Whole genome sequencing in AMR surveillance

Whole genome sequencing (WGS) is a comprehensive method used to determine the complete DNA sequence of an organism’s genome. As the cost of WGS continues to decrease, more laboratories and governments are considering whether to introduce it for applications such as antimicrobial resistance (AMR) surveillance. In this course, you will be introduced to the basics of WGS and how it can be applied to AMR surveillance efforts in your country or region.

After completing this course, you will be able to:

• describe the basic principles of WGS
• explain when, how and why WGS is used in AMR surveillance, and how it complements other types of testing and data to strengthen the AMR surveillance system
• give examples of how WGS is used in AMR-related surveillance
• reflect on how increasing WGS capacity could strengthen AMR surveillance in your work
• recognise the cost and economic advantages of WGS for AMR surveillance and understand the barriers to its implementation
• outline what is needed to make the case for increasing WGS capacity in your work.

In order to achieve your digital badge and Statement of Participation for this course, you must:

• click on every page of the course
• pass the end-of-course quiz
• complete the course satisfaction survey.

The quiz allows up to three attempts at each question. A passing grade is 50% or more.

When you have successfully achieved the completion criteria listed above you will receive an email notification that your badge and Statement of Participation have been awarded. (Please note that it can take up to 24 hours for these to be issued.)

In this course you will be introduced to the fundamental concepts of WGS, including commonly used terminology. If you haven’t done so already, it might be helpful for you to review the course Introducing antimicrobial resistance to make sure that you understand what a genome is and how it is relevant to AMR.

So what is WGS?

It’s a laboratory process that determines the complete deoxyribonucleic acid (DNA) sequence of an organism’s genome. It involves extracting the DNA, sequencing it and assembling the sequences into a complete genome using bioinformatic tools. These processes are described in more detail in Section 3 of this course.

As you may have learned in the course Antimicrobial susceptibility testing, laboratories traditionally use phenotypical testing to determine antimicrobial susceptibility. Laboratories might do additional characterisation of resistance determinants by using, for example, polymerase chain reaction (PCR) for confirmation of specific resistance antimicrobial resistance genes (ARGs). Each additional analysis requires further laboratory procedures. (To dive further into this topic, you might want to visit the course Testing for mechanisms of resistance.)

WGS also offers the ability to conduct many types of analysis from a single sequencing run that enables faster and more comprehensive analysis, which can allow public health systems to respond more rapidly to potential outbreak situations. WGS data can be used to identify genes and mutations present in both chromosomal and plasmid DNAs (Figure 1), allowing you to more easily understand the mechanisms behind AMR. If sequence data is good enough, you can determine the possible origin of the host bacteria and even the transmission networks for AMR during an outbreak situation (WHO, 2020).

Figure 1 Genetic components of a bacterial cell. A bacterial cell containing circular chromosomal DNA (large circle, right) and two plasmids (small circles, left). The zoomed-in section represents DNA (the genetic information) that is read during WGS (Lacy-Roberts, 2025).

Further advantages of using WGS for AMR surveillance are summarised in Table 1.

As noted in the previous section, WGS can produce detailed genetic information that allows for precise identification of resistance genes and mutations (WHO, 2020).

Beyond the complete DNA sequence of the bacterial genome, you can learn a lot more information about bacteria using WGS.

Some WGS data can help you track, monitor and compare bacterial strains across time, location and sectors to understand how infections spread and evolve. This data includes the stages listed below:

• Outbreak origin: WGS can show whether a strain has been seen before and where; this helps to track outbreaks and for you to understand whether the strains are spreading in hospitals, communities or across regions.
• Detect outbreaks early: By comparing WGS data, you can tell if infections from different places are caused by related bacteria – indicating a possible outbreak.
• Monitor what is circulating: WGS helps you to keep track of which bacteria and resistance mechanisms are present in your location over time.
• Linking data across sectors: WGS can link data from animals, food, the environment and humans; this supports a One Health approach to understanding and tackling AMR (WHO, 2020).
• How your results compare to other laboratories: WGS provides standard typing results that can be easily shared and compared to those from other labs and countries.

WGS data can also help you assess the potential impact of a bacterial strain on public health. This data includes the following benefits:

• Treatability: WGS identifies AMR genes and point mutations (resistance determinants) that can be used to predict which antibiotics are likely to work, or not work, against an infection.
• Transferability: WGS shows where resistance determinants are located in the DNA sequence, which can tell you if the resistance mechanism can be transferred to other bacteria; this helps to determine whether the resistance is likely to spread and whether the strain poses a higher risk.
• Severity: Some bacteria can cause more serious disease than others; WGS data can help you to identify the virulence (disease severity) of bacteria so that you know if it will be more or less harmful to humans or animals.

WGS data can also be used to identify and describe the specific features of the bacteria.

• Prediction of emerging threats: WGS can detect unusual patterns or new combinations of resistance, helping you spot threats before they become widespread.
• Exact type of bacteria: WGS can tell you not just the species, but also the extract type or strain; this helps you to identify who the bacteria might harm and which hosts it could infect (e.g. humans, poultry, cattle) based on how it causes illness.

You have now been introduced to WGS and its applications. In the next section you will learn more details of its use in AMR surveillance programmes and see several real-life examples of its applications.

In this section, you will explore how WGS can be used in AMR surveillance through a few case studies. You will also look at how WGS can be integrated with, or replace, other testing methods.

Please see the course Antimicrobial resistance in animals for more information about integrated surveillance. It might also be helpful to review Introducing AMR surveillance systems for an overview of the relationship between local, regional, national and international AMR surveillance systems.

The use of WGS for AMR surveillance has grown exponentially since it was introduced in the early 2000s. Public, veterinary and environmental health agencies around the world now use WGS as part of routine surveillance. In the US state of Washington, the Department of Health (DOH) integrated a genomics-first approach into its AMR surveillance system, with the result that it is now better able to identify additional outbreak cases, sensitively classify outbreak or non-outbreak cases, and confirm hypothesised linkages between cases (Torres, et al., 2025). In South Africa, the National Institute for Communicable Diseases conducts WGS-based AMR surveillance with support from the UK Aid Fleming Fund’s SeqAfrica project (DTU National Food Institute, n.d. 1).

Case Study 1 is an example of the use of WGS for AMR surveillance at the national level.

Case Study 1: DANMAP

The Danish Integrated Antimicrobial Resistance Monitoring and Research Programme (DANMAP) integrated WGS into its ongoing surveillance activities to monitor and control AMR starting in 2018. It now uses WGS to:

• improve the detection of resistance genes and mutations in bacterial pathogens
• monitor trends in AMR over time
• investigate and manage outbreaks of resistant infections.

DANMAP is a One Health system that can integrate data from various health registers, thereby connecting genomic data to patient-level metadata, such as hospital records, prescription data and regional food laboratory records and epidemiological information. DANMAP makes use of EPILINX, a visualisation tool that maps patient networks and tracks transmission events during healthcare-setting outbreaks. This tool supports molecular epidemiologists in linking patient movements, risk factors and infection sources through visualisations such as an epidemic curve and colour-coded hospital ward mappings.

DANMAP’s website includes more information about the programme, including previous reports and recordings.

Figure 2 provides a demonstration of how this tool visualises AMR trends over time across different animal and food sources using DANMAP data (DANMAP, 2025).

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Figure 2 Ampicillin resistance in E. coli from Danish livestock and meat, 2000–2023 – percentage of resistance from broilers, pigs, cattle and corresponding meat products (DANMAP, n.d.).

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This graph shows the percentage of E. coli isolates resistant to ampicillin from Danish livestock (broilers, pigs, and cattle) and corresponding meat products (broiler meat, pork, and beef) between 2000 and 2023. Data are based on phenotypic antimicrobial susceptibility testing as reported annually in DANMAP. Each coloured line represents one source, with live animal and meat product categories shown separately to highlight differences in resistance trends across the production chain. The general trend is that resistance in cattle remains low, between 0 and 10%; from the mid-2010s, pork is the highest, at around 30%; broilers begin and end the period at around 15%, with some variation; beef is a similar low level to cattle between 2008 and 2015; pork is similar to pigs over the same period.

Figure 2 Ampicillin resistance in E. coli from Danish livestock and meat, 2000–2023 – percentage of resistance from broilers, pigs, ...

Based on Figure 2, which source has the highest ampicillin-resistant E. coli levels from 2015 onward, and how does this compare to resistance in other livestock groups during that time? What might explain the difference?

Answer

From 2015 onwards, E. coli isolates from Danish pigs show the highest resistance to ampicillin, often exceeding 30%. In contrast, resistance in cattle remains consistently low, generally under 10%. This likely reflects differences in antimicrobial usage between pig and cattle production systems, as well as variation in infection pressure and treatment practices. For broilers, the level is intermediate between that observed in pigs and cattle.

In this section, you have seen examples that illustrate how WGS is transforming AMR surveillance by enabling more precise detection, tracking and response to resistance trends, while also allowing data to be integrated across sectors to follow outbreaks and monitor changes over time, ultimately supporting more effective, data-driven public health interventions.

Phenotypic surveillance is widely used to monitor AMR but often has practical limitations, especially in low-resource settings. Time, cost and infrastructure constraints mean that routine surveillance is often limited to identifying the species, serotype or multi-locus sequence type (MLST type), and performing antimicrobial-susceptibility testing (AST). In higher-income countries, this may be supplemented with other methods, but such tools are often not feasible or scalable for routine use elsewhere.

In most routine surveillance systems, particularly in hospital and veterinary contexts, phenotypic AST continues to serve as the primary method for detecting resistance trends. The data forms the foundation of national and international AMR reporting systems, and is indispensable for tracking resistance prevalence over time.

While WGS offers powerful capabilities, it is not a replacement for phenotypic testing. WGS provides predictions of resistance based on known genes and mutations, but it cannot confirm whether those genetic determinants are expressed or functional. In this sense, WGS is best viewed as a complement to phenotypic surveillance. Phenotypic AST continues to play a critical role in validating genotypic predictions, revealing discrepancies and capturing resistance mechanisms that may not yet be well characterised in genetic databases. That said, phenotypic AST is not without its own limitations: it can miss low-level resistance and may be affected by test conditions.

As sequencing technologies advance, a genomic era is being entered where WGS is increasingly taking the lead in AMR surveillance. It offers the ability to detect a broad range of resistance genes, uncover transmission dynamics and generate detailed data from a single assay. While WGS is not yet ready to replace phenotypic testing for decisions about clinical treatment, it is already transforming how AMR is detected, understood and monitored. In the future, phenotypic testing may take on a more selective role, serving primarily as a confirmatory tool to support increasingly comprehensive genomic approaches.

Here’s how WGS adds value.

• Addressing limitations of routine testing: As described above, WGS provides more detailed information than standard tests, making it easier to spot new threats and understand how resistance spreads – saving time and enabling consistent, scalable surveillance across settings.
• Improving the accuracy of resistance detection: A concordance study has shown that WGS can be more accurate than phenotypic AST – especially where testing quality is inconsistent or performance varies (Rebelo et al., 2025).
• Detecting clinically relevant resistant genes: One of the biggest concerns in AMR surveillance is that most resistance genes affect how bacteria respond to treatment; by detecting these genes, WGS can indicate real clinical risk, making it a powerful tool for early warning systems and timely public health action.
• Explaining unusual phenotypic results: WGS can reveal mutations or resistance mechanisms that explain unexpected AST results, such as resistance without an obvious gene or vice versa.
• Enabling tracking and prediction: WGS can spot signs of antibiotic resistance before they show up in standard laboratory tests, helping track how resistance spreads between people, animals and the environment, and catching new threats early.

Having seen a summary of how WGS adds value to surveillance systems, Case Study 2 highlights some of the real-life benefits.

Case Study 2: ESBL-producing E. coli in Nigerian abattoirs: the added value of WGS

By combining tests for phenotypic antimicrobial susceptibility with WGS, Aworh and colleagues achieved a more holistic understanding of AMR dynamics at the human-animal-environment interphase. Their research, which was conducted in two major Nigeria abattoirs, investigated the prevalence and genetic characteristics of extended-spectrum ß-lactamase-producing Escherichia coli (ESBL-EC) in humans, cattle and environmental samples (Aworh et al., 2022). One example of their data is shown in Figure 3.

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Figure 3 Percentage of different AMR genes detected in ESBL-producing Escherichia coli isolates from three sources: humans (blue), cattle (orange), and abattoir environments (dark green) in Nigeria (Aworh et al., 2022). Each bar represents the total number of isolates carrying a specific resistance gene (named under each bar), grouped by antimicrobial class. The data was generated using WGS, enabling detailed comparison of gene distribution across the human-animal-environment interface (Nilsson, 2025).

What does Figure 3 tell you about how resistance genes such as blaCTX-M-15 or sul1 are distributed across humans, cattle and the environment?

(Sul1 is a sulphonamide resistance gene; blaCTX-M-15 is an extended-spectrum β-lactamase [ESBL] resistance gene.)

Answer

Figure 3 shows that some resistance genes, like blaCTX-M-15 and sul1, are found in E. coli isolates from all three sources: humans, cattle and the environment.

What kind of insight does WGS provide in this example that would not be visible from phenotypic testing alone?

Answer

This suggests that these genes may be circulating between sectors, pointing to possible transmission routes or shared reservoirs of resistance.

WGS allows researchers to detect the exact genes responsible for resistance, something that phenotypic testing alone cannot do. While phenotypic testing can show resistance to a drug class, it cannot reveal whether the same gene is present in different sources or how widespread it is. This genetic detail helps build a clearer picture of how resistance spreads and supports targeted public health interventions.

Phenotypic testing of samples revealed high levels of multidrug resistance, while WGS provided deeper insights into the genetic mechanisms underpinning resistance. WGS identified the widespread presence of key resistance genes, clonal relationships and gene transfer among isolates. The study also highlighted abattoir workers as a high-risk group for faecal carriage of ESBL-EC.

These findings underscore the importance of a One Health approach and demonstrate how genomic tools can enhance surveillance, inform risk assessment and support evidence-based policy decisions such as regulating antimicrobial use in livestock and improving hygiene practices in abattoirs.

Molecular testing refers to techniques such as PCR (polymerase chain reaction) that detect specific resistance genes or mutations at the genetic level. PCR is a method used to amplify small segments of DNA, allowing the detection of even low levels of a resistance gene in a sample. Unlike phenotypic AST, which tests whether bacteria can grow when exposed to antibiotics, molecular methods look for specific resistance genes in the bacteria’s DNA. This means they can detect resistance even if it’s not currently causing a problem.

Molecular tests are often used to confirm the presence of specific resistance genes; for example, following phenotypic detection of AMR. However they can only detect what they are designed to target, so may miss novel or unexpected resistance mechanisms.

To better appreciate the pros and cons of the three main different approaches to AMR screening, Table 2 shows a comparison between AST, molecular testing and WGS.

When it’s combined with epidemiological, clinical and AST data, WGS data can be integrated through analysis to guide effective public health actions. Figure 4 illustrates the importance of linking diverse data sources to produce meaningful, actionable insights.

Figure 4 Interlinkage of different data types for actionable public health results (WHO, 2020).

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An image showing four boxes labelled ‘Epidemiology (population and individual date)’, ‘Clinical data (patient and prescribing data)’, ‘WGS data (reference database)’ and ‘AST data (reference database)’, all connected via arrows to a box labelled ‘Combined analysis’. Thia last box is connected via an arrow to another box, labelled ‘Public health action (prevention and control of infections; community health interventions)’.

Figure 4 Interlinkage of different data types for actionable public health results (WHO, 2020).

To finish this section, complete Activity 4.

Having seen how WGS technology compares to existing surveillance approaches, the next section will focus on the specifics of the processes and discuss practical laboratory aspects to consider.

Based on what you have learnt in the previous sections about the advantages of WGS for AMR surveillance, you might be considering introducing WGS for your country or region’s AMR surveillance.

Introducing WGS can be a complicated process; you must consider various factors. This section will help you consider how you might apply WGS in your own work. It starts by reviewing the process of conducting WGS, including the equipment and tools needed, from DNA extraction to data analysis. By the end of this section, you should understand the essential components required to establish an AMR WGS surveillance facility.

An overview of the four main stages of WGS is shown in Figure 5.

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Figure 5 The four stages of WGS for AMR surveillance (Nilsson, 2025).

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Stage 1 – sample collection: biological or environmental samples are collected from humans, animals, food or the environment as part of One Health surveillance. Stage 2 – DNA isolation and library preparation: DNA is extracted from the samples and prepared into sequencing libraries. Stage 3 – sequencing: DNA is sequenced using sequencing machines, such as Illumina or Oxford Nanopore’s MinION. Stage 4 – data analysis: sequencing data is analysed to identify antimicrobial resistance genes, bacterial species or strains, and potential epidemiological links.

Figure 5 The four stages of WGS for AMR surveillance (Nilsson, 2025).

Each of the four stages is discussed in more detail in the following sections. Data sharing and infrastructure considerations are also discussed. The specific steps in each stage are tailored to whether you are conducting short or long read sequencing. Important aspects that need to be considered in planning are noted in the next sections.

As with phenotypic surveillance, the first step in conducting WGS is sample collection.

When you collect a sample for WGS, you will need to ensure that relevant metadata is also collected. Metadata is data that describes the context of your sample and typically includes the location, date and time of collection; species, host and sample source; and type (e.g. faeces, sewage). You may also want to collect demographic, clinical and epidemiological information of relevance.

It is important to collect metadata at the time of sampling, as it will be critical for interpreting your results later and for further analysis. Metadata provides the context needed to interpret WGS data and is key to turning it into meaningful public health or research insights. For more information on the importance of metadata for analysis of AMR patterns and trends you may like to visit the Gender and equity in AMR surveillance course.

Once you have collected your samples, you will need to decide which ones to sequence. Sample selection for WGS can be a complicated topic, as explained below.

• Sequencing is much more costly than typical phenotypic methods, so you may not be able to sequence every isolate.
• You may want to prioritise what you sequence, perhaps by ensuring that you sequence samples from a wide geographic area in your surveillance zone.
• You might want to randomise which isolates are sequenced to understand prevalent genotypes.
• A good sample selection strategy will need to be part of your plan for using WGS in your AMR surveillance.

For more information on this topic, you can watch a webinar produced by the Fleming Fund’s SeqAfrica project, ‘How to decide what to sequence’.

As in phenotypic surveillance, you’ll begin wet-laboratory work for WGS by culturing your samples. However, the next steps will differ somewhat from the usual microbiology laboratory work. For WGS, laboratory technicians must conduct DNA extraction, where cells from a pure culture are broken to isolate the DNA, and proteins and cell debris are removed.

You should also measure the extracted DNA (quantitation) to make sure that you have the specific volume and amount of DNA needed for the library preparation step that follows.

The next step in preparing for sequencing in the laboratory is called library preparation. This step includes modifying the ends of the DNA that you’ve extracted to make sure they are recognised and read by the sequencing machine. Additional steps may also be included, depending on the sequence technology being used (CDC, 2024).

This stage involves loading your DNA library into the sequencing machine and running the appropriate sequencing software to determine the sequence of DNA. The basic unit of DNA is a base pair which is made up of two chemical bases that pair together (A with T, and C with G). DNA can be thought of as a long string of these base pairs, and sequencing reveals the exact order of those pairs in the string. The software you use for sequencing will depend on the type of sequencing technology in your laboratory.

Two approaches to WGS are commonly used:

• Second-generation (short read) sequencing produces short reads (DNA fragment sequences) (50–500 base pairs) by running many reactions at once.
• Third-generation (long read) sequencing uses newer methods to read much longer stretches of DNA.

Each technology has its own advantages and limitations; Table 3 has a detailed comparison of them.

For short-read sequencing, Illumina machines are the most widely used machines in all genomics work. There is a wide range of Illumina machines (Figure 6), with varying prices and specs. As an example, a SeqAfrica partner laboratory in Ghana purchased an Illumina Nextseq 1000, including freight, installation and training for around £186,000 (2023).

Figure 6 An Illumina Nextseq 500 on a laboratory bench.

Video 1 shows how an Illumina workflow functions.

Oxford Nanopore Technologies (ONT) produces the most used long-read sequencing devices. A Nanopore MinION Mk1D pack including sequencing consumables and five MinION Flow Cells cost around £3600 in 2025. As seen in Figure 7, the MinION is a portable real-time sequencing device.

Figure 7 An Oxford Nanopore MinION, which illustrates the instrument’s portability.

The MinION device needs to be connected to a PC or laptop (for field-based analysis) to run and collect data. Oxford Nanopore Technologies’ website includes a video of how nanopore sequencing works.

For both types of sequencing, the companies produce a variety of machines, each with different levels of accuracy and potential throughput.

After the sequencing is completed, the machine returns DNA fragment sequences (‘reads’) that can be analysed on a computer.

Before further analysis, a quality control (QC) step ensures that only high-quality sequence data is used. An example of an automatically generated QC report from an ONT sequencing run is shown in Figure 8 below.

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Figure 8 Sequencing run summary from an ONT report.

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A screenshot with the heading ‘Run summary’ showing various data points. Under the sub-heading ‘Data output’, estimated bases are 5.9 Gb, data produced is 59.37 GB, reads generated are 1.5 m and estimated N50 is 10.56 kb. Under the sub-heading ‘Run duration’, elapsed time is 19 hours and 42 minutes of 72 hours, and run status is described as ‘running’. Under the sub-heading ‘Basecalling’, reads called are 99.99% and bases called (with a minimum Q score of 9) are listed as 5.02 Gb pass and 839.77 Mb fail.

Figure 8 Sequencing run summary from an ONT report.

The sample portion of a QC report generated by ONT during a sequencing run shown in Figure 8 was generated about 19 hours into a 72-hour run. This is just a portion of a much larger report to give you a sense of how an ONT run report looks. As can be seen, the system has produced approximately 60 gigabytes (GB) of raw data. You can see that 99.99% of the sequence has been successfully read as base pairs (reads called), with only a tiny percentage being highlighted as a fail. This is only a partial run; ONT runs typically generate even more data if allowed to complete the full 72 hours. ONT provides detailed, real-time QC to help users monitor yield, quality and run progress at any time.

Once the high-quality data is ready, it is analysed using specialised software. This can be done by trained bioinformaticians, but there are also free, cloud-based platforms available that make analysis more accessible, especially for smaller labs or public health institutions.

The next steps depend on the type of sequencing that has been performed, as detailed below.

• With short-read data, the DNA fragments are usually compared to the sequence of a known reference genome. This helps to identify DNA sequence differences between your sample and known strains, such as changes that may affect resistance to antibiotics, how the bacteria are spreading or how they may have evolved.
• With long-read data, the software often builds the sequence of the genome from scratch – a process called genome assembly. This approach is especially good for detecting plasmids, mobile resistance genes and parts of the genome that are difficult to analyse using short-read technologies. Plasmids and mobile resistance genes are important because they often carry AMR genes and can move between bacteria. This means they can spread resistance not just within one species but across different species and environments. Being able to detect and track these elements helps us understand how resistance is spreading and where interventions may be needed.

After the reads are mapped or assembled into a full genome, the sequences can be uploaded by the users to a trusted international database such as ResFinder, CARD (The Comprehensive Antibiotic Resistance Database), and VFDB (The Virulence Factor Database), where the reads are compared to existing genomes already in the database to see how closely related they are. This is important for tracking outbreaks, understanding how resistance is spreading, and identifying the likely source of an infection.

Figure 9 shows the AMR profile predicted from the genome of an E. coli isolate using the ResFinder tool. As can be seen, the panel lists antimicrobials screened, their class, the predicted resistance phenotype based on WGS and the genetic background (i.e. the resistance gene detected). This output demonstrates how WGS can be used to predict resistance to multiple antibiotic classes and link phenotypes to specific genetic determinants.

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Figure 9 ResFinder output for a whole genome assembly of Escherichia coli.

While this course focuses on sequencing from cultured isolates, another emerging approach is metagenomics – the direct sequencing of DNA from complex samples such as food, clinical or wastewater samples without prior culturing. These samples often contain a mixture of DNA from multiple organisms. During the data analysis stage, bioinformatic tools are used to separate and identify the different organisms’ DNA sequences. Metagenomics has the potential to reduce turnaround times and improve detection of unculturable bacteria or mixed infections. However, it still faces technical and cost-related barriers, including low DNA yield, contamination and complex data interpretation. As sequencing technologies and analysis tools continue to advance, metagenomic approaches may become increasingly practical and valuable in the future.

After your data has been analysed, your next task is to ensure that the information you’ve gathered is used within your public, veterinary and/or environmental health systems.

If you suspect an outbreak or emerging threat, you might need to take immediate public health action based on your results. This could involve alerting a local hospital, a disease-control agency or government health department. You may have findings that should be shared with policy-makers. More information about using data for action in AMR can be found in the course Using AMR data for policy-making.

From a systemic surveillance perspective, you will need to ensure that you comply with national reporting guidelines and follow privacy and confidentiality protections related to data-sharing. Please also see the course Legal and ethical considerations in AMR data for more information on legal and ethical considerations related to data-sharing.

As previously mentioned, one of the advantages of WGS is that the data can be re-analysed at a future time if you have a new question or emerging issue. Therefore, you will want to ensure that you save the raw data, your analysis, your comprehensive metadata and the genome in a secure, accessible and well organised location.

You can consider saving the data in a public repository such as the National Center for Biotechnology Information (NCBI)’s GenBank, the European Nucleotide Archive (ENA) or the DNA Data Bank of Japan (DDBJ). These repositories are part of international data-sharing networks that ensure your sequencing data can be accessed by researchers and public health agencies worldwide. Sharing data publicly supports global efforts to monitor AMR, detect emerging threats and respond quickly to outbreaks. It also allows your data to be reused for future studies, making your contribution more impactful.

You could also consider using a commercial cloud service or local storage such as a high-performance computing (HPC) cluster or network-attached storage (NAS). These solutions will have costs associated with them.

Establishing a WGS facility requires you to consider whether the existing infrastructure you have is suitable.

As part of the planning process, suitable facilities will need to be put in place for these stages.

In addition to laboratory space, when establishing a WGS centre it’s essential to ensure reliable power and internet access to avoid costly disruptions during sequencing and analysis. If power is interrupted during an Illumina run, the sequencing process usually cannot be resumed, often resulting in the loss of reagents and the need to restart the entire run. By contrast, Oxford Nanopore Technologies (ONT) runs can sometimes be paused and resumed, depending on the set-up and software version. Continuous power is also important for keeping reagents and samples refrigerated or frozen.

Similarly, during the data-analysis phase, internet connectivity plays a key role. Cloud-based platforms such as BaseSpace or Epi2Me depend on uninterrupted internet access to function properly: if the connection is lost, the analysis may stall or fail. However, local tools and offline workflows (e.g. command-line pipelines) can typically continue running without internet access, provided the required data and tools are already downloaded.

Another critical infrastructure consideration is data storage (Figure 10). Especially when it is done at scale, sequencing generates large volumes of data that must be securely stored and managed.

Figure 10 Sequencing data requires large amounts of data storage, which can be local or cloud-based.

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A photograph of a data storage facility.

Figure 10 Sequencing data requires large amounts of data storage, which can be local or cloud-based.

As an example of the storage requirements, sequencing a single E. coli isolate typically produces around 250–500 MB of data with short-read machines like Illumina, and approximately 150–300 MB with long-read machines like ONT. Even though ONT reads are much longer, fewer are needed to sequence a genome, so the total amount of data generated can be smaller than with short-read technologies.

Both machines generate additional files during downstream analysis, such as assemblies and resistance-gene reports, that can quickly consume terabytes of space when processing large numbers of samples. Institutions may choose between cloud storage (for flexibility and scalability) or local servers (for faster access and better control – especially in low-bandwidth environments).

You’ll also need to consider the computational resources required for data analysis. Web-based tools like EPI2ME, BaseClear and PathogenWatch are user-friendly and require minimal local computing power, making them ideal for beginners or low-resource settings. However, more advanced or large-scale tasks (e.g. genome assembly, variant detection or AMR surveillance) require a high-performance computer with a strong processor, sufficient RAM and ample hard drive space.

Careful planning across power supply, internet connectivity, data storage and computing capacity is essential to build efficient, scalable and sustainable WGS operations, particularly in resource-limited settings.

Having learned about the practicalities of establishing a WGS facility, in the next section the focus moves onto examining how a laboratory might be integrated into a wider surveillance network.

In the previous section you looked at the requirements for introducing WGS in an individual laboratory. Now you will look at the requirements for conducting AMR surveillance using WGS within a national, state or regional context.

Genomic surveillance has many of the same requirements as phenotypic surveillance. Therefore, a key requirement for building a genomic AMR surveillance system is usually the existence of a functioning phenotypic AMR surveillance system. To conduct AMR surveillance you will need a referral system through which samples are collected and sent to a national reference laboratory (NRL) for analysis. The referral sites should collect the needed metadata related to each sample.

In most cases, the NRL should be endorsed by the national authorities for its surveillance role to lend the laboratory legitimacy and remit. The referral system and NRL will need sufficient resources such as capacity, staff and funding to perform their roles within the system. The referral system should be as comprehensive as possible in both demographic and geographic scope. The timeliness of your referral system is also important: the turnaround time from sample collection and sequencing is vital to ensuring that your system can inform public health action.

The NRL should have the required and competent staff to analyse and use the data resulting from WGS. These specialists must work closely together to achieve their collective surveillance goals. At a minimum this staff should include the specialists listed below:

• Bioinformatician: Responsible for ensuring sequencing data quality is suited for analysis. They will also employ tools for sequencing data analysis. They should be able to use a variety of bioinformatic tools for both analysis and quality control. They should ideally have strong knowledge of genomics and how to work with genomic data because they will interpret what the data shows for the other team members.
• Microbiologist: Responsible for interpreting bacterial typing results. Their job is to determine what is important about a specific bacterium within a specific context. The microbiologist will work closely with the bioinformatician to help them determine what markers to look for in the data; they will also work closely with the epidemiologist to provide an interpretation of the biological data for action.
• Epidemiologist: Responsible for examining and interpreting epidemiological data, resistance trends and changing epidemiology. They will identify possible sources of outbreaks, and record and report outbreak signals to determine what action should be taken to protect public health. The epidemiologist will work closely with the bioinformatician to determine what type of analysis should be conducted.

A national or regional plan for priority pathogens for sequencing can also be important for introducing WGS into your AMR surveillance system. Thinking about local disease burden, the population’s immunity, case-fatality rates, outbreak risk, transmissibility and drug resistance will help inform this plan (Khoo et al., 2024). Phenotypic data such as antimicrobial-susceptibility profiles can further guide the selection of isolates for sequencing by highlighting unusual resistance patterns or treatment failures. This ensures that sequencing efforts are targeted toward the most clinically and epidemiologically relevant cases.

The purpose of collecting and analysing the AMR surveillance data is to inform public health actions. Therefore, a major consideration for establishing a WGS system for AMR surveillance is whether there is political commitment to use the data. This commitment should ideally be codified within legislation, policy or regulations. If political commitment and laws exist for the phenotypic surveillance system, they will need to be built upon to also support the use of WGS. Policy-makers and stakeholders should be introduced to the benefits of WGS for AMR surveillance and they should be trained in how to interpret and use the new data.

Ideally, WGS data should be integrated into national AMR or One Health surveillance and reporting systems as soon as possible. This will allow the health system to take advantage of the new data for detecting community or hospital outbreaks. The addition of this new type of data may require upgrades to the current system and staff training in how to upload and use the new data (Rebelo, 2022).

Many governments have reporting requirements regarding AMR. When certain pathogens are found within the surveillance system, public authorities must be notified. The appearance of other less virulent pathogens might only need to be shared within the country’s reporting system.

Whatever the requirements, the introduction of WGS to a surveillance system should not adversely impact required reporting. Data should be reported in a timely and transparent manner as it was within the phenotypic surveillance system. Regulations concerning data protection and the anonymisation of patient data should also continue to be respected. When data will be shared between entities, data-sharing agreements that outline the terms and conditions for sharing data should be put in place.

Now that you have considered the wider aspects of whether and how a new WGS facility would fit within your system, the focus shifts to considering the important aspect of the costs involved in establishing a WGS facility.

In this section you will cover the costs of introducing and maintaining WGS as part of your AMR surveillance system. You can refer to the course The health and economic burden of AMR for more information on this topic, and the WHO has introduced a genomics costing tool that can serve as a reference for you (WHO, 2024).

While only a few manufacturers dominate the supply of WGS equipment and supplies, costs are variable depending on geography. It is often more expensive to introduce and maintain a WGS system in a low- or middle-income country (LMIC) than in a high-income country owing to the lack of local manufacturing facilities, economies of scale and the cost of transporting materials and equipment – especially items that require a cold chain. Service charges added by local suppliers, national taxes and customs fees can also vary dramatically between countries.

Figure 11 A typical molecular laboratory utilises specialised laboratory equipment (pipettes), consumables (tubes, gloves) and cold chain-dependent reagents.

Because of the high costs of conducting WGS, countries might instead consider using regional rather than national or local sequencing laboratories. The WHO report GLASS Whole-genome Sequencing for Surveillance of Antimicrobial Resistance (2020) recommends the creation of regional ‘hub-and-spoke’ surveillance systems where participating countries can send isolates of interest to a regional sequencing centre.

As you think about the costs of WGS, Table 6 can help you to compare the average costs and scale of your current phenotypic surveillance system with a WGS system.

The costs of setting up WGS in a laboratory will depend on many factors, including the considerations below.

• What infrastructure is currently in the laboratory? Laboratories that already conduct molecular methods, for example, will see lower conversion costs to WGS.
• How many isolates will be sequenced on a regular basis? This will determine whether you need a large, high-throughput machine or can use a smaller one.
• Is the laboratory currently limited to phenotypic methods such as AST? Laboratories without existing molecular workflows may face steeper initial investments in equipment, staff training and bioinformatics capacity. However, leveraging existing AST data to prioritise isolates for sequencing can help to maximise the value of early WGS efforts and support a phased implementation approach.

As you think about the costs of setting up a system, don’t forget about broadband internet connections, generators and uninterruptible power supplies.

For laboratories currently using only phenotypic AST, it is important to recognise that these methods will continue to play a role in AMR surveillance; WGS is a complementary tool that enhances the resolution and scope of surveillance, rather than a replacement for existing phenotypic systems.

As described above, a WGS system requires at minimum laboratory scientists with genomics experience, bioinformaticians with epidemiology and microbiology training, and an epidemiologist. Ongoing skills development should be considered as part of your budget owing to the continually evolving nature of genomics technologies, protocols and bioinformatic approaches.

The cost of hiring staff and training them in new skills will vary depending on the local job market – but note that specialists skilled in these areas can be difficult to hire and retain in LMICs (Halpin, 2025).

Anyone contemplating the introduction of WGS to their AMR surveillance must also consider the maintenance and running costs of the system.

Procuring the necessary reagents can be costly and challenging for many LMICs’ laboratories because of a poor supply chain. Many reagents for WGS have a short shelf-life and/or must be kept cold, so delays owing to customs clearance can be expensive and wasteful. Establishing a network for procurement with other laboratories in your area/country is one solution to be considered prior to starting up WGS. Similarly, establishing a national procurement forecasting plan for reagents can also be a helpful tool (Khoo et al., 2024).

The price of reagents varies according to geography and suppliers. The Fleming Fund SeqAfrica project, for example, saw the price of sequencing a single isolate range from £50 to £140 depending on the country and the throughput of the laboratory. It is worth considering, however, that the cost of testing for all known AMR mechanisms at once using WGS is only marginally higher than testing for only one mechanism, whereas testing all the necessary antimicrobials with phenotypic AST would require more reagents, materials and staff time.

By contrast, phenotypic AST has relatively low and predictable ongoing costs. Reagents such as antibiotic disks or broth media are widely available, have longer shelf lives and do not require cold-chain logistics. Additionally, AST does not require highly specialised staff or computational infrastructure, making it more sustainable for routine surveillance in many low-resource settings. However, while WGS has higher per-sample costs, it can provide broader data from a single test, potentially reducing the need for multiple phenotypic assays.

Beyond ongoing procurement, you should consider the cost of repairs and maintenance of expensive WGS machines. Most suppliers of WGS machines offer a service contract for the first year after the machine is purchased; however, after this runs out, further coverage can be very expensive. For example, in 2024 one laboratory in Senegal was offered a service contract costing $8000 for a one-year contract on a NextSeq 550, while a laboratory in Ghana was quoted $25,000 for a one-year contract on a NextSeq 2000 system. These costs may strain laboratory budgets, but a single repair can cost as much as the service contract, if not more, making the contract a good investment and insurance mechanism.

If you are beginning to contemplate the utility of WGS for your AMR surveillance system, you might consider outsourcing some or all the tasks associated with the WGS process. Commercial sequencing services can conduct tasks ranging from sample preparation to sequencing to basic or complex bioinformatic analysis. There are also services and genomic platforms focused on bioinformatic analysis that may be of interest to new entrants to WGS.

By outsourcing your WGS needs at first, you will be able to demonstrate to key policy-makers and other stakeholders the value of WGS for your surveillance. Commercial providers will also have fast turnaround time for analysis, provide high-quality results, and can scale up or down based on your current workload.

However, outsourcing comes with its own challenges. For example:

• shipping biological materials can be tricky and expensive
• material transfer agreements must be negotiated between the laboratory providing the sample and the sequencing or analytical company
• governments may not allow or may discourage samples leaving their national borders.

All these considerations must be taken into account when considering outsourcing WGS services.

Having now considered the costs of WGS, the course finishes with a look at the economic advantages of introducing this technology.

What are some of the ways that using WGS as part of an AMR surveillance system can provide economic benefits to a country or region?

While the above-mentioned costs associated with WGS can seem overwhelming, the economic benefits of introducing the new technology are also remarkable. The introduction of a tool that can aid in early detection and identification of outbreaks in the short term and can prevent the longer-term loss of economic growth and productivity can be highly beneficial.

The use of WGS can:

• guide antimicrobial stewardship
• evaluate the effectiveness of diagnostic tools
• improve contract tracing (Khoo et al., 2024).

Because AMR impacts not just humans but animals and food as well, cost ‘gains’ associated with averted outbreaks can save significant sums by preventing the need to cull herds or stop the export of a country’s produce.

While it is difficult to put a specific value on the gains of an outbreak that was avoided or mitigated, a major economic benefit of surveillance is preparedness – which should not be underestimated.

A few studies have tried to monetise these savings. An analysis of the USFDA GenomeTrakr SGS Network estimated that the system would accrue an estimated annual health benefit of nearly $500 million, compared with an approximately $22 million investment by public health agencies (Brown et al., 2021). A study of the introduction of WGS in the detection of Salmonellosis outbreaks in Canada estimated monetary and non-monetary costs. The incidence of illnesses in Canada was 47,082 annually, representing a cost of C$287.78 million (total cases) and C$166.28 million (reported cases). The introduction of WGS was estimated to accrue benefits ranging from C$5.21 million to C$90.25 million (Jain, Mukhopadhyay and Thomassin, 2019).

You should now have a better understanding of how WGS can be used for AMR surveillance. Before you finish this course, the final activity below will help you to think about your role in introducing the concept of WGS to your peers and stakeholders.

As you consider integrating WGS into your AMR surveillance activities, remember that phenotypic testing remains important for monitoring resistance trends, validating genomic predictions and fulfilling reporting obligations. WGS adds depth and context to the data from these, enabling a more comprehensive understanding of resistance dynamics.

Moving forward, you might want to consult these resources for further learning:

• Online courses and webinars created through the SeqAfrica project (DTU National Food Institute, n.d. 2)
• GLASS Whole-genome Sequencing for Surveillance of Antimicrobial Resistance (WHO, 2020)
• Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential, 2022–2032 (WHO, 2022)
• Whole Genome Sequencing as a Tool to Strengthen Foodborne Disease Surveillance and Response (WHO, 2023)
• Whole Genome Sequencing for Foodborne Disease Surveillance: Landscape Paper (WHO, 2018)

Well done – you have reached the end of this course and can now do the quiz to test your learning.

This quiz is an opportunity for you to reflect on what you have learned rather than a test, and you can revisit it as many times as you like. 

• End-of-course quiz

Open the quiz in a new tab or window by holding down ‘Ctrl’ (or ‘Cmd’ on a Mac) when you click on the link.

In this course you have learned the basics of introducing WGS into your AMR surveillance system. You have read about the WGS process in the laboratory, the minimum requirements for introducing WGS into an AMR surveillance system and the costs and cost benefits associated with using WGS.

You should now be able to:

• describe the basic principles of WGS
• explain when, how and why WGS is used in AMR surveillance, and how it complements other types of testing and data to strengthen the AMR surveillance system
• give examples of how WGS is used in AMR-related surveillance
• reflect on how increasing WGS capacity could strengthen AMR surveillance in your work
• recognise the cost and economic advantages of WGS for AMR surveillance and understand the barriers to its implementation
• outline what is needed to make the case for increasing WGS capacity in your work.

Now that you have completed this course, consider the following questions:

• What is the single most important lesson that you have taken away from this course?
• How relevant is it to your work?
• Can you suggest ways in which this new knowledge can benefit your practice?

When you have reflected on these, go to your reflective blog  and note down your thoughts.

You’ve now reached the end of this course. If you’ve enrolled on a pathway, please go back to the pathway page and tick the box to confirm that you’ve completed this course. On the pathway page you’ll see both your progress so far as well as the other courses you need to complete in order to achieve your Certificate of Completion for that pathway.

Now that you have completed this course, take a few moments to reflect on your experience of working through it. Please complete a survey to tell us about your reflections. Your responses will allow us to gauge how useful you have found this course and how effectively you have engaged with the content. We will also use your feedback on this pathway to better inform the design of future online experiences for our learners.

Many thanks for your help.

Now go to the survey.

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This free course was collaboratively written by Heike Schmitt, Roosmarijn Luiken, Hilary MacQueen and Dawn Harmon, and reviewed by Michael Macey, Will Gaze, Stanley Fenwick and Rachel McMullan.

Except for third party materials and otherwise stated (see terms and conditions), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence.

The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course:

Images

Course banner: PASIEKA/SCIENCE PHOTO LIBRARY/Universal Images Group

Figure 1: Lacy-Roberts (2025); created in BioRender, https://BioRender.com/h1tar74; this file is licensed under the Creative Commons Attribution Licence https://creativecommons.org/licenses/by/4.0/

Figure 2: DANMAP

Figure 3: Aworh et al. (2022) ; this file is licensed under the Creative Commons Attribution Licence https://creativecommons.org/licenses/by/4.0/

Figure 4: adapted from https://www.who.int/publications/i/item/9789240011007

Figure 5: Nilsson (2025); created in BioRender, https://BioRender.com/lqzgrel; this file is licensed under the Creative Commons Attribution Licence https://creativecommons.org/licenses/by/4.0/

Figure 6: DTU Food, GloCab

Figure 8: captured by Niamh Lacy-Roberts

Figure 10: panumas nikhomkhai/Pexels

Figure 11: Martin Lopez/Pexels