The Role of Whole-Genome Sequencing in Global AMR Surveillance

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Whole-genome sequencing strengthens global antimicrobial resistance surveillance by revealing resistance genes, mobile genetic elements, and strain relationships that phenotypic testing alone cannot detect.
• Public health laboratories can participate in coordinated surveillance through established frameworks such as WHO GLASS and NCBI Pathogen Detection, which aggregate sequence data across borders.
• Geographic and pathogen coverage remains uneven, and sequencing capacity, data standards, and biosafety requirements create real constraints for many national programs.

The Global AMR Surveillance Landscape

Antimicrobial resistance is a biological phenomenon in which bacteria evolve mechanisms that reduce or eliminate the effectiveness of drugs used to treat infections. The World Health Organization frames AMR as a global health threat that requires coordinated surveillance, stewardship, and prevention efforts across human health, animal health, and environmental sectors. Surveillance programs collect data on which resistant organisms are circulating, where they are found, and how they are spreading. This information guides treatment decisions, infection control policies, and public health interventions.

Traditional AMR surveillance relies on phenotypic antimicrobial susceptibility testing, which measures whether a bacterial isolate grows in the presence of specific drugs. Clinical and Laboratory Standards Institute documents provide standardized methods for these tests, and laboratories use them to generate reproducible results across facilities [<a href="#ref-1">1</a>]. Phenotypic testing remains essential for clinical decision-making because it directly measures drug susceptibility. However, it does not reveal the genetic mechanisms behind resistance, the mobile elements that carry resistance genes, or the evolutionary relationships between isolates.

Whole-genome sequencing changes the resolution of surveillance. Instead of asking only whether an isolate is resistant, sequencing asks which genes are present, how they are arranged, and how the isolate relates to others in the same geographic region or across continents. This information supports outbreak detection, transmission tracing, and the identification of emerging resistance mechanisms before they become widespread.

The practical problem for public health professionals is not whether WGS is useful. The problem is understanding the current landscape of global surveillance initiatives, identifying where their laboratory can contribute, and recognizing the gaps that limit the usefulness of existing data. This article reviews the major global programs, the technical and operational requirements for participation, and the limitations that laboratories should consider when planning their sequencing activities.

At a Glance

The table below summarizes the major global AMR surveillance initiatives and the practical considerations for laboratories that want to participate.

Core Principles of Genomic AMR Surveillance

What Whole-Genome Sequencing Detects

Whole-genome sequencing reads the complete DNA sequence of a bacterial isolate. The resulting data can be analyzed for the presence of known antimicrobial resistance genes, mutations in chromosomal genes that confer resistance, and the genetic context of these elements. This context matters because resistance genes located on plasmids or other mobile genetic elements can transfer between bacterial species and strains.

The sequence data also supports phylogenetic analysis. By comparing the genomes of isolates from different patients, hospitals, or countries, researchers can determine whether the isolates are closely related and whether they share a common source. This is the basis for outbreak detection and transmission tracing.

The WHO describes AMR surveillance as a core component of the global response to antimicrobial resistance [WHO AMR]. Genomic surveillance adds a layer of information that phenotypic testing cannot provide, but it does not replace phenotypic testing. The two approaches are complementary.

The Relationship Between Genotype and Phenotype

A resistance gene does not always produce a resistant phenotype. The gene may be present but not expressed, or the organism may have additional mutations that affect the outcome. Conversely, resistance can arise through mutations in genes that are not yet annotated as resistance determinants. This means that genomic prediction of resistance is not always accurate.

Laboratories that use sequencing for surveillance should therefore interpret genomic data in the context of phenotypic susceptibility testing. The CLSI standards provide the framework for phenotypic testing, and the results from these tests remain the reference point for clinical decisions [CLSI]. Genomic data adds context but does not replace the need for phenotypic confirmation.

Major Global Surveillance Initiatives

WHO GLASS and the Global AMR Surveillance System

The WHO Global Antimicrobial Resistance and Use Surveillance System, known as GLASS, is the primary global mechanism for standardizing AMR surveillance data. GLASS collects data from participating countries on the prevalence of resistance in selected bacterial pathogens. The system uses standardized case definitions and data collection protocols to make comparisons across countries meaningful.

GLASS focuses on phenotypic data from clinical isolates. Participating countries report data on pathogens such as Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and others. The system also tracks antimicrobial consumption data, which helps link resistance patterns to drug use.

The WHO describes AMR surveillance as essential for informing treatment guidelines, infection prevention, and stewardship programs [WHO]. GLASS is the operational mechanism for this surveillance at the global level.

NCBI Pathogen Detection

The NCBI Pathogen Detection system is a public database that aggregates bacterial whole-genome sequences from submitters worldwide. The system automatically analyzes the sequences for resistance genes, virulence factors, and phylogenetic relationships. It also clusters related isolates to identify potential outbreaks.

The database is open to any laboratory that can submit sequence data. Submitters provide metadata about the isolate, including the source, collection date, and geographic location. The system then performs the analysis and makes the results publicly available.

This system is a major resource for genomic surveillance because it allows laboratories to compare their isolates with sequences from around the world. It also supports the identification of emerging resistance mechanisms and the tracking of transmission events across borders.

WHO AMR Surveillance and Stewardship

The WHO coordinates AMR surveillance through its broader antimicrobial resistance program. This program includes surveillance, stewardship, and prevention activities. The WHO provides guidance to countries on how to establish and maintain AMR surveillance systems, and it supports the development of national action plans.

The WHO also coordinates the Global Antimicrobial Resistance and Use Surveillance System, which is the data collection mechanism for the global program. The WHO describes the program as a response to the growing threat of AMR and a framework for coordinated action [WHO].

Laboratory Standards and Quality Control

The Clinical and Laboratory Standards Institute publishes standards for antimicrobial susceptibility testing. These standards define the methods, reagents, and quality control procedures that laboratories should use to produce reliable results [CLI]. The standards are used by laboratories worldwide, and they provide the basis for comparing data across facilities.

The American Type Culture Collection provides authenticated bacterial strains that laboratories use for quality control. These strains have known resistance profiles and are used to validate testing methods and ensure that results are reproducible [ATCC]. Authenticated strains are essential for quality control in both phenotypic and genomic testing.

The American Society for Microbiology provides professional education and laboratory practice resources for microbiologists [ASM]. These resources support the workforce that performs AMR surveillance and testing.

Practical Workflow for Genomic Surveillance

Step 1: Define the Surveillance Question

Before starting sequencing, a laboratory should define the question it wants to answer. This could be a question about the prevalence of a specific resistance gene, the relationship between isolates from different patients, or the emergence of a new resistance mechanism. The question determines the sampling strategy, the number of isolates to sequence, and the analysis methods.

Step 2: Select Isolates for Sequencing

The isolates selected for sequencing should represent the population of interest. This may be all isolates from a specific clinical syndrome, a random sample of isolates from a geographic area, or isolates that have unusual phenotypic resistance patterns. The sampling strategy should be documented so that the results can be interpreted in the context of the population.

Step 3: Prepare and Sequence DNA

DNA extraction and sequencing require specialized equipment and protocols. The laboratory should follow validated protocols for DNA extraction, library preparation, and sequencing. The quality of the sequence data depends on the quality of the DNA and the sequencing platform used.

Step 4: Analyze the Sequence Data

The sequence data is analyzed using bioinformatics tools. The analysis includes quality assessment, assembly, and the identification of resistance genes and other genetic elements. The analysis should be performed using validated tools and the results should be interpreted in the context of the surveillance question.

Step 5: Submit Data to Public Databases

The sequence data and associated metadata should be submitted to a public database such as NCBI Pathogen Detection. The submission should include the isolate source, collection date, and geographic location. This allows the data to be compared with other isolates and contributes to the global surveillance effort.

Step 6: Interpret and Report the Results

The results should be interpreted in the context of the surveillance question and the limitations of the data. The laboratory should report the results to the relevant stakeholders, including public health authorities and clinical teams. The report should include the limitations of the data and the implications for public health.

Options and Tradeoffs in Sequencing Platforms

The choice of sequencing platform depends on the laboratory's needs and resources. The main options are short-read sequencing, long-read sequencing, and targeted sequencing.

Short-read sequencing is the most common approach. It produces high-quality data for most applications, including resistance gene identification and phylogenetic analysis. The main limitation is that short reads cannot always resolve repetitive regions or the full structure of plasmids.

Long-read sequencing produces longer reads that can span repetitive regions and provide a more complete picture of the genome. This is useful for resolving plasmid structures and for identifying the genetic context of resistance genes. The cost is higher and the error rate is higher than short-read sequencing.

Targeted sequencing focuses on specific genes or regions of interest. This is useful for surveillance of specific resistance genes, but it does not provide the full genome information that is needed for phylogenetic analysis.

The choice of platform should be based on the surveillance question, the available resources, and the need for data that can be compared with other laboratories.

Records and Measurements

Metadata Standards

The value of genomic surveillance data depends on the quality of the metadata. The metadata should include the isolate source, the collection date, the geographic location, and the clinical context. The metadata should be recorded in a standardized format so that the data can be compared across laboratories.

Quality Control Records

The laboratory should maintain records of the quality control procedures used for sequencing. This includes the DNA extraction quality, the sequencing depth, and the quality of the assembled genome. The records should be available for review and should be used to assess the reliability of the data.

Data Storage and Backup

The sequence data should be stored in a secure and accessible format. The laboratory should have a backup system to prevent data loss. The data should be stored in a format that can be used for future analysis and comparison.

Common Failure Patterns in Genomic Surveillance

Incomplete Metadata

The most common failure in genomic surveillance is incomplete metadata. Without the source, date, and location, the sequence data has limited value for surveillance. The laboratory should ensure that the metadata is complete and accurate before submission.

Poor Quality Control

The quality of the sequence data depends on the quality of the DNA and the sequencing process. Poor quality control can lead to data that is not reliable. The laboratory should follow the quality control procedures and record the results.

Lack of Phenotypic Confirmation

The genomic data should be compared with the phenotypic data. The laboratory should not rely on genomic data alone for clinical decisions. The phenotypic data should be used to confirm the resistance profile.

Inadequate Bioinformatics Capacity

The analysis of sequence data requires bioinformatics capacity. The laboratory should have the tools and the expertise to analyze the data. The lack of bioinformatics capacity is a common limitation for laboratories that are new to sequencing.

Limitations of Genomic Surveillance

Geographic and Pathogen Coverage

The global surveillance data is not evenly distributed. Some countries have extensive sequencing capacity, while others have limited or no capacity. This means that the global data is not representative of the global population. The data gaps are particularly significant in low- and middle-income countries.

Data Standards and Interoperability

The data from different laboratories is not always comparable. The differences in sequencing platforms, analysis methods, and metadata standards can make it difficult to compare the data. The global initiatives are working to standardize the data, but the progress is slow.

Cost and Infrastructure

The cost of sequencing and the infrastructure required for the sequencing are significant barriers. The sequencing requires specialized equipment, reagents, and bioinformatics capacity. The cost of the sequencing is decreasing, but it is still a barrier for many laboratories.

Biosafety and Containment

The sequencing of bacterial isolates requires biosafety and containment procedures. The laboratory should follow the biosafety guidelines for the handling of the isolates and the DNA. The CDC and NIH provide biosafety guidelines for the handling of the biological materials [CDC]. The laboratory should follow these guidelines to ensure the safety of the personnel and the environment.

Biosafety and Regulatory Context

The laboratory should follow the biosafety guidelines for the handling of the biological materials. The CDC and NIH provide the biosafety guidelines for the handling of the biological materials [CDC]. The guidelines cover the risk assessment, the containment, and the decontamination of the biological materials.

The laboratory should also follow the regulatory requirements for the handling of the biological materials. The regulatory requirements vary by country and by the type of the biological material. The laboratory should be aware of the regulatory requirements and should follow them.

The American Society for Microbiology provides the professional education and the laboratory practice for the microbiologists [ASM]. The ASM supports the workforce that performs the surveillance and the testing.

Professional Escalation Criteria

The laboratory should escalate the results to the relevant stakeholders when the results indicate a potential public health threat. The escalation criteria include the identification of a new resistance mechanism, the detection of an outbreak, or the identification of a highly resistant organism.

The laboratory should also escalate the results when the data quality is not sufficient for the surveillance. The escalation should include the limitations of the data and the recommendations for the improvement.

Building a Laboratory Data Quality and Interoperability Framework for Genomic AMR Surveillance

The Interoperability Problem in Practice

Public health laboratories that generate whole-genome sequencing data face a problem that is distinct from the technical challenge of sequencing itself. The problem is ensuring that the data they produce can be compared with data from other laboratories, aggregated into global databases, and interpreted correctly by stakeholders who did not generate the data. This is the interoperability problem, and it is the most common reason why sequencing data fails to contribute to global AMR surveillance.

The WHO describes antimicrobial resistance surveillance as a coordinated effort that requires standardized data collection across human health, animal health, and environmental sectors [<a href="#ref-2">2</a>]. When a laboratory sequences an isolate and submits the data to a public database, the value of that submission depends on whether other laboratories can understand and use it. A sequence file without standardized metadata is like a clinical record without a patient identifier. It exists, but it cannot be connected to anything else.

The practical consequence of poor interoperability is that global databases contain sequences that cannot be compared. Two laboratories may sequence the same bacterial strain, but if they use different metadata fields, different naming conventions, or different quality thresholds, the data cannot be reliably combined. The result is a fragmented surveillance picture that undermines the purpose of global coordination.

This section provides a practical framework for laboratories that want to build a data collection and interoperability system that supports global AMR surveillance. The framework covers the decisions that laboratories must make before sequencing, the records they must keep, the quality checks they must perform, and the troubleshooting steps they can use when data does not meet submission standards.

The Four Interoperability Layers

Interoperability in genomic surveillance operates at four distinct layers. Each layer must be addressed for data to be useful beyond the generating laboratory.

The first layer is technical interoperability. This refers to the file formats and data structures used to store sequence data and metadata. The laboratory must use file formats that are accepted by the target database. The sequence data must be in a format that can be read by standard bioinformatics tools. The metadata must be in a structured format that can be parsed by the database.

The second layer is semantic interoperability. This refers to the meaning of the terms used in the metadata. Two laboratories may both record the source of an isolate, but one may use the term blood and the other may use the term bloodstream. These terms may mean the same thing, but the database cannot know that unless the terms are standardized. Semantic interoperability requires the use of controlled vocabularies and standardized field names.

The third layer is analytical interoperability. This refers to the methods used to analyze the sequence data. Two laboratories may both identify resistance genes, but they may use different databases or different thresholds for calling a gene present. The results may differ even when the sequence data is identical. Analytical interoperability requires the use of validated tools and the documentation of analysis parameters.

The fourth layer is interpretive interoperability. This refers to the meaning of the results in a public health context. A resistance gene that is present in a sequence may or may not produce a resistant phenotype. The interpretation of the genomic data must be consistent with the phenotypic data and with the clinical context. Interpretive interoperability requires the integration of genomic and phenotypic data.

The framework below addresses all four layers. It is designed for laboratories that are new to genomic surveillance and for laboratories that have sequencing capacity but have not yet established a formal data management system.

Step 1: Define the Data Elements Before Sequencing

The first step in building a data collection framework is to define the data elements that will be collected for each isolate. This definition should happen before the sequencing begins, not after. The data elements should be recorded in a standardized format that can be submitted to the target database.

The minimum data elements for genomic AMR surveillance include the isolate identifier, the source of the isolate, the collection date, the geographic location, and the submitting laboratory. The isolate identifier must be unique and must be consistent across all records for that isolate. The source should describe the clinical or environmental origin of the isolate. The collection date should be recorded in a standard format. The geographic location should include the country and the administrative region where the isolate was collected.

The laboratory should also record the phenotypic susceptibility data for the isolate. This data is essential for interpreting the genomic data. The phenotypic data should be recorded using the standard methods and the standard terminology defined by the Clinical and Laboratory Standards Institute [<a href="#ref-1">1</a>]. The laboratory should record the drugs that were tested, the results, and the method used.

The laboratory should record the sequencing data, including the sequencing platform, the library preparation method, and the sequencing depth. This information is needed to assess the quality of the data and to compare the data with data from other laboratories.

The data elements should be recorded in a standardized format. The laboratory should use a data dictionary that defines each field, the allowed values, and the format. The data dictionary should be reviewed and updated as the surveillance program evolves.

Step 2: Use Authenticated Reference Materials for Quality Control

The quality of the sequencing data depends on the quality of the laboratory processes. The laboratory should use authenticated reference materials to validate the sequencing process and to ensure that the data is reliable.

The American Type Culture Collection provides authenticated bacterial strains that are used for quality control in microbiology laboratories [ATCC]. These strains have known resistance profiles and known genome sequences. The laboratory should sequence these strains on a regular basis to validate the sequencing process and to ensure that the results are reproducible.

The reference strains should be sequenced using the same protocols as the surveillance isolates. The results should be compared with the known genome sequence of the strain. If the results do not match, the laboratory should investigate the cause and correct the problem before sequencing additional isolates.

The reference strains should also be used to validate the bioinformatics analysis. The analysis tools should be tested on the reference data to ensure that they produce the expected results. The results should be recorded and reviewed on a regular basis.

Step 3: Establish a Quality Control Checklist

The laboratory should establish a quality control checklist that is used for every isolate. The checklist should be completed before the data is submitted to a public database. The checklist should include the following items.

The first item is the completeness of the metadata. The laboratory should verify that all required fields are filled in and that the values are in the correct format. The laboratory should verify that the isolate identifier is unique and that the source, date, and location are recorded.

The second item is the quality of the sequence data. The laboratory should verify that the sequencing depth is sufficient for the analysis. The laboratory should verify that the sequence assembly is complete and that the quality metrics are within the acceptable range.

The third item is the quality of the analysis. The laboratory should verify that the analysis tools were run with the correct parameters and that the results are consistent with the expected results. The laboratory should verify that the resistance genes identified in the genomic data are consistent with the phenotypic data.

The fourth item is the completeness of the submission. The laboratory should verify that the sequence data and the metadata are submitted together and that the submission is complete.

The checklist should be completed by the person who performed the analysis and reviewed by a second person. The completed checklist should be stored with the data records.

Step 4: Submit Data With Complete Metadata

The submission of the data to a public database is the final step in the data collection process. The submission should include the sequence data and the metadata. The metadata should be complete and should be in the format required by the database.

The NCBI Pathogen Detection system accepts sequence data from submitters worldwide. The system requires the submitter to provide metadata about the isolate, including the source, the collection date, and the geographic location. The system uses this metadata to analyze the data and to cluster related isolates.

The laboratory should verify that the metadata is complete before the submission. The laboratory should also verify that the sequence data is in the correct format and that the data is of sufficient quality.

The submission should be reviewed by a second person before it is finalized. The review should verify that the metadata is correct and that the sequence data is complete.

Records and Measurements for the Data Framework

The laboratory should maintain records of the data collection process. The records should include the data dictionary, the quality control checklist, the reference strain results, and the submission records.

The data dictionary should be maintained and updated as the surveillance program evolves. The dictionary should be reviewed on a regular basis to ensure that the fields are still relevant and that the values are still correct.

The quality control records should be stored with the data records. The records should include the results of the reference strain sequencing and the results of the quality control checks. The records should be available for review.

The submission records should include the date of the submission, the database, and the accession numbers. The records should be used to track the data that has been submitted and to identify any gaps in the submission.

Common Failure Patterns in Data Collection

The most common failure in the data collection framework is the incomplete metadata. The laboratory may sequence the isolate but fail to record the source, the date, or the location. The data is then not useful for surveillance.

The second common failure is the inconsistent metadata. The laboratory may use different names for the same field in different records. The data is then difficult to compare and analyze.

The third common failure is the lack of quality control. The laboratory may not use reference strains or may not verify the quality of the sequence data. The data may then be unreliable.

The fourth common failure is the lack of phenotypic data. The laboratory may sequence the isolate but not record the phenotypic susceptibility data. The genomic data cannot then be interpreted in the context of the clinical resistance.

The fifth common failure is the lack of a data dictionary. The laboratory may not have a standardized format for the metadata. The data is then inconsistent and difficult to use.

Troubleshooting the Data Collection Framework

When the data does not meet the submission standards, the laboratory should use a systematic troubleshooting process. The process should identify the cause of the problem and the corrective action.

The first step is to review the metadata. The laboratory should verify that all required fields are filled in and that the values are in the correct format. If the metadata is incomplete, the laboratory should correct the records before the submission.

The second step is to review the sequence quality. The laboratory should verify that the sequencing depth is sufficient and that the assembly is complete. If the quality is poor, the laboratory should repeat the sequencing.

The third step is to review the analysis. The laboratory should verify that the analysis tools were run with the correct parameters. If the analysis is incorrect, the laboratory should rerun the analysis.

The fourth step is to review the submission. The laboratory should verify that the sequence data and the metadata were submitted together and that the submission is complete.

Professional Escalation Criteria

The laboratory should escalate the data quality issues to the relevant stakeholders when the issues cannot be resolved within the laboratory. The escalation should include the description of the problem, the cause, and the recommended action.

The laboratory should escalate when the data quality is not sufficient for the surveillance. The escalation should include the limitations of the data and the recommendations for the improvement.

The laboratory should also escalate when the data is not interoperable with the data from other laboratories. The escalation should include the description of the interoperability problem and the recommendations for the standardization.

Biosafety and Data Management

The data collection framework should be integrated with the biosafety procedures of the laboratory. The CDC and NIH provide the biosafety guidelines for the handling of the biological materials [CDC]. The laboratory should follow these guidelines for the handling of the isolates and the DNA.

The data management should also follow the regulatory requirements for the handling of the biological materials. The regulatory requirements vary by country and by the type of the biological material. The laboratory should be aware of the regulatory requirements and should follow them.

The American Society for Microbiology provides the professional education and the laboratory practice for the microbiologists [ASM]. The ASM supports the workforce that performs the surveillance and the testing. The laboratory should use the ASM resources to train the staff on the data management and the quality control.

Comparison of Data Management Approaches

The laboratory has two main options for the data management: the manual data management and the automated data management.

The manual data management uses spreadsheets and paper records. The laboratory records the metadata in a spreadsheet and the quality control records on paper. The manual approach is simple and does not require specialized software. The limitation is that the manual approach is prone to errors and is difficult to scale.

The automated data management uses a laboratory information management system. The system records the metadata, the quality control records, and the submission records in a centralized database. The automated approach is more reliable and easier to scale. The limitation is the cost of the system and the need for the training.

The choice of the approach should be based on the size of the laboratory and the volume of the data. The laboratory should start with the manual approach and move to the automated approach as the volume of the data grows.

The Role of the Data Collection Framework in Global Surveillance

The data collection framework is the foundation of the global AMR surveillance. The framework ensures that the data is complete, consistent, and comparable. The framework also ensures that the data is submitted to the public databases and that the data is used for the public health decisions.

The WHO describes the AMR surveillance as a core component of the global response to the antimicrobial resistance [WHO]. The data collection framework is the operational mechanism for the surveillance at the laboratory level. The framework ensures that the data from the laboratory contributes to the global effort.

The framework also supports the workforce that performs the surveillance. The American Society for Microbiology provides the professional education and the laboratory practice for the microbiologists [ASM]. The framework provides the structure for the workforce to perform the surveillance and to contribute to the global effort.

The laboratory should use the framework to build the data collection system. The framework should be reviewed and updated on a regular basis. The laboratory should also use the framework to train the workforce and to ensure that the data is reliable and interoperable.

Frequently Asked Questions

What is the difference between whole-genome sequencing and traditional antimicrobial susceptibility testing?

Whole-genome sequencing reads the complete DNA sequence of a bacterial isolate and identifies resistance genes and genetic relationships. Traditional antimicrobial susceptibility testing measures whether the isolate grows in the presence of antibiotics. The two methods are complementary, and the genomic data should be interpreted in the context of the phenotypic data.

How does whole-genome sequencing support outbreak detection?

Whole-genome sequencing compares the genomes of isolates from different patients or locations. Closely related isolates are likely to share a common source. This information supports the identification of outbreaks and the tracing of transmission patterns.

What is the role of the WHO in global AMR surveillance?

The WHO coordinates global AMR surveillance through the Global Antimicrobial Resistance and Use Surveillance System, which collects standardized data from participating countries. The WHO also provides guidance on the establishment of national surveillance systems and the development of national action plans.

What is the NCBI Pathogen Detection system?

The NCBI Pathogen Detection system is a public database that aggregates bacterial whole-genome sequences from submitters worldwide. The system automatically analyzes the sequences for resistance genes and identifies related isolates to support outbreak detection.

What are the limitations of genomic surveillance?

The limitations include the uneven geographic and pathogen coverage, the cost of sequencing, the need for bioinformatics capacity, and the need for phenotypic confirmation. The data gaps are particularly significant in low- and middle-income countries.

What is the role of the CLSI in AMR surveillance?

The CLSI publishes standards for antimicrobial susceptibility testing. These standards define the methods and the quality control procedures that laboratories should use to produce reliable results. The standards provide the basis for comparing results across laboratories.

What is the role of the ATCC in AMR surveillance?

The ATCC provides authenticated bacterial materials for quality control. These materials have known resistance profiles and are used to validate testing methods and ensure that the results are reproducible.

What are the biosafety considerations for sequencing?

The laboratory should follow the biosafety guidelines for the handling of the biological materials. The CDC and NIH provide the biosafety guidelines for the handling of the biological materials. The laboratory should follow the guidelines to ensure the safety of the laboratory and the environment.

Using the Evidence

Related Bacteriology Guides

• Antimicrobial Resistance in Livestock-Associated Staphylococcus aureus: Genomic Epidemiology and One Health Implications
• Salmonellosis in Poultry: Serovar Surveillance, Antimicrobial Resistance, and Egg Safety
• Avian Influenza (H5N1): Global Spread, Clinical Manifestations, and One Health Surveillance
• Biological Foundation Models for Antimicrobial Peptide Discovery in Veterinary Pathogens
• Avian Influenza: Global Surveillance and Pandemic Preparedness

References and Further Reading

References

This article is educational and does not replace validated laboratory procedures, clinical diagnosis, institutional biosafety review, or public-health guidance.