc. Natl. Acad. Sci. U. S. A. 2016; 113: 13881-13886Crossref PubMed Scopus (54) Google Scholar, 7Achtman M. et al.Multilocus sequence typing as a replacement for serotyping in Salmonella enterica.PLoS Pathog. 2012; 8: 19Crossref Scopus (434) Google Scholar], defining an intuitive, meaningful and rigorous classification for genome sequences represents a major challenge. For strictly clonal species, which undergo no horizontal gene transfer (HGT), such as M. tuberculosis, it is possible to devise a ‘natural’ robust phylogenetically based classification [8Coll F. et al.A robust SNP barcode for typing Mycobacterium tuberculosis complex strains.Nat. Commun. 2014; 5: 5Crossref Scopus (356) Google Scholar]. Unfortunately, organisms undergoing regular HGT, and with a significant accessory genome, do not fall neatly into existing classification schemes. In fact, it is even questionable whether a completely satisfactory classification scheme could be devised for such organisms, as classifications based on the core genome, accessory genome, housekeeping genes (MLST), genotypic markers, plasmid sequence, virulence factors or AMR profile may all produce incompatible categories (Figure 1). Beyond species identification and characterization, genome sequences provide a rich resource that can be exploited to predict the pathogen’s phenotype. The main microbial traits of clinical relevance are AMR and virulence, but may also include other traits such as the ability to form biofilms or survival in the environment. Sequence-based drug profiling is one of the pillars of HIV treatment and has to be credited for the remarkable success of antiretroviral therapy (ART) regimes. Prediction of AMR from sequence data has also received considerable attention for bacterial pathogens but has not led to comparable success at this stage. Resistance against single drugs can be relatively straightforward to predict in some instances. For example, the presence of the SCCmec cassette is a reliable predictor for broad-spectrum beta-lactam resistance in Staphylococcus aureus, with strains carrying this element referred to as methicillin-resistant S. aureus (MRSA). In principle, WGS offers the possibility to predict the full resistance profile to multiple drugs (the ‘resistome’). Possibly the first real attempt to predict the resistome from WGS data was a study by Holden et al. in 2013, showing that, for a large dataset of S. aureus ST22 isolates, 98.8% of all phenotypic resistances could be explained by at least one previously documented AMR element or mutation in the sequence data [9Holden M.T.G. et al.A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic.Genome Res. 2013; 23: 653-664Crossref PubMed Scopus (324) Google Scholar]. Since then, several tools have been developed for the prediction of resistance profiles from WGS. These include those designed for prediction of resistance phenotype from acquired AMR genes, including ResFinder [10Larsen M.V. et al.Benchmarking of methods for genomic taxonomy.J. Clin. Microbiol. 2014; 52: 1529-1539Crossref PubMed Scopus (174) Google Scholar] and ABRicate (https://github.com/tseemann/abricate), together with those also taking into account point mutations in chromosome-borne genes such as Arg-Annot [11Gupta S.K. et al.ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.Antimicrob. Agents Chemother. 2014; 58: 212-220Crossref PubMed Scopus (810) Google Scholar], the Sequence Search Tool for Antimicrobial Resistance (SSTAR) [12de Man T.J.B. Limbago B.M. SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor.mSphere. 2016; 1: 10Crossref Scopus (49) Google Scholar], and the Comprehensive Antibiotic Resistance Database (CARD) [12de Man T.J.B. Limbago B.M. SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor.mSphere. 2016; 1: 10Crossref Scopus (49) Google Scholar]. Of these, ResFinder and CARD can be implemented as online methods that, dependent on user traffic, can be considerably slower than most other tools that only use the command-line. They are, however, superior in terms of broad usability and are more intuitive than, for example, the graphical user interface of SSTAR. Other tools exist for richer species-specific characterization such as PhyResSE [13Feuerriegel S. et al.PhyResSE: a web tool delineating Mycobacterium tuberculosis antibiotic resistance and lineage from whole-genome sequencing data.J. Clin. Microbiol. 2015; 53: 1908-1914Crossref PubMed Scopus (185) Google Scholar] and PATRIC-RAST [14Davis J.J. et al.Antimicrobial resistance prediction in PATRIC and RAST.Sci. Rep. 2016; 6: 12Crossref PubMed Scopus (119) Google Scholar]. Further tools have been developed to predict phenotype directly from unassembled sequencing reads, bypassing genome assembly [15Coll F. et al.Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences.Genome Med. 2015; 7: 10Crossref PubMed Scopus (246) Google Scholar, 16Bradley P. et al.Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.Nat. Commun. 2015; 6: 14Crossref Scopus (334) Google Scholar]. It has been proposed that WGS-based phenotyping might, in some instances, be equally, if not more, accurate than traditional phenotyping [16Bradley P. et al.Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.Nat. Commun. 2015; 6: 14Crossref Scopus (334) Google Scholar, 17Tyson G.H. et al.WGS accurately predicts antimicrobial resistance in Escherichia coli.J. Antimicrob. Chemother. 2015; 70: 2763-2769Crossref PubMed Scopus (157) Google Scholar, 18Gordon N.C. et al.Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing.J. Clin. Microbiol. 2014; 52: 1182-1191Crossref PubMed Scopus (219) Google Scholar, 19Harris K.A. et al.Whole-genome sequencing and epidemiological analysis do not provide evidence for cross-transmission of Mycobacterium abscessus in a cohort of pediatric cystic fibrosis patients.Clin. Infect. Dis. 2015; 60: 1007-1016PubMed Google Scholar]. However, it is probably no coincidence that the most successful applications to date have primarily been on M. tuberculosis and S. aureus, which are characterised by essentially no, or very limited, accessory genomes, respectively. Other successful examples include streptococcal pathogens, where WGS-based predictions and measured phenotypic resistance show good agreement even in large and diverse samples of isolates [20Metcalf B.J. et al.Short-read whole genome sequencing for determination of antimicrobial resistance mechanisms and capsular serotypes of current invasive Streptococcus agalactiae recovered in the USA.Clin. Microbiol. Infect. 2017; 23: 8Abstract Full Text Full Text PDF Scopus (80) Google Scholar, 21Metcalf B.J. et al.Using whole genome sequencing to identify resistance determinants and predict antimicrobial resistance phenotypes for year 2015 invasive pneumococcal disease isolates recovered in the United States.Clin. Microbiol. Infect. 2016; 22: 8Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar]. On the whole, however, predicting comprehensive AMR profiles in organisms with open genomes, such as Escherichia coli, where only 6% of genes are found in every single strain [22Lukjancenko O. et al.Comparison of 61 sequenced Escherichia coli genomes.Microb. Ecol. 2010; 60: 708-720Crossref PubMed Scopus (365) Google Scholar], is challenging and requires extremely extensive and well curated reference databases. The transition to WGS might appear relatively straightforward if viewed as merely replacing PCR panels which are already used when traditional phenotyping can be cumbersome and unreliable. However, to put the problem in context, there are over 2000 described β-lactamase gene sequences responsible for multiresistance to β-lactam antibiotics such as penicillins, cephalosporins, and carbapenems [23Brandt C. et al.In silico serine beta-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.Sci. Rep. 2017; 7: 13Crossref PubMed Scopus (33) Google Scholar]. Whilst β-lactam resistance in some pathogens, including S. pneumoniae, can be predicted through, for example, penicillin-binding protein (PBP) typing and machine-learning-based approaches [24Li Y. et al.Validation of beta-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences.BMC Genomics. 2017; 18: 10Crossref PubMed Scopus (41) Google Scholar], the general problem of reliably assigning resistance phenotype based on many described gene sequences is commonplace. At this stage, many of the AMR reference databases are not well integrated or curated and have no minimum clinical standard. They often have varying predictive ranges and biases and produce fairly inaccessible output files with little guidance on how to interpret or utilise this information for clinical intervention. Perhaps because of these limitations, although of obvious benefit as part of a diagnostics platform, both awareness and uptake in the clinic has been limited. Additionally, with some notable exceptions, such as the pneumococci [24Li Y. et al.Validation of beta-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences.BMC Genomics. 2017; 18: 10Crossref PubMed Scopus (41) Google Scholar], most AMR profile predictions from WGS data are qualitative, simply predicting whether an isolate is expected to be resistant or susceptible against a compound despite AMR generally being a continuous and often complex trait. The level of resistance of a strain to a drug can be affected by multiple epistatic AMR elements or mutations [25Durão P. et al.Evolutionary mechanisms shaping the maintenance of antibiotic resistance.Trends Microbiol. 2018; 26: 677-691Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar], the copy number variation of these elements [26San Milian A. et al.Multicopy plasmids potentiate the evolution of antibiotic resistance in bacteria.Nat. Ecol. Evol. 2017; 1: 8PubMed Google Scholar], the function of the genetic background of the strain [27Bjorkholm B. et al.Mutation frequency and biological cost of antibiotic resistance in Helicobacter pylori.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 14607-14612Crossref PubMed Scopus (295) Google Scholar, 28Luo N.D. et al.Enhanced in vivo fitness of fluoroquinolone-resistant Campylobacter jejuni in the absence of antibiotic selection pressure.Proc. Natl. Acad. Sci. U. S. A. 2005; 102: 541-546Crossref PubMed Scopus (277) Google Scholar, 29MacLean R.C. et al.Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa.Genetics. 2010; 186: 1345-1354Crossref PubMed Scopus (105) Google Scholar], and modulating effects by the environment [30Baym M. et al.Spatiotemporal microbial evolution on antibiotic landscapes.Science. 2016; 353: 1147-1151Crossref PubMed Scopus (284) Google Scholar]. The level of resistance is generally well captured by the semiquantitative phenotypic measurement minimum inhibitory concentration (MIC), even if clinicians often use a discrete interpretation of MICs into resistant/susceptible based on fairly arbitrary cut-off values. Quantitative resistance predictions are not just of academic interest. In the clinic, low-level resistance strains can still be treated with a given antibiotic but the standard dose should be increased, which can be the best option at hand, especially for drugs with low toxicity. The majority of efforts to predict phenotypes from bacterial genomes have been on AMR profiling. Yet, some tools have also been developed for multispecies virulence profiling: the Virulence Factors Database (VFDB) [31Chen L.H. et al.VFDB 2016: hierarchical and refined dataset for big data analysis – 10 years on.Nucleic Acids Res. 2016; 44: D694-D697Crossref PubMed Scopus (734) Google Scholar] or VirulenceFinder [32Joensen K.G. et al.Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli.J. Clin. Microbiol. 2014; 52: 1501-1510Crossref PubMed Scopus (781) Google Scholar] as well as the bespoke virulence prediction tool for Klebsiella pneumoniae, Kleborate [33Lam M.M.C. et al.Genetic diversity, mobilisation and spread of the yersiniabactin-encoding mobile element ICEKp in Klebsiella pneumoniae populations.Microb. Genomics. 2018; 4e000196Crossref Scopus (152) Google Scholar]. One major challenge is that virulence is often a context-dependent trait. For example, in K. pneumoniae various imperfect proxies for virulence are used. These include capsule type, hypermucovisity, biofilm and siderophore production, or survival in serum-killing assays. While all of these traits are quantifiable and reproducible, and could thus in principle be predicted using WGS, it remains unclear how well they correlate with virulence in the patient. Given that virulence is one of the most commonly studied phenotypes, yet lacks a clear definition, the general problem of predicting bacterial phenotype from genotype may be substantially more complex than the special case of AMR, which is itself far from solved for all clinically relevant species. Beyond phenotype prediction for individual isolates, WGS has allowed reconstructing outbreaks within hospitals and the community across a diversity of taxa ranging from carbapenem-resistant K. pneumoniae [34Jiang Y. et al.Tracking a hospital outbreak of KPC-producing ST11 Klebsiella pneumoniae with whole genome sequencing.Clin. Microbiol. Infect. 2015; 21: 7Abstract Full Text Full Text PDF Scopus (38) Google Scholar, 35Sheppard A.E. et al.Nested Russian doll-like genetic mobility drives rapid dissemination of the carbapenem resistance gene bla(KPC).Antimicrob. Agents Chemother. 2016; 60: 3767-3778Crossref PubMed Scopus (171) Google Scholar, 36Yang S.X. et al.Evolution and transmission of carbapenem-resistant Klebsiella pneumoniae expressing the bla(OXA-232) gene during an institutional outbreak associated with endoscopic retrograde cholangiopancreatography.Clin. Infect. Dis. 2017; 64: 894-901Crossref PubMed Scopus (29) Google Scholar] and Acinetobacter baumannii [37Fitzpatrick M.A. et al.Utility of whole-genome sequencing in characterizing Acinetobacter epidemiology and analyzing hospital outbreaks.J. Clin. Microbiol. 2016; 54: 593-612Crossref PubMed Scopus (65) Google Scholar] to MRSA [38Koser C.U. et al.Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak.N. Engl. J. Med. 2012; 366: 2267-2275Crossref PubMed Scopus (490) Google Scholar, 39Kong Z.Z. et al.Whole-genome sequencing for the investigation of a hospital outbreak of MRSA in China.PLoS One. 2016; 11: 12Google Scholar], streptococcal disease [40Nanduri S.A. et al.Prolonged and large outbreak of invasive group A Streptococcus disease within a nursing home: repeated intrafacility transmission of a single strain.Clin. Microbiol. Infect. 2018; (Published online May 18, 2018)https://doi.org/10.1016/j.cmi.2018.04.034Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar], and Neisseria gonorrhoea [41Didelot X. et al.Genomic analysis and comparison of two gonorrhea outbreaks.mBio. 2016; 7: 8Crossref Scopus (42) Google Scholar], amongst others. WGS can reveal which isolates are part of an outbreak lineage and, by integrating epidemiological data with phylogenetic information, detect direct probable transmission events [42Klinkenberg D. et al.Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.PLoS Comput. Biol. 2017; 13: 32Crossref Scopus (63) Google Scholar, 43Eldholm V. et al.Impact of HIV co-infection on the evolution and transmission of multidrug-resistant tuberculosis.eLife. 2016; 5: 19Crossref Google Scholar, 44Didelot X. et al.Bayesian inference of infectious disease transmission from whole-genome sequence data.Mol. Biol. Evol. 2014; 31: 1869-1879Crossref PubMed Scopus (142) Google Scholar, 45Didelot X. et al.Microevolutionary analysis of Clostridium difficile genomes to investigate transmission.Genome Biol. 2012; 13: 13Crossref Scopus (148) Google Scholar]. Timed phylogenies, for example generated through BEAST [46Bouckaert R. et al.BEAST 2: a software platform for Bayesian evolutionary analysis.PLoS Comput. Biol. 2014; 10: e1003537Crossref PubMed Scopus (4094) Google Scholar, 47Drummond A.J. et al.Bayesian phylogenetics with BEAUti and the BEAST 1.7.Mol. Biol. Evol. 2012; 29: 1969-1973Crossref PubMed Scopus (7814) Google Scholar], can provide likely time-windows on inferred transmissions, as well as dating when an outbreak lineage may have started to expand. Approaches based on transmission chains can also be used to identify sources of recurrent infections (so called ‘super-spreaders’), and do not necessarily rely on all isolates within the outbreak having been sequenced, allowing for partial sampling and analyses of ongoing outbreaks [48Didelot X. et al.Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks.Mol. Biol. Evol. 2017; 34: 997-1007PubMed Google Scholar]. In this way WGS-based inference can elucidate patterns of infection which are impossible to recapitulate from standard sequence typing alone [35Sheppard A.E. et al.Nested Russian doll-like genetic mobility drives rapid dissemination of the carbapenem resistance gene bla(KPC).Antimicrob. Agents Chemother. 2016; 60: 3767-3778Crossref PubMed Scopus (171) Google Scholar]. However, WGS-informed outbreak tracking is usually performed only retrospectively. Typically, the publication dates of academic literature relating to outbreak reconstruction lag greatly, often in the order of at least 5 years since the initial identification of an outbreak [49Price J.R. et al.Transmission of Staphylococcus aureus between health-care workers, the environment, and patients in an intensive care unit: a longitudinal cohort study based on whole-genome sequencing.Lancet Infect. Dis. 2017; 17: 207-214Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar, 50De Silva D. et al.Whole-genome sequencing to determine transmission of Neisseria gonorrhoeae: an observational study.Lancet Infect. Dis. 2016; 16: 1295-1303Abstract Full Text Full Text PDF PubMed Scopus (109) Google Scholar]. Even analyses published more rapidly are generally still too slow to inform on real-time interventions [38Koser C.U. et al.Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak.N. Engl. J. Med. 2012; 366: 2267-2275Crossref PubMed Scopus (490) Google Scholar]. Some attempts have been made to show that near-real-time hospital outbreak reconstruction is feasible retrospectively [51Eyre D.W. et al.A pilot study of rapid benchtop sequencing of Staphylococcus aureus and Clostridium difficile for outbreak detection and surveillance.BMJ Open. 2012; 2: 9Crossref Scopus (200) Google Scholar, 52Harris S.R. et al.Whole-genome sequencing for analysis of an outbreak of methicillin-resistant Staphylococcus aureus: a descriptive study.Lancet Infect. Dis. 2013; 13: 130-136Abstract Full Text Full Text PDF PubMed Scopus (402) Google Scholar] or have performed analyses for ongoing outbreaks in close to real-time [53McGann P. et al.Real time application of whole genome sequencing for outbreak investigation – what is an achievable turnaround time?.Diagn. Microbiol. Infect. Dis. 2016; 5: 277-282Abstract Full Text Full Text PDF Scopus (24) Google Scholar, 54Kwong J.C. et al.Translating genomics into practice for real-time surveillance and response to carbapenemase-producing Enterobacteriaceae: evidence from a complex multi-institutional KPC outbreak.PeerJ. 2018; 6: 32Crossref Scopus (37) Google Scholar], but these studies are still in a minority and remain largely within the academic literature. Some of this time-lag probably relates to the difficulty of transmission-chain reconstruction at actionable time-scales. This can be relatively straightforward for viruses with high mutation rates, small genomes, and fast and constant transmission times, such as Ebola [55Quick J. et al.Real-time, portable genome sequencing for Ebola surveillance.Nature. 2016; 530: 228-232Crossref PubMed Scopus (820) Google Scholar] and Zika virus [56Quick J. et al.Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples.Nat. Protocol. 2017; 12: 1261-1276Crossref PubMed Scopus (473) Google Scholar], but conversely, reconstructing outbreaks for bacteria and fungi poses a series of challenges. Available tools tend to be sophisticated and complex to implement, and the sequence data needs extremely careful quality control and curation. Unfortunately, in some cases insufficient genetic variation will have accumulated over the course of an outbreak, and a transmission chain simply cannot be inferred without this signal [57Rieux A. Balloux F. Inferences from tip-calibrated phylogenies: a review and a practical guide.Mol. Ecol. 2016; 25: 1911-1924Crossref PubMed Scopus (73) Google Scholar, 58Campbell F. et al.When are pathogen genome sequences informative of transmission events?.PLoS Pathog. 2018; 14e1006885Crossref PubMed Scopus (46) Google Scholar]. Furthermore, extensive within-host genetic diversity (typical in chronic infections) can render the inference of transmission chains intractable [59Worby C.J. et al.Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data.PLoS Comput. Biol. 2014; 10: 10Crossref Scopus (109) Google Scholar]. These complexities mean that a ‘one-size fits all’ bioinformatics approach to outbreak analyses simply does not exist. One of the key promises of WGS is in molecular surveillance and real-time tracking of infectious disease. This relies on transparent and standardized data sharing of the millions of genomes sequenced each year, together with accompanying metadata on isolation host, date of sampling, and geographic location. With enough data, surveillance initiatives have the potential to identify the likely geographic origin of emerging pathogens and AMR genes, group seemingly unrelated cases into outbreaks, and clearly identify when sequences are divergent from other circulating strains. In a hospital setting, surveillance can help to detect transmission within the hospital and inflow from the community, optimize antimicrobial stewardship, and inform treatment decisions; at national and global scales, it can highlight worldwide emerging trends for which collated evidence can direct both retrospective but also anticipatory policy decisions. Amongst the most successful global surveillance initiatives and analytical frameworks are those relating specifically to the spread of viruses. Influenza surveillance is arguably the most developed, with large sequencing repositories such as the GISAID database (gisaid.org) and online data exploration and phylodynamics available through web tools such as NextFlu [60Neher R.A. Bedford T. nextflu: real-time tracking of seasonal influenza virus evolution in humans.Bioinformatics. 2015; 31: 3546-3548Crossref PubMed Scopus (100) Google Scholar] and NextStrain" @default.
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• W2892078430 title "From Theory to Practice: Translating Whole-Genome Sequencing (WGS) into the Clinic" @default.
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• W2892078430 doi "https://doi.org/10.1016/j.tim.2018.08.004" @default.
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• W2892078430 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30193960" @default.
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