Clinical and biological insights from viral genome sequencing


Whole-genome sequencing (WGS) of pathogens is becoming increasingly important not only for basic research but also for clinical science and practice. In virology, WGS is important for the development of novel treatments and vaccines, and for increasing the power of molecular epidemiology and evolutionary genomics. In this Opinion article, we suggest that WGS of viruses in a clinical setting will become increasingly important for patient care. We give an overview of different WGS methods that are used in virology and summarize their advantages and disadvantages. Although there are only partially addressed technical, financial and ethical issues in regard to the clinical application of viral WGS, this technique provides important insights into virus transmission, evolution and pathogenesis.


Since the publication of the first shotgun-sequenced genome (cauliflower mosaic virus1), the draft human genome2 and the first bacterial genomes (Haemophilus influenzae3 and Mycoplasma genitalium4), and enabled by the rapidly decreasing cost of high-throughput sequencing5, genomics has changed our understanding of human and pathogen biology. Several large projects that aim to systematically analyse microbial genomes have recently been completed or are ongoing (for example, sequencing thousands of microbiomes6 and fungal genomes6,7); these projects are shaping our knowledge of the genetic variation that is present in pathogen populations, the genetic changes that underlie disease and the diversity of microorganisms with which we share our environment.

The methods and data from whole-genome sequencing (WGS), which have been developed through basic scientific research, are increasingly being applied to clinical medicine, involving both humans8 and pathogens. For example, WGS has been used to identify new routes of transmission of Mycobacterium abscessus9 in healthcare facilities (nosocomial transmission) and to understand Neisseria meningitidis epidemics in Africa10, whereas the sequencing of partial genomes has been used to detect drug resistance in RNA viruses, such as influenza virus11, and DNA viruses, such as human cytomegalovirus (HCMV)12. Viral genome sequencing is becoming ever more important, especially in clinical research and epidemiology. WGS of pathogens has the advantage of detecting all known drug-resistant variants in a single test, whereas deep sequencing (that is, sequencing at high coverage) can identify low levels of drug-resistant variants to enable intervention before resistance becomes clinically apparent13,14. Whole genomes also provide good data with which to identify linked infections for public health and infection control purposes15,16. However, progress in using viral WGS for clinical practice has been slow. By contrast, WGS of bacteria is now well accepted, particularly for tracking outbreaks and for the management of nosocomial transmission of antimicrobial-resistant bacteria17,18.

In this Opinion article, we will address the challenges and opportunities for making WGS, using modern next-generation sequencing (NGS) methods, standard practice in clinical virology. We will discuss the strengths, weaknesses and technical challenges of different viral WGS methods (Table 1), and the importance of deeply sequencing some viral pathogens. We will also explore two areas in which viral WGS has recently proven its clinical utility: metagenomic sequencing to identify viruses that cause encephalitis (Box 1); and the role of WGS in molecular epidemiology and public health management of the Pan-American Zika virus outbreak (Box 2). Finally, we will briefly consider the ethical and data analysis challenges that clinical viral WGS presents.

Table 1 Advantages and disadvantages of different viral sequencing methods

Why sequence viruses in the clinic?

For small viruses, such as HIV, influenza virus, hepatitis B virus (HBV) and hepatitis C virus (HCV), the sequencing of partial genomes has been widely used for research, but it also has important clinical applications. One of the main applications and reasons for sequencing viruses is the detection of drug resistance. For example, the management of highly active antiretroviral therapy (HAART) for HIV relies on viral sequencing for the detection of drug-resistant variants. HAART has substantially improved the survival of patients who have HIV, but successful therapy requires long-term suppression of viral replication with antiretroviral drugs, which may be prevented by impaired host immunity, suboptimal drug penetration in certain tissue compartments and incomplete adherence to therapy19. When viral replication continues despite treatment, the high mutation rate of HIV enables resistant variants to develop. It has become standard practice in many parts of the world to sequence the HIV pol gene, which encodes the main viral enzymes, to detect variants that confer resistance to inhibitors of reverse transcriptase, integrase or protease20, particularly when patients are first diagnosed and when viral loads indicate treatment failure. Sequencing resistant variants has enabled targeted changes in treatment, which has resulted in greater reductions in viral loads than with standard care (undetectable HIV load in 32% versus 14% of patients after six months)21,22. Thus, sequencing resistant variants to guide HIV treatment improves disease outcomes. Similar approaches have been used to identify resistant variants of HCV23, HBV24 and influenza virus25.

Why sequence whole genomes?

Limited sequencing of the small number of genes that encode targets of antiviral agents, such as HIV pol, has been the norm in clinical practice. For the detection of a limited number of antiviral-resistant variants, WGS has been too costly and labour-intensive to use compared with sequencing only the specific genes that are targeted by the drugs. However, the increasing number of resistance genes that are located across viral genomes, together with decreasing costs of sequencing and the use of sequence data for transmission studies, are driving a reappraisal of the need for WGS. For example, antiviral treatment for HCV now targets four gene products (NS3, NS4A, NS5A and NS5B), and these genes encompass more than 50% of the viral genome26. Individually sequencing each of these genes can be as expensive and time-consuming as WGS27. Partial-genome sequencing is particularly problematic for large viral genomes, in particular those of the herpesviruses HCMV12, varicella zoster virus (VZV)28, herpes simplex virus 1 (HSV-1)29 and HSV-2 (Ref. 30). These viruses have traditionally been treated with drugs that target the viral thymidine or serine/threonine protein kinases and DNA polymerase. However, the increasing number of drugs in development that interact with different proteins that are encoded by viral genes scattered across the genome, means that targeted sequencing for resistance testing is costly, involves more PCR reactions (which increases the chance of failure), requires more starting material, is more labour intensive and generally less tractable for diagnostic use31. Sequencing the whole genome simultaneously captures all resistant variants and removes the need to design and optimize PCR assays for the detection of resistance to new drugs. A good example of this is HCMV, for which WGS can simultaneously capture the genes that encode targets of licensed therapies, such as UL27 (unknown function), UL54 (DNA polymerase) and UL97 (serine/threonine protein kinase), and of newer drugs, such as letermovir, which targets UL56 (terminase complex). This enables comprehensive antiviral-resistance testing in a single test12. In addition, WGS can provide information on antigenic epitopes, virus evolution in a patient over time12, and evidence of recombination between HCMV strains32. WGS can also detect putative novel drug-resistant variants and predict changes to epitopes, although phenotypic testing of variants is required to confirm clinical resistance33 and to map epitope changes34.

As pre-existing resistance to antiviral drugs increases (for example, HCV that is resistant to protease inhibitors35 and HBV that is resistant to nucleoside analogue reverse-transcriptase inhibitors36), WGS will provide the comprehensive resistance data that are required for selecting appropriate treatment. The complete knowledge of all resistant variants can also support novel decisions in clinical management; for example, the identification of extensive genome-wide HCMV drug resistance in a patient supported the decision to treat the individual with autologous cytomegalovirus-specific T cells instead of antiviral drugs37.

WGS may also better identify transmission events and outbreaks, which is not always possible with sequences of subgenomic fragments. For example, WGS of respiratory syncytial virus (RSV) identified variation outside of the gene that is traditionally used for genotyping, and such information could be used to track outbreaks in households when the genetic variability in single genes is too low for transmission studies38. The numerous phylogenetically informative variant sites that can be obtained from full-length or near full-length genomes removes the need for high-quality sequences, which enabled the robust linking of cases of Ebola virus infection and public health interventions in real time during the 2015 epidemic39. Another example of WGS supporting public health efforts is with the recent outbreak of Zika virus (Box 2).

The routine use of pathogen WGS for diagnostic purposes40 is likely to have wider clinical and research benefits. For example, Zika virus sequences that were generated for epidemiological purposes inform public health decisions41. In addition, HIV genomes that were sequenced to identify antiviral-resistant variants have also been used to study virus evolution42 and viral genetic association with disease, including genotype–phenotype association studies and genome-to-genome association studies, which look for associations between viral genetic variants, host genetic variants and outcomes of infection, such as viral load set point in HIV infection43,44.

Why do we need deep sequencing?

Modern methods, which use massively parallel sequencing, enable better examination of diversity and the analysis of virus populations that contain nucleotide variants or haplotypes at low frequencies (less than 50% of the consensus sequence). Minority variant analysis is particularly powerful for RNA viruses, reverse-transcribing DNA viruses and retroviruses, because they typically show high diversity, even in a single host. HIV is the classic example; the reverse transcriptase of HIV is error-prone and introduces mutations at an extremely high rate (4.1 ± 1.7 × 10−3 per base per cell)45. Many closely related, but subtly different, viral variants exist in a single patient. These variants are sometimes described as a quasispecies or a cloud of intra-host virus diversity. The presence of a mixed population of viruses introduces problems for the determination of the true consensus 'majority' sequence, but these minority (non-consensus) variants may also change the clinical phenotype of the virus, and can be used to predict changes in genotype, tropism or drug resistance. For example, a minor variant that confers drug resistance in HIV that is present in only 2.1% of sequencing reads in a patient at baseline can rapidly become the majority (consensus) variant under the selective pressure of drug treatment46. Similar changes in the frequency of resistance-associated alleles during treatment have been observed for HBV47, HCV48, HCMV12 and influenza virus49.

Deep sequencing of viruses is not only required to detect drug resistance, it is also key for genotypically predicting the receptor tropism of HIV, which has treatment implications. HIV can be grouped by its use of cellular co-receptor into R5 (uses CC-chemokine receptor 5 (CCR5)), X4 (uses CXC chemokine receptor 4 (CXCR4)) or R5X4 (dual tropism). Maraviroc is a CCR5 antagonist that blocks infection of R5-tropic HIV, but not of X4-tropic and R5X4-tropic HIV. Just a 2% frequency of X4 or R5X4 genotypes is predictive of maraviroc treatment failure50. Sub-consensus frequencies of X4-tropic or R5X4-tropic HIV are also important for the success51 and failure52 of bone marrow transplants from CCR5-deficient (CCR5-Δ32) donors, and this information may influence the decision to stop antiviral therapy in these patients51.

Minority variants and the identification of haplotypes can also be used to detect mixed infections. Infections with different HCMV genotypes or super-infections53 are associated with poor clinical outcomes, and the detection of such mixed infections by WGS might justify more aggressive treatment.

Sanger sequencing of a virus population can detect minority variants at frequencies between 10% and 40%54, whereas NGS can sequence those same PCR amplicons to a much greater depth55, and, consequently, capture more of the variability that is present. Sensitivity and specificity are specific for the analysed virus and the sequencing method. Many studies of drug resistance in HIV that use deep-sequencing of PCR amplicons require minority variants to be present at >1% to decrease the possibility of false-positive results56,57. This may miss drug-resistance mutations at frequencies of 0.1–1% and lead to poor treatment outcome57. Although a 1–2% frequency threshold (or lower) may be clinically relevant for the detection of drug resistance in HIV, it is less clear whether the same degree of sensitivity is required for monitoring vaccine escape in HBV or drug resistance in herpesviruses (discussed below). Large cohorts of patients need to be tested before, during and after treatment46,50 to establish thresholds for minority drug-resistant variants12 and vaccine-escape variants that are clinically relevant for each virus.

Direct deep sequencing of clinical material, either by shotgun methods or RNA-seq methods (so called metagenomic methods), also enables the unbiased detection and diagnosis of pathogens, and provides an alternative to culture, electron microscopy and quantitative PCR (qPCR; see below).

Practical considerations

Sequencing viral nucleic acids, whether from cultures or directly from clinical specimens, is complicated by the presence of contaminating host DNA58. By contrast, most bacterial sequencing is currently carried out on clinical isolates that are cultured; thus, sample preparation is comparatively straightforward (Table 2 and reviewed in Ref. 59). Currently, genome sequencing of viruses can be achieved by ultra-deep sequencing or through the enrichment for viral nucleic acids before sequencing, either directly or by concentrating virus particles. All of these approaches have their own costs and complexities.

Table 2 Limitations of viral sequencing compared with bacterial sequencing

Three main methods are currently used for viral genome sequencing: metagenomic sequencing, PCR amplicon sequencing and target enrichment sequencing (Fig. 1).

Figure 1: Methods for sequencing viral genomes from clinical specimens.

All specimens originally comprise a mix of host (in blue) and pathogen (in red) DNA sequences. For pathogens that have RNA genomes, RNA in the sample is converted into complementary DNA (cDNA) before PCR and library preparation. Direct metagenomic sequencing provides an accurate representation of the sequences in the sample, although at high sequencing and data analysis and storage costs. PCR amplicon sequencing uses many discrete PCR reactions to enrich the viral genome, which increases the workload for large genomes substantially but decreases the costs. Target enrichment sequencing uses virus-specific nucleotide probes that are bound to a solid phase, such as beads, to enrich the viral genome in a single reaction, which reduces workload but increases the cost of library preparation compared with PCR.

PowerPoint slide

Metagenomics. Metagenomic approaches have been used extensively for pathogen discovery and for the characterization of microbial diversity in environmental and clinical samples60,61. Total DNA and/or RNA, including from the host, bacteria, viruses, fungi and other pathogens, are extracted from a sample, and a library is prepared and sequenced by shotgun sequencing or RNA sequencing (RNA-seq). Box 1 explores the diagnostic applications for metagenomics and RNA-seq; for example, in encephalitis of unknown aetiology62,63,64, for which conventional methods such as PCR are often not diagnostic, metagenomics and RNA-seq have detected viral infections65,66,67 and other causes68 of encephalitis. In addition, these methods have been used to sequence the whole genome of some viruses, including Epstein–Barr virus (EBV)69 and HCV27. However, in clinical specimens, the presence of contaminating nucleic acids from the host and commensal microorganisms58 (Table 2) decreases sensitivity. The proportion of reads that match the target virus genome from metagenomic WGS is often low; for example, 0.008% for EBV in the blood of a healthy adult70, 0.0003% for Lassa virus in clinical samples71 and 0.3% for Zika virus in a sample that was enriched for virus particles through filtration and centrifugation72. The read depth is often inadequate to detect resistance27 and the cost is high. Thus, metagenomic sequencing has typically only been carried out on a small number of samples for research purposes72,73. The concentration of virus particles (see the Zika virus example above72), depletion of host material and/or sequencing to high read depth can increase the amount of virus sequence, but all of these methods add to the cost. The concentration of virus particles from clinical specimens by antibody-mediated pull-down (for example, virus discovery based on complementary DNA (cDNA)–amplified fragment length polymorphism (AFLP), abbreviated to VIDISCA), filtration, ultracentrifugation and the depletion of free nucleic acids, which mostly come from the host, have all been tried74,75,76,77; however, these methods may also decrease the total amount of viral nucleic acids so that it is insufficient for preparing a sequencing library. Non-specific amplification methods, such as multiple displacement amplification (MDA), which make use of random primers and Φ29 polymerases, can increase the DNA yield. However, these approaches are time consuming, costly, and may increase the risk of biases, errors and contamination, without necessarily improving sensitivity78,79. Moreover, the proportion of host reads often remains high80.

When metagenomic methods are used for pathogen discovery or diagnosis, it is crucial to use appropriate bioinformatic tools and databases that can evaluate whether detected pathogen sequences are likely to be the cause of infection, incidental findings or contaminants. Bioinformatic analyses of large metagenomic datasets require high-performance computational resources.

The fact that metagenomics requires no prior knowledge of the viral genome, can be considered an advantage27 as it enables novel viruses to be sequenced without the need for primer or probe design and synthesis. This is particularly relevant for rapid responses to emerging threats, such as Zika virus81. For virus-associated cancers, metagenomics can inform clinical care, provide information on cancer evolution and generate high-coverage data of integrated virus genomes69. However, incidental findings, both in host and microbial sequences, may also present ethical and even diagnostic dilemmas for clinical metagenomics82 (see below). A recent example involved a cluster of cases of acute flaccid myelitis that were associated with enterovirus D68 (Ref. 83). The metagenomic data from samples taken from patients showed the presence of alternative pathogens, some of which are treatable, and was debated in formal84 and informal scientific channels (see Omicsomics blogspot article). Regulation and reporting frameworks will be important to resolve future issues of this kind.

PCR amplicon enrichment. An alternative to metagenomic approaches is to enrich the specific viral genome before sequencing. PCR amplification of viral genetic material using primers that are complementary to a known nucleotide sequence has been the most common approach for enriching small viral genomes, such as HIV and influenza virus. Recent examples of PCR amplicon enrichment followed by WGS include phylogenetic analysis of a measles virus outbreak at the 2010 Winter Olympics85 and tracking the recent Ebola virus39 and Zika virus (Box 2) epidemics. PCR amplicon WGS of norovirus, which has a genome size of 7.5 kb, has been used to understand virus transmission in community86 and hospital87 settings, which revealed both independent introductions of the pathogen to the hospital and nosocomial transmission despite measures to control infection87. Other PCR-based deep-sequencing studies have generated several whole genomes of influenza virus88 (13.5 kb), dengue virus89 (11 kb) and HCV90 (9.6 kb). This was feasible because these viruses all have relatively small genomes that require only a few PCR amplicons to assemble whole-genome sequences. However, the heterogeneity of RNA viruses, such as HCV27, norovirus86, rabies virus91 and RSV38, may necessitate the use of multiple overlapping sets of primers to ensure the amplification of all genotypes. PCR amplicon sequencing is more successful for WGS from samples that have low virus concentrations than metagenomic methods27, although other methods such as target enrichment of viral sequences may work equally well in such samples, as shown for norovirus samples92.

Overlapping PCRs combined with NGS have been used to sequence the whole genomes of larger viruses, such as HCMV93, but this method has limited scalability, as many primers and a relatively large amount of starting DNA are required93. This limits the number of suitable samples that are available and also the genomes that can be studied using this method. For example, 8–19 PCR products were required to amplify the genome of Ebola virus39, and two studies of norovirus needed 14 and 22 PCR products, respectively86,87. For clinical applications this is problematic because of the high laboratory workload that is associated with numerous discrete PCR reactions, the necessity for individually normalizing concentrations of different PCR amplicons before pooling, the increasing probability of reaction failure due to primer mismatch (particularly for highly variable viruses), and the high costs of labour and consumables94. Therefore, although PCR-based sequencing of viruses as large as 250 kb is technically possible, the proportional relationship between genome size and technical complexity make PCR-based sequencing of viral genomes that are more than 20–50 kb impractical with current technologies, particularly for large multi-sample studies or routine diagnostics. Another consideration is that increasing numbers of PCR reactions require a corresponding increase in sample amount, and this is not always possible as clinical specimens are limited. Improvements in microfluidic technologies may help to overcome some of these barriers; for example, Fluidigm, RainDance and other 'droplet' sequencing technologies. Microfluidics-based PCR and the pooling of multiple amplicons have been used successfully to sequence several antimicrobial-resistance loci (for example, from the microbiome of pigs)95 and can also be used for viral genomes, potentially down to the single-genome level96.

Highly variable pathogens, particularly those that have widely divergent genetic lineages or genotypes, such as HCV97 and norovirus, cause problems for PCR amplification, such as primer amplification27,92 and primer mismatches86. Careful primer design may help to mitigate these problems, but novel variants remain problematic.

Target enrichment. Methods of target enrichment (also known as pull-down, capture or specific enrichment methods) can be used to sequence whole viral genomes directly from clinical samples without the need for prior culture or PCR98,99,100. These methods typically involve small RNA or DNA probes that are complementary to the pathogen reference sequence (or a panel of reference sequences). Unlike specific PCR amplicon-based methods, the reaction can be carried out in a single tube that contains overlapping probes that cover the whole genome. In a hybridization reaction, the probes, which are bound to a solid phase (for example, streptavidin-labelled magnetic beads), capture or 'pull down' complementary DNA sequences from the total nucleic acids that are present in a sample. Capture is followed by sequencer-specific adaptor ligation and a small number of PCR cycles to enrich for successfully ligated fragments. This has been used successfully to characterize small and large, clinically relevant viruses, such as HCV27, HSV-1 (Ref. 101), VZV100, EBV102, HCMV69, human herpesvirus 6 (HHV6)103 and HHV7 (Ref. 104). The reaction is carried out in a single well and, similarly to microfluidics-based PCR, is amenable to high-throughput automation102. The lack of a culture step means that the sequences that are obtained are more representative of original virus rather than cultured virus isolates, and there are fewer mutations than in PCR-amplified templates69,100. The success of this method depends on the available reference sequences for the virus of interest; specificity increases when probes are designed against a larger panel of reference sequences, as this leads to better capture of the diversity in and between samples. Target enrichment is possible despite small mismatches between template and probe; however, whereas PCR amplification requires only knowledge of flanking regions of a target region, target enrichment requires knowledge of the internal sequence to design probes. However, if one probe fails, internal and overlapping regions may still be captured by other probes69,100. Target enrichment is not suitable for the characterization of novel viruses that have low homology to known viruses for which metagenomics, and, in some cases, PCR using degenerate primers, which are a mix of similar but variable primers, may be more appropriate.

As with all methods, the technique is constrained by the starting virus concentration. Although viruses could be sequenced from samples with viral loads as low as 2,000 International Units (IU) ml−1 (for HCV) or 2,500 IU ml−1 (for HCMV), there was a reduced depth of coverage in sequencing data at lower virus concentrations27,69. With metagenomics, the proportion of sequencing data that map to the pathogen from unenriched clinical samples is small. Target enrichment can increase the percentage of on-target viral reads from 0.01% to 80% or more69. The improvement in quality and depth of sequence that results enables more samples to be sequenced per run than unenriched metagenomic libraries for equivalent on-target sequencing performance. This improvement also decreases the price of sequencing, although the cost of library preparation is increased. There are alternative approaches for the enrichment of viral reads, including pulsed-field gel electrophoresis (PFGE)105, which separates large viral genomes from smaller fragments of host DNA.

Enrichment techniques that use degenerate RNA or DNA probes to capture hundreds of viral species have also been developed; for example, virome capture sequencing (VirCapSeq)106. This method is designed for the detection of both known and novel viruses, although its performance remains to be evaluated.

Comparison of methods. To date, there has been very little direct comparison between the three methods for viral genome sequencing in clinical practice, with only one paper evaluating relative performance for the sequencing of HCV27. Results from this study, in which three different enrichment protocols, two metagenomic methods and one overlapping PCR method were evaluated, showed that metagenomic methods were the least sensitive, yielded the lowest genome coverage for comparable sequencing effort and were more prone to result in incomplete genome assemblies. The PCR method required repeated amplification and was the most likely to miss mixed infections, but when reactions were successful it resulted in the most consistent read depth, whereas read depth was proportional to virus copy number in metagenomics and target enrichment. PCR generated more incomplete sequences for some HCV genotypes (particularly genotype 2) than metagenomics and target enrichment. Target enrichment was the most consistent method to result in full genomes and identical consensus sequences. The ease of library preparation for metagenomic and target enrichment sequencing of HCV was considered a major advantage for clinical applications, but PCR may still be appropriate for samples that have very low viral loads.

Similar results were achieved in a study that compared PCR amplicon sequencing and target enrichment sequencing of norovirus92. With target enrichment sequencing, the whole viral genome could be sequenced in all 164 samples, whereas PCR-based capsid sequencing was only possible in 158 out of the 164 samples, owing to low virus titres and PCR primer mismatches, which suggests that target enrichment is more sensitive than PCR for sequencing norovirus and better accommodates between-strain sequence heterogeneity92. Target enrichment has also been used for samples that have low viral loads and incomplete genome coverage in metagenomic sequencing107. Both metagenomic and target enrichment sequencing can be used for pathogen genomes of all sizes, whereas PCR-based methods are less suitable for large viral genomes or for non-viral (that is, bacterial, fungal and parasite) genomes.

Direct comparisons of different methods27,92 will be important for determining when each method should be used, based on sensitivity and specificity, as well as factors such as cost, scalability and turn-around time, which are particularly important in clinical applications (Table 1).

Analysis and interpretation challenges

Beyond the technical challenges of viral WGS that are mentioned above, there are several other roadblocks that may slow the advance of WGS in the clinic. They may be considered in three groups: ethical issues, including incidental host and microbiological findings; regulatory issues, such as the establishment of standards, good laboratory practice, and sensitivity and specificity thresholds for sequencing; and analytical issues regarding data interpretation and the numerous choices of analysis options.

Ethical issues and incidental findings. In many clinical tests (for example, magnetic resonance imaging (MRI) scans and sequencing of the genomes of patients), there is a risk of detecting a disease association that is not part of the original investigation but might be of clinical importance for the individual or their family. These incidental findings remain a topic of intense medical ethical debate108. The risk of incidental findings in pathogen sequencing (for example, the discovery of HIV infection during metagenomic sequencing for other pathogens) is not novel and the solution in clinical virology laboratories that use multiplex PCRs is to suppress results that have not been requested (J.B., unpublished observations). In the United Kingdom, the clinical virologist who interprets the test results is part of the team that manages the patient, and, as such, may decide to discuss an unexpected result with the physician in charge. Incidental host genetic findings (for example, the detection of variants that predispose to cancer development) in a pathogen metagenomic analysis are not reported to the individual in the United Kingdom, because this is only permissible with the consent of the patient. In regard to both host and virus incidental findings, target enrichment and PCR have the advantage of only providing results about the pathogen of interest. The ethical and privacy concerns that are associated with the presence of host genetic data in publically available metagenomic datasets have been well reviewed82 and represent a separate challenge.

Regulatory challenges. Regulation, as well as helping to address some of the ethical concerns, is also important in standardizing WGS of viruses. The framework that is required to make viral WGS sufficiently robust and reproducible in clinical practice will come from several areas.

The framework of laboratory accreditation and benchmark testing that are already available (for example, Clinical Laboratory Improvement Amendments of 1988 (CLIA) regulations in the USA, or accreditation according to medical laboratory quality and competence standardization criteria for ISO 15189) will support the development of viral WGS standards, provided that there is sufficient need and pressure to implement clinical viral WGS.

Lessons learned from the use of PCR in diagnostics may be useful here, starting with ensuring good clinical laboratory and molecular practices109,110. This will mean including negative samples in every sequencing run to assess contamination thresholds, spiking samples with a known virus to provide a sensitivity threshold, and including positive controls and controls for batch-to-batch variation111, all of which will increase sequencing costs and are likely to deter the adoption of pathogen genome sequencing by laboratories that are sequencing only small batches of samples. The centralization of virus WGS can help to ensure the maintenance of adequate standards, the processing of large batches of samples and reducing costs.

The issues of sensitivity and contamination are especially important in WGS, because of the risk of both false-negative and false-positive detection of pathogens. Highly sensitive sequencing (whether metagenomic, PCR-based or target enrichment-based) may detect low-level contaminating viral nucleic acids112,113. For example, murine leukaemia virus114,115 and parvovirus-like sequences116,117 are just two of many contaminants that can come from common laboratory reagents, such as nucleic acid extraction columns118. As with other highly sensitive technologies, robust laboratory practices and protocols are required to minimize contamination. It is also important to remember that the detection of viral nucleic acid does not necessarily identify the cause of illness, and it is good practice when using NGS methods for the diagnosis of viral infections to confirm the findings with alternative independent methods that do not rely on testing for nucleic acids. For example, in cases of encephalitis of unknown origin, positive NGS findings can be confirmed through immunohistochemical analysis of the affected tissue65,119, or identification of the virus by electron microscopy or tissue culture82.

The standardization of methods, including bioinformatics, will be key to the success of NGS and WGS in clinical virology. Software packages that use a graphical user interface (GUI) are preferable to tools that require command-line expertise. Strict version control of software and analysis pipelines is required to ensure that results are reproducible, to make best practices easily shareable, and to enable the accreditation of analysis software. However, best-practice analysis methods are continually evolving and the premature standardization of best practices in an overly rigid manner may inhibit innovation. Commercialization and regulation may help, as they provide financial and regulatory incentives to ensure that analysis tools and technologies meet clinical needs. Finally, the development of well-curated databases that show which variants are truly indicative of drug resistance will be crucial for accurate clinical interpretation. Such databases have already been created for HIV120, HBV121,122 and HCV123, but without recognition of their value by funding agencies and corresponding centralized funding to ensure their continued maintenance and upkeep, these databases and associated tools may become swiftly outdated or unusable.

Financial barriers to the clinical use of viral WGS. Although there are good reasons for sequencing whole genomes and, in general, for using NGS, if diagnostic or hospital-based laboratories are to be persuaded to transition away from sequencing subgenomic fragments, they need to see the benefit of the additional information for patient care and the practical feasibility of WGS. This includes WGS workflows that are as scalable and automatable as subgenomic fragment sequencing, a suitable regulatory framework and a price for sequencing whole genomes that is competitive with sequencing fragments.

Currently, the cost of sequencing viral genomes, despite their small size, remains higher than the cost of sequencing subgenomic resistance genes. The cost difference between sequencing a target region and the whole virus genome is largely governed by the size of the genome versus the size and number of target loci. In addition, whole-genome information may provide important additional knowledge, as discussed above.

What does the future hold?

Current NGS technologies that are based around Illumina, 454, Ion Torrent or Sanger methodologies all generate short-read data, which presents challenges for haplotype phasing; that is, determining whether genetic variants (whether inter-host or intra-host) occur on the same genetic background (single viral genome or clonal) or on related, highly similar but different genetic backgrounds in the same population (sometimes called a viral swarm or cloud). Furthermore, repetitive regions and recombination are more difficult to resolve using short reads owing to problems such as mapping ambiguities. The clinical implications of understanding whether, for example, multidrug-resistant variants occur together on a single viral genome or are distributed between a mixed population of viruses, each with different drug-resistance profiles, are currently unclear.

Although there are computational tools124 to help resolve these issues, new technologies can generate longer reads. Newer, single-molecule sequencers, such as PacBio (Pacific Biosciences) and MinION (Oxford Nanopore), are capable of extremely long-read sequencing, and whole viral genomes (for example, viruses that have genomes less than 20 kb in size, such as Ebola virus, norovirus and influenza A virus) could theoretically be obtained from single reads. MinION also has the advantage of being very fast, taking as little as four hours to go from sample receipt to reporting of analysed data125. So far, viral read lengths achieved by MinION sequencing have been relatively modest; examples of mean read lengths include 751 bp for modified vaccinia Ankara virus, 758 bp for cowpox virus126, 455 bp (range 126–1477 bp) for chikungunya virus, 358 bp (range 220–672 bp) for Ebola virus, 1,576 bp127 (M.A.B., unpublished observations: 6,895bp) for HCMV and 572bp (range 318–792 bp) for HCV128. Results from the better-established PacBio technology are more promising, including a recent report of a mean read length of 12,777 bp for pseudorabies virus129, which has a double-stranded DNA genome that is around 142 kb in length. 9.2 kb reads have been achieved using PacBio for HCV, although 9.2 kb of the 9.6 kb genome had been pre-amplified by PCR130.

A drawback of both NGS and single-molecule sequencing is the need for high coverage to minimize the effect of sequencing errors. This is particularly problematic for studies of drug resistance, as drug resistance most frequently results from single-nucleotide mutations or small deletions (1–3 bases), especially in lower-fidelity RNA viruses131. Achieving the high coverage that is necessary to ensure accurate variant typing is challenging when there is a lot of host DNA compared with viral sequences, and when the error profile of a technology makes point mutations particularly hard to detect125. At the time of writing, MinION sequencing (R9 pore chemistry) has raw high quality (so called '2D reads') read error rates of around 5% (J. Quick, personal communication), which compares unfavourably with the error rates of other technologies (Illumina (<0.1%), Ion Torrent (1%), but not PacBio (13% single pass))132, although accuracy can been improved using circular consensus read sequencing133,134.

However, combining these long-read technologies with target enrichment provides a potential way forward127,135, as ambiguities can be resolved if sufficient depth of sequence is achieved for the target pathogen, and error rates for all methodologies may be decreased by further technological and analytical improvements. Depleting the nucleic acids of the host is an alternative solution, as a higher proportion of virus reads would be recovered from each sequencing run. Although there are already solutions in place to achieve this for bacterial sequencing (for example, depletion of human ribosomal RNA or mitochondria, and selective depletion of DNA with a certain methylation pattern), no similar methods exist, so far, for viral sequencing.


Viral WGS is of increasing clinical importance for diagnosis, disease management, molecular epidemiology and infection control. There are several methods that are available to achieve WGS of viruses from clinical samples; amplicon sequencing, target enrichment or metagenomics. Currently, the choice of method is specific to both the virus and the clinical question. Metagenomic sequencing is most appropriate for diagnostic sequencing of unknown or poorly characterized viruses, PCR amplicon sequencing works well for short viral genomes and low diversity in primer binding sites, and target enrichment works for all pathogen sizes but is particularly advantageous for large viruses and for viruses that have diverse but well-characterized genomes. Two obvious areas of innovation currently exist: methods that can effectively deplete host DNA without affecting viral DNA, and the further development of long-read technologies to achieve the flexibility and competitive pricing of short-read technologies. New technologies are required to unite the strengths of these different methods and enable healthcare providers to invest in a single technology that is suitable for all viral WGS applications.


  1. 1

    Gardner, R. C. et al. The complete nucleotide sequence of an infectious clone of cauliflower mosaic virus by M13mp7 shotgun sequencing. Nucleic Acids Res. 9, 2871–2888 (1981).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    CAS  Google Scholar 

  3. 3

    Fleischmann, R. D. et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269, 496–512 (1995).

    CAS  Google Scholar 

  4. 4

    Fraser, C. M. et al. The minimal gene complement of Mycoplasma genitalium. Science 270, 397–403 (1995).

    CAS  Google Scholar 

  5. 5

    Hayden, E. C. Technology: the $1,000 genome. Nature 507, 294–295 (2014).

    Google Scholar 

  6. 6

    Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Grigoriev, I. V. et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 42, D699–D704 (2014).

    CAS  Google Scholar 

  8. 8

    Worthey, E. A. et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet. Med. 13, 255–262 (2011).

    Google Scholar 

  9. 9

    Bryant, J. M. et al. Whole-genome sequencing to identify transmission of Mycobacterium abscessus between patients with cystic fibrosis: a retrospective cohort study. Lancet 381, 1551–1560 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Lamelas, A. et al. Emergence of a new epidemic Neisseria meningitidis serogroup A clone in the African meningitis belt: high-resolution picture of genomic changes that mediate immune evasion. mBio 5, e01974-14 (2014).

    PubMed  PubMed Central  Google Scholar 

  11. 11

    Zaraket, H. et al. Genetic makeup of amantadine-resistant and oseltamivir-resistant human influenza A/H1N1 viruses. J. Clin. Microbiol. 48, 1085–1092 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Houldcroft, C. J. et al. Detection of low frequency multi-drug resistance and novel putative maribavir resistance in immunocompromised pediatric patients with cytomegalovirus. Front. Microbiol. 7, 1317 (2016).

    PubMed  PubMed Central  Google Scholar 

  13. 13

    Witney, A. A. et al. Clinical application of whole-genome sequencing to inform treatment for multidrug-resistant tuberculosis cases. J. Clin. Microbiol. 53, 1473–1483 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. 14

    Simen, B. B. et al. Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes. J. Infect. Dis. 199, 693–701 (2009).

    Google Scholar 

  15. 15

    Smith, G. J. et al. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 459, 1122–1125 (2009).

    CAS  Google Scholar 

  16. 16

    Gire, S. K. et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Koser, C. U. et al. Routine use of microbial whole-genome sequencing in diagnostic and public health microbiology. PLoS Pathog. 8, e1002824 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Cartwright, E. J., Koser, C. U. & Peacock, S. J. Microbial sequences benefit health now. Nature 471, 578 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Paredes, R. & Clotet, B. Clinical management of HIV-1 resistance. Antiviral Res. 85, 245–265 (2010).

    CAS  Google Scholar 

  20. 20

    Van Laethem, K., Theys, K. & Vandamme, A. M. HIV-1 genotypic drug resistance testing: digging deep, reaching wide? Curr. Opin. Virol. 14, 16–23 (2015).

    CAS  Google Scholar 

  21. 21

    Durant, J. et al. Drug-resistance genotyping in HIV-1 therapy: the VIRADAPT randomised controlled trial. Lancet 353, 2195–2199 (1999).

    CAS  Google Scholar 

  22. 22

    Clevenbergh, P. et al. Persisting long-term benefit of genotype-guided treatment for HIV-infected patients failing HAART. The Viradapt Study: week 48 follow-up. Antivir. Ther. 5, 65–70 (2000).

    CAS  Google Scholar 

  23. 23

    Khudyakov, Y. Molecular surveillance of hepatitis C. Antivir. Ther. 17, 1465–1470 (2012).

    CAS  Google Scholar 

  24. 24

    Kim, J. H., Park, Y. K., Park, E. S. & Kim, K. H. Molecular diagnosis and treatment of drug-resistant hepatitis B virus. World J. Gastroenterol. 20, 5708–5720 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. 25

    McGinnis, J., Laplante, J., Shudt, M. & George, K. S. Next generation sequencing for whole genome analysis and surveillance of influenza A viruses. J. Clin. Virol. 79, 44–50 (2016).

    CAS  Google Scholar 

  26. 26

    Pawlotsky, J. M. Hepatitis C virus resistance to direct-acting antiviral drugs in interferon-free regimens. Gastroenterology 151, 70–86 (2016).

    CAS  Google Scholar 

  27. 27

    Thomson, E. et al. Comparison of next generation sequencing technologies for the comprehensive assessment of full-length hepatitis C viral genomes. J. Clin. Microbiol. 54, 2470–2484 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Brunnemann, A. K. et al. Drug resistance of clinical varicella-zoster virus strains confirmed by recombinant thymidine kinase expression and by targeted resistance mutagenesis of a cloned wild-type isolate. Antimicrob. Agents Chemother. 59, 2726–2734 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Karamitros, T. et al. De novo assembly of human herpes virus type 1 (HHV-1) genome, mining of non-canonical structures and detection of novel drug-resistance mutations using short- and long-read next generation sequencing technologies. PLoS ONE 11, e0157600 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Piret, J. & Boivin, G. Antiviral drug resistance in herpesviruses other than cytomegalovirus. Rev. Med. Virol. 24, 186–218 (2014).

    CAS  Google Scholar 

  31. 31

    Melendez, D. P. & Razonable, R. R. Letermovir and inhibitors of the terminase complex: a promising new class of investigational antiviral drugs against human cytomegalovirus. Infect. Drug Resist. 8, 269–277 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Lassalle, F. et al. Islands of linkage in an ocean of pervasive recombination reveals two-speed evolution of human cytomegalovirus genomes. Virus Evol. 2, vew017 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Lanier, E. R. et al. Analysis of mutations in the gene encoding cytomegalovirus DNA polymerase in a phase 2 clinical trial of brincidofovir prophylaxis. J. Infect. Dis. 214, 32–35 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Kaverin, N. V. et al. Epitope mapping of the hemagglutinin molecule of a highly pathogenic H5N1 influenza virus by using monoclonal antibodies. J. Virol. 81, 12911–12917 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Franco, S. et al. Detection of a sexually transmitted hepatitis C virus protease inhibitor-resistance variant in a human immunodeficiency virus-infected homosexual man. Gastroenterology 147, 599–601.e1 (2014).

    Google Scholar 

  36. 36

    Fujisaki, S. et al. Outbreak of infections by hepatitis B virus genotype A and transmission of genetic drug resistance in patients coinfected with HIV-1 in Japan. J. Clin. Microbiol. 49, 1017–1024 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Pierucci, P. et al. Novel autologous T-cell therapy for drug-resistant cytomegalovirus disease after lung transplantation. J. Heart Lung Transplant. 35, 685–687 (2016).

    Google Scholar 

  38. 38

    Agoti, C. N. et al. Local evolutionary patterns of human respiratory syncytial virus derived from whole-genome sequencing. J. Virol. 89, 3444–3454 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Aanensen, D. M. et al. Whole-genome sequencing for routine pathogen surveillance in public health: a population snapshot of invasive Staphylococcus aureus in Europe. mBio 7, e00444-16 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Faria, N. R. et al. Zika virus in the Americas: early epidemiological and genetic findings. Science 352, 345–349 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Mbisa, J. L. et al. Evidence of self-sustaining drug resistant HIV-1 lineages among untreated patients in the United Kingdom. Clin. Infect. Dis. 61, 829–836 (2015).

    Google Scholar 

  43. 43

    Bartha, I. et al. A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control. eLife 2, e01123 (2013).

    PubMed  PubMed Central  Google Scholar 

  44. 44

    Power, R. A. et al. Genome-wide association study of HIV whole genome sequences validated using drug resistance. PLoS ONE 11, e0163746 (2016).

    PubMed  PubMed Central  Google Scholar 

  45. 45

    Cuevas, J. M., Geller, R., Garijo, R., Lopez-Aldeguer, J. & Sanjuan, R. Extremely high mutation rate of HIV-1 in vivo. PLoS Biol. 13, e1002251 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Vandenhende, M. A. et al. Prevalence and evolution of low frequency HIV drug resistance mutations detected by ultra deep sequencing in patients experiencing first line antiretroviral therapy failure. PLoS ONE 9, e86771 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. 47

    Zhou, B. et al. Composition and interactions of hepatitis B virus quasispecies defined the virological response during telbivudine therapy. Sci. Rep. 5, 17123 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Itakura, J. et al. Resistance-associated NS5A variants of hepatitis C virus are susceptible to interferon-based therapy. PLoS ONE 10, e0138060 (2015).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Rogers, M. B. et al. Intrahost dynamics of antiviral resistance in influenza A virus reflect complex patterns of segment linkage, reassortment, and natural selection. mBio 6, e02464-14 (2015).

    PubMed  PubMed Central  Google Scholar 

  50. 50

    Swenson, L. C., Daumer, M. & Paredes, R. Next-generation sequencing to assess HIV tropism. Curr. Opin. HIV AIDS 7, 478–485 (2012).

    CAS  Google Scholar 

  51. 51

    Hutter, G. et al. Long-term control of HIV by CCR5 delta32/delta32 stem-cell transplantation. N. Engl. J. Med. 360, 692–698 (2009).

    Google Scholar 

  52. 52

    Kordelas, L. et al. Shift of HIV tropism in stem-cell transplantation with CCR5 delta32 mutation. N. Engl. J. Med. 371, 880–882 (2014).

    Google Scholar 

  53. 53

    Coaquette, A. et al. Mixed cytomegalovirus glycoprotein B genotypes in immunocompromised patients. Clin. Infect. Dis. 39, 155–161 (2004).

    Google Scholar 

  54. 54

    Solmone, M. et al. Use of massively parallel ultradeep pyrosequencing to characterize the genetic diversity of hepatitis B virus in drug-resistant and drug-naive patients and to detect minor variants in reverse transcriptase and hepatitis B S antigen. J. Virol. 83, 1718–1726 (2009).

    CAS  Google Scholar 

  55. 55

    Chou, S. et al. Improved detection of emerging drug-resistant mutant cytomegalovirus subpopulations by deep sequencing. Antimicrob. Agents Chemother. 58, 4697–4702 (2014).

    PubMed  PubMed Central  Google Scholar 

  56. 56

    Fonager, J. et al. Identification of minority resistance mutations in the HIV-1 integrase coding region using next generation sequencing. J. Clin. Virol. 73, 95–100 (2015).

    CAS  Google Scholar 

  57. 57

    Kyeyune, F. et al. Low-frequency drug resistance in HIV-infected Ugandans on antiretroviral treatment is associated with regimen failure. Antimicrob. Agents Chemother. 60, 3380–3397 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Liu, P. et al. Direct sequencing and characterization of a clinical isolate of Epstein–Barr virus from nasopharyngeal carcinoma tissue by using next-generation sequencing technology. J. Virol. 85, 11291–11299 (2011).

    PubMed  PubMed Central  Google Scholar 

  59. 59

    Loman, N. J. & Pallen, M. J. Twenty years of bacterial genome sequencing. Nat. Rev. Microbiol. 13, 787–794 (2015).

    CAS  Google Scholar 

  60. 60

    Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74 (2004).

    CAS  Google Scholar 

  61. 61

    Mulcahy-O'Grady, H. & Workentine, M. L. The challenge and potential of metagenomics in the clinic. Front. Immunol. 7, 29 (2016).

    PubMed  PubMed Central  Google Scholar 

  62. 62

    Hoffmann, B. et al. A variegated squirrel bornavirus associated with fatal human encephalitis. N. Engl. J. Med. 373, 154–162 (2015).

    CAS  Google Scholar 

  63. 63

    Perlejewski, K. et al. Next-generation sequencing (NGS) in the identification of encephalitis-causing viruses: unexpected detection of human herpesvirus 1 while searching for RNA pathogens. J. Virol. Methods 226, 1–6 (2015).

    CAS  Google Scholar 

  64. 64

    Duncan, C. J. et al. Human IFNAR2 deficiency: lessons for antiviral immunity. Sci. Transl Med. 7, 307ra154 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. 65

    Morfopoulou, S. et al. Human coronavirus OC43 associated with fatal encephalitis. N. Engl. J. Med. 375, 497–498 (2016).

    Google Scholar 

  66. 66

    Naccache, S. N. et al. Diagnosis of neuroinvasive astrovirus infection in an immunocompromised adult with encephalitis by unbiased next-generation sequencing. Clin. Infect. Dis. 60, 919–923 (2015).

    PubMed  PubMed Central  Google Scholar 

  67. 67

    Huang, W. et al. Whole-genome sequence analysis reveals the enterovirus D68 isolates during the United States 2014 outbreak mainly belong to a novel clade. Sci. Rep. 5, 15223 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Wilson, M. R. et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N. Engl. J. Med. 370, 2408–2417 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. 69

    Depledge, D. P. et al. Specific capture and whole-genome sequencing of viruses from clinical samples. PLoS ONE 6, e27805 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Allen, U. D. et al. The genetic diversity of Epstein–Barr virus in the setting of transplantation relative to non-transplant settings: a feasibility study. Pediatr. Transplant. 20, 124–129 (2016).

    CAS  Google Scholar 

  71. 71

    Matranga, C. B. et al. Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples. Genome Biol. 15, 519 (2014).

    PubMed  PubMed Central  Google Scholar 

  72. 72

    Calvet, G. et al. Detection and sequencing of Zika virus from amniotic fluid of fetuses with microcephaly in Brazil: a case study. Lancet Infect. Dis. 16, 653–660 (2016).

    Google Scholar 

  73. 73

    Lei, H. et al. Epstein–Barr virus from Burkitt Lymphoma biopsies from Africa and South America share novel LMP-1 promoter and gene variations. Sci. Rep. 5, 16706 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Kohl, C. et al. Protocol for metagenomic virus detection in clinical specimens. Emerg. Infect. Dis. 21, 48–57 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Sauvage, V. & Eloit, M. Viral metagenomics and blood safety. Transfus. Clin. Biol. 23, 28–38 (2016).

    CAS  Google Scholar 

  76. 76

    Lecuit, M. & Eloit, M. The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening. Front. Cell. Infect. Microbiol. 4, 25 (2014).

    PubMed  PubMed Central  Google Scholar 

  77. 77

    Oude Munnink, B. B. et al. Autologous antibody capture to enrich immunogenic viruses for viral discovery. PLoS ONE 8, e78454 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Sabina, J. & Leamon, J. H. Bias in whole genome amplification: causes and considerations. Methods Mol. Biol. 1347, 15–41 (2015).

    CAS  Google Scholar 

  79. 79

    Jensen, R. H. et al. Target-dependent enrichment of virions determines the reduction of high-throughput sequencing in virus discovery. PLoS ONE 10, e0122636 (2015).

    PubMed  PubMed Central  Google Scholar 

  80. 80

    Denesvre, C., Dumarest, M., Remy, S., Gourichon, D. & Eloit, M. Chicken skin virome analyzed by high-throughput sequencing shows a composition highly different from human skin. Virus Genes 51, 209–216 (2015).

    CAS  Google Scholar 

  81. 81

    Mlakar, J. et al. Zika virus associated with microcephaly. N. Engl. J. Med. 374, 951–958 (2016).

    CAS  Google Scholar 

  82. 82

    Hall, R. J., Draper, J. L., Nielsen, F. G. & Dutilh, B. E. Beyond research: a primer for considerations on using viral metagenomics in the field and clinic. Front. Microbiol. 6, 224 (2015).

    PubMed  PubMed Central  Google Scholar 

  83. 83

    Greninger, A. L. et al. A novel outbreak enterovirus D68 strain associated with acute flaccid myelitis cases in the USA (2012–2014): a retrospective cohort study. Lancet Infect. Dis. 15, 671–682 (2015).

    PubMed  PubMed Central  Google Scholar 

  84. 84

    Breitwieser, F. P., Pardo, C. A. & Salzberg, S. L. Re-analysis of metagenomic sequences from acute flaccid myelitis patients reveals alternatives to enterovirus D68 infection. F1000Res. 4, 180 (2015).

    PubMed  PubMed Central  Google Scholar 

  85. 85

    Gardy, J. L. et al. Whole-genome sequencing of measles virus genotypes H1 and D8 during outbreaks of infection following the 2010 Olympic Winter Games reveals viral transmission routes. J. Infect. Dis. 212, 1574–1578 (2015).

    Google Scholar 

  86. 86

    Cotten, M. et al. Deep sequencing of norovirus genomes defines evolutionary patterns in an urban tropical setting. J. Virol. 88, 11056–11069 (2014).

    PubMed  PubMed Central  Google Scholar 

  87. 87

    Kundu, S. et al. Next-generation whole genome sequencing identifies the direction of norovirus transmission in linked patients. Clin. Infect. Dis. 57, 407–414 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Watson, S. J. et al. Molecular epidemiology and evolution of influenza viruses circulating within European swine between 2009 and 2013. J. Virol. 89, 9920–9931 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Parameswaran, P. et al. Genome-wide patterns of intrahuman dengue virus diversity reveal associations with viral phylogenetic clade and interhost diversity. J. Virol. 86, 8546–8558 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Newman, R. M. et al. Whole genome pyrosequencing of rare hepatitis C virus genotypes enhances subtype classification and identification of naturally occurring drug resistance variants. J. Infect. Dis. 208, 17–31 (2013).

    CAS  Google Scholar 

  91. 91

    Jakava-Viljanen, M. et al. Evolutionary trends of European bat lyssavirus type 2 including genetic characterization of Finnish strains of human and bat origin 24 years apart. Arch. Virol. 160, 1489–1498 (2015).

    Google Scholar 

  92. 92

    Brown, J. R. et al. Norovirus whole genome sequencing by SureSelect target enrichment: a robust and sensitive method. J. Clin. Microbiol. 54, 2530–2537 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93

    Renzette, N., Bhattacharjee, B., Jensen, J. D., Gibson, L. & Kowalik, T. F. Extensive genome-wide variability of human cytomegalovirus in congenitally infected infants. PLoS Pathog. 7, e1001344 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Bialasiewicz, S. et al. Detection of a divergent parainfluenza 4 virus in an adult patient with influenza like illness using next-generation sequencing. BMC Infect. Dis. 14, 275 (2014).

    PubMed  PubMed Central  Google Scholar 

  95. 95

    Johnson, T. A. et al. Clusters of antibiotic resistance genes enriched together stay together in swine agriculture. mBio 7, e02214-15 (2016).

    PubMed  PubMed Central  Google Scholar 

  96. 96

    Ocwieja, K. E. et al. Dynamic regulation of HIV-1 mRNA populations analyzed by single-molecule enrichment and long-read sequencing. Nucleic Acids Res. 40, 10345–10355 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Bonsall, D. et al. ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens. F1000Res. 4, 1062 (2015).

    PubMed  PubMed Central  Google Scholar 

  98. 98

    Wylie, T. N., Wylie, K. M., Herter, B. N. & Storch, G. A. Enhanced virome sequencing using targeted sequence capture. Genome Res. 25, 1910–1920 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Tsangaras, K. et al. Hybridization capture using short PCR products enriches small genomes by capturing flanking sequences (CapFlank). PLoS ONE 9, e109101 (2014).

    PubMed  PubMed Central  Google Scholar 

  100. 100

    Depledge, D. P. et al. Deep sequencing of viral genomes provides insight into the evolution and pathogenesis of varicella zoster virus and its vaccine in humans. Mol. Biol. Evol. 31, 397–409 (2014).

    CAS  Google Scholar 

  101. 101

    Ebert, K., Depledge, D. P., Breuer, J., Harman, L. & Elliott, G. Mode of virus rescue determines the acquisition of VHS mutations in VP22-negative herpes simplex virus 1. J. Virol. 87, 10389–10393 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Palser, A. L. et al. Genome diversity of Epstein–Barr virus from multiple tumor types and normal infection. J. Virol. 89, 5222–5237 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Tweedy, J. et al. Complete genome sequence of the human herpesvirus 6A strain AJ from Africa resembles strain GS from North America. Genome Announc. 3, e01498-14 (2015).

    PubMed  PubMed Central  Google Scholar 

  104. 104

    Donaldson, C. D., Clark, D. A., Kidd, I. M., Breuer, J. & Depledge, D. D. Genome sequence of human herpesvirus 7 strain UCL-1. Genome Announc. 1, e00830-13 (2013).

    PubMed  PubMed Central  Google Scholar 

  105. 105

    Kamperschroer, C., Gosink, M. M., Kumpf, S. W., O'Donnell, L. M. & Tartaro, K. R. The genomic sequence of lymphocryptovirus from cynomolgus macaque. Virology 488, 28–36 (2016).

    CAS  Google Scholar 

  106. 106

    Briese, T. et al. Virome capture sequencing enables sensitive viral diagnosis and comprehensive virome analysis. mBio 6, e01491-15 (2015).

    PubMed  PubMed Central  Google Scholar 

  107. 107

    Naccache, S. N. et al. Distinct Zika virus lineage in Salvador, Bahia, Brazil. Emerg. Infect. Dis. 22, 1788–1792 (2016).

    PubMed  PubMed Central  Google Scholar 

  108. 108

    Hofmann, B. Incidental findings of uncertain significance: to know or not to know — that is not the question. BMC Med. Ethics 17, 13 (2016).

    PubMed  PubMed Central  Google Scholar 

  109. 109

    Public Health England. Good laboratory practice when performing molecular amplification assays. Public Health England (2013).

  110. 110

    Viana, R. V. & Wallis, C. L. in Wide Spectra of Quality Control Ch. 3 (ed Akyar, I.) (InTech, 2011).

    Google Scholar 

  111. 111

    Blomquist, T., Crawford, E. L., Yeo, J., Zhang, X. & Willey, J. C. Control for stochastic sampling variation and qualitative sequencing error in next generation sequencing. Biomol. Detect. Quantif. 5, 30–37 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112

    Houldcroft, C. J. & Breuer, J. Tales from the crypt and coral reef: the successes and challenges of identifying new herpesviruses using metagenomics. Front. Microbiol. 6, 188 (2015).

    PubMed  PubMed Central  Google Scholar 

  113. 113

    Munro, A. C. & Houldcroft, C. Human cancers and mammalian retroviruses: should we worry about bovine leukemia virus? Future Virol. 11, 163–166 (2016).

    CAS  Google Scholar 

  114. 114

    Hue, S. et al. Disease-associated XMRV sequences are consistent with laboratory contamination. Retrovirology 7, 111 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Erlwein, O. et al. DNA extraction columns contaminated with murine sequences. PLoS ONE 6, e23484 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Rosseel, T., Pardon, B., De Clercq, K., Ozhelvaci, O. & Van Borm, S. False-positive results in metagenomic virus discovery: a strong case for follow-up diagnosis. Transbound. Emerg. Dis. 61, 293–299 (2014).

    CAS  Google Scholar 

  117. 117

    Naccache, S. N. et al. The perils of pathogen discovery: origin of a novel parvovirus-like hybrid genome traced to nucleic acid extraction spin columns. J. Virol. 87, 11966–11977 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).

    PubMed  PubMed Central  Google Scholar 

  119. 119

    Lipkin, W. I. A. Vision for investigating the microbiology of health and disease. J. Infect. Dis. 212 (Suppl. 1), S26–S30 (2015).

    Google Scholar 

  120. 120

    Shafer, R. W. Rationale and uses of a public HIV drug-resistance database. J. Infect. Dis. 194 (Suppl. 1), S51–S58 (2006).

    PubMed  PubMed Central  Google Scholar 

  121. 121

    Gnaneshan, S., Ijaz, S., Moran, J., Ramsay, M. & Green, J. HepSEQ: international public health repository for hepatitis B. Nucleic Acids Res. 35, D367–D370 (2007).

    CAS  Google Scholar 

  122. 122

    Rhee, S. Y. et al. Hepatitis B virus reverse transcriptase sequence variant database for sequence analysis and mutation discovery. Antiviral Res. 88, 269–275 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123

    Kuiken, C., Yusim, K., Boykin, L. & Richardson, R. The Los Alamos hepatitis C sequence database. Bioinformatics 21, 379–384 (2005).

    CAS  Google Scholar 

  124. 124

    Hong, L. Z. et al. BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads. Genome Biol. 15, 517 (2014).

    PubMed  PubMed Central  Google Scholar 

  125. 125

    Schmidt, K. et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J. Antimicrob. Chemother. 72, 104–114 (2017).

    CAS  Google Scholar 

  126. 126

    Kilianski, A. et al. Bacterial and viral identification and differentiation by amplicon sequencing on the MinION nanopore sequencer. Gigascience 4, 12 (2015).

    PubMed  PubMed Central  Google Scholar 

  127. 127

    Eckert, S. E., Chan, J. Z.-M., Houniet, D., Breuer, J. & Speight, G. Enrichment of long DNA fragments from mixed samples for Nanopore sequencing. Preprint at bioRxiv (2016).

    Google Scholar 

  128. 128

    Greninger, A. L. et al. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med. 7, 99 (2015).

    PubMed  PubMed Central  Google Scholar 

  129. 129

    Mathijs, E., Vandenbussche, F., Verpoest, S., De Regge, N. & Van Borm, S. Complete genome sequence of pseudorabies virus reference strain NIA3 using single-molecule real-time sequencing. Genome Announc. 4, e00440-16 (2016).

    PubMed  PubMed Central  Google Scholar 

  130. 130

    Bull, R. A. et al. A method for near full-length amplification and sequencing for six hepatitis C virus genotypes. BMC Genomics 17, 247 (2016).

    PubMed  PubMed Central  Google Scholar 

  131. 131

    Kimberlin, D. W. & Whitley, R. J. Antiviral resistance: mechanisms, clinical significance, and future implications. J. Antimicrob. Chemother. 37, 403–421 (1996).

    CAS  Google Scholar 

  132. 132

    Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    CAS  Google Scholar 

  133. 133

    Li, C. et al. INC-Seq: accurate single molecule reads using nanopore sequencing. Gigascience 5, 34 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134

    Travers, K. J., Chin, C. S., Rank, D. R., Eid, J. S. & Turner, S. W. A flexible and efficient template format for circular consensus sequencing and SNP detection. Nucleic Acids Res. 38, e159 (2010).

    PubMed  PubMed Central  Google Scholar 

  135. 135

    Karamitros, T. & Magiorkinis, G. A novel method for the multiplexed target enrichment of MinION next generation sequencing libraries using PCR-generated baits. Nucleic Acids Res. 43, e152 (2015).

    PubMed  PubMed Central  Google Scholar 

  136. 136

    Guan, H. et al. Detection of virus in CSF from the cases with meningoencephalitis by next-generation sequencing. J. Neurovirol. 22, 240–245 (2016).

    CAS  Google Scholar 

  137. 137

    Morfopoulou, S. et al. Deep sequencing reveals persistence of cell-associated mumps vaccine virus in chronic encephalitis. Acta Neuropathol. (2016).

  138. 138

    Brown, J. R. et al. Astrovirus VA1/HMO-C: an increasingly recognized neurotropic pathogen in immunocompromised patients. Clin. Infect. Dis. 60, 881–888 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139

    Fremond, M. L. et al. Next-generation sequencing for diagnosis and tailored therapy: a case report of astrovirus-associated progressive encephalitis. J. Pediatric Infect. Dis. Soc. 4, e53–e57 (2015).

    Google Scholar 

  140. 140

    Barjas-Castro, M. L. et al. Probable transfusion-transmitted Zika virus in Brazil. Transfusion 56, 1684–1688 (2016).

    Google Scholar 

  141. 141

    Musso, D. et al. Potential for Zika virus transmission through blood transfusion demonstrated during an outbreak in French Polynesia, November 2013 to February 2014. Euro Surveill. 19, 20761 (2014).

    Google Scholar 

  142. 142

    Ellison, D. W. et al. Complete genome sequences of Zika virus strains isolated from the blood of patients in Thailand in 2014 and the Philippines in 2012. Genome Announc. 4, e00359-16 (2016).

    PubMed  PubMed Central  Google Scholar 

  143. 143

    Faria, N. R. et al. Mobile real-time surveillance of Zika virus in Brazil. Genome Med. 8, 97 (2016).

    PubMed  PubMed Central  Google Scholar 

  144. 144

    Chitsaz, H. et al. Efficient de novo assembly of single-cell bacterial genomes from short-read data sets. Nat. Biotechnol. 29, 915–921 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145

    Feehery, G. R. et al. A method for selectively enriching microbial DNA from contaminating vertebrate host DNA. PLoS ONE 8, e76096 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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The authors thank J. Brown and K. Gilmour (Great Ormond Street Hospital (GOSH), London, UK) and R. Doyle (University College London (UCL), UK) for their helpful discussions, and J. Quick (University of Birmingham, UK) for sharing unpublished MinION statistics. C.J.H. was funded by Action Medical Research (grant GN2424). M.A.B. was funded through the European Union's Seventh Programme for research, technological development and demonstration under grant agreement number 304875 held by J.B. This work was supported by the UK National Institute for Health Research Biomedical Research Centre at GOSH National Health Service (NHS) Foundation Trust and UCL. J.B. receives funding from the University College London Hospitals (UCLH)/UCL National Institute for Health Research Biomedical Research Centre. The authors acknowledge infrastructure support for the UCL Pathogen Genomics Unit, from the UCL UK Medical Research Council (MRC) Centre for Molecular Medical Virology and the UCLH/UCL National Institute for Health Research Biomedical Research Centre. The funders had no role in study design, data collection and interpretation, or the decision to submit work for publication.

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Correspondence to Judith Breuer.

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Houldcroft, C., Beale, M. & Breuer, J. Clinical and biological insights from viral genome sequencing. Nat Rev Microbiol 15, 183–192 (2017).

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