The Role of Public Databases in Genomic Surveillance of Bacterial Pathogens

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

Quick Answer

• Public genomic databases such as NCBI, ENA, and BV-BRC store pathogen genome sequences and metadata that enable researchers to track bacterial spread, identify resistance markers, and compare isolates across time and geography.
• The practical next step for a laboratory or research group is to select one primary repository, learn its submission and query tools, and establish a routine for depositing and retrieving sequence data.
• A key limitation is that database utility depends on the quality and completeness of submitted metadata, so inconsistent or sparse records reduce the reliability of surveillance conclusions.

At a Glance

Introduction to Public Databases in Bacterial Genomic Surveillance

Bacterial genomic surveillance depends on the ability to compare sequence data from many isolates across laboratories, regions, and time periods. Public databases provide the shared infrastructure that makes such comparisons possible. They store raw sequencing reads, assembled genomes, and associated metadata, and they offer tools for searching, analyzing, and retrieving data. For researchers and laboratory professionals, understanding how these databases operate and how to use them effectively is a core skill in modern pathogen surveillance.

The global context for this work is the growing concern about antimicrobial resistance. The World Health Organization identifies antimicrobial resistance as a threat to public health that requires coordinated surveillance and stewardship efforts. Genomic surveillance is one of the tools used to track resistant pathogens and understand their spread. Public databases support this effort by making sequence data available to researchers and public health agencies worldwide.

The practical value of these databases lies in their ability to connect a laboratory result to a broader picture. A single bacterial isolate sequenced in one laboratory can be compared to thousands of other isolates from other laboratories. This comparison can reveal whether a strain is part of a larger outbreak, whether it carries specific resistance genes, and how it relates to strains from other countries. Without public databases, each laboratory would work in isolation and the ability to detect and respond to emerging threats would be severely limited.

Core Principles of Public Genomic Databases

Public genomic databases operate on a set of principles that make them useful for surveillance. These principles include open access, data standardization, and the use of accession numbers as unique identifiers.

Open Access and Data Sharing

The primary principle is open access. Researchers deposit sequence data into public databases so that others can use it. This sharing model allows for the accumulation of large datasets that would be impossible for any single laboratory to generate. The databases are maintained by international organizations and national agencies, and they are designed to be freely accessible to the scientific community.

Data Standardization and Metadata

For data to be useful, it must be accompanied by metadata. Metadata includes information about the isolate, such as the source, the date of collection, the geographic location, and the laboratory that performed the sequencing. Standardized metadata allows researchers to filter and compare data effectively. The quality of the metadata is a major factor in the utility of the database. Incomplete or inconsistent metadata can limit the ability to draw conclusions from the data.

Stable Identifiers

Each record in a public database is assigned a stable identifier, such as an accession number. These identifiers allow researchers to cite specific data and to link data across different databases. The stability of these identifiers is critical for reproducibility and for tracking the history of a particular sequence.

The Role of NCBI in Genomic Surveillance

The National Center for Biotechnology Information (NCBI) is a major repository for genomic data. It provides a range of databases and tools that are used in bacterial surveillance.

GenBank and the Sequence Read Archive

GenBank is the primary database for assembled genome sequences. It contains annotated sequences that have been submitted by researchers and are available for download. The Sequence Read Archive (SRA) stores raw sequencing data, which is the unprocessed output of sequencing machines. The SRA is important for researchers who need to reanalyze raw data or who need to verify the quality of assembled genomes.

NCBI Tools for Surveillance

NCBI provides several tools that are used in surveillance. The Pathogen Detection system is a key resource. It integrates data from multiple sources and provides a platform for identifying and tracking bacterial pathogens. The system uses a set of algorithms to cluster related isolates and to identify potential outbreaks. It also provides a way to search for specific genes, including antimicrobial resistance genes.

The Role of NCBI in Outbreak Detection

NCBI's Pathogen Detection system is used by public health laboratories to identify and track outbreaks. The system compares the sequences of isolates to a large reference database and identifies clusters of related isolates. This allows researchers to see whether a particular strain is spreading and to identify the source of an outbreak. The system is designed to be used by laboratories that have limited bioinformatics expertise, making it accessible to a wide range of users.

The European Nucleotide Archive in Global Surveillance

The European Nucleotide Archive (ENA) is a major repository for nucleotide sequence data. It is maintained by the European Bioinformatics Institute (EBI) and provides a comprehensive collection of raw and assembled data.

Data Types and Submission

ENA accepts a wide range of data types, including raw reads, assembled genomes, and annotated sequences. The archive is designed to be a permanent record of the data, and it provides tools for submitting and retrieving data. The submission process is designed to be flexible, allowing researchers to submit data in a variety of formats.

ENA Tools and Access

ENA provides a web-based interface for searching and retrieving data. The REST API allows programmatic access to the data, which is useful for researchers who need to integrate data into their own workflows. The archive also provides a set of tools for analyzing data, including a genome browser and a set of search tools.

ENA in Cross-Border Surveillance

ENA is used in cross-border surveillance because it allows researchers from different countries to share and compare data. The archive is a central repository for data from Europe and other regions, and it is used in international surveillance projects. The ability to access data from multiple countries is essential for tracking the spread of pathogens across borders.

BV-BRC and Integrated Analysis

The Bacterial and Viral Bioinformatics Resource Center (BV-BRC) is a database that provides a platform for analyzing bacterial and viral genomes. It integrates data from multiple sources and provides a suite of tools for analysis.

Data Integration and Annotation

BV-BRC integrates data from a variety of sources, including NCBI and ENA. It provides a set of tools for annotating genomes, which includes identifying genes, proteins, and other features. The annotation process is important for understanding the function of a genome and for identifying resistance and virulence genes.

Tools for Resistance and Virulence Analysis

BV-BRC provides tools for analyzing antimicrobial resistance and virulence. The database includes a collection of resistance genes and a set of tools for identifying these genes in a genome. It also provides tools for analyzing the genetic basis of resistance and for comparing the genomes of different isolates.

BV-BRC for Comparative Genomics

BV-BRC is used for comparative genomics, which is the comparison of genomes from different isolates. This is important for understanding the evolution of pathogens and for identifying the genetic basis of traits such as resistance and virulence. The database provides tools for comparing genomes and for visualizing the results of these comparisons.

Practical Workflow for Using Public Databases

Step 1: Define the Surveillance Question

The first step in using public databases is to define the surveillance question. This could be a question about the spread of a particular strain, the presence of resistance genes, or the relationship between isolates. The question will determine the type of data that is needed and the tools that will be used.

Step 2: Select the Appropriate Database

The choice of database depends on the type of data and the analysis that is needed. For example, if the goal is to compare a new isolate to a large reference set, NCBI's Pathogen Detection may be the most appropriate. If the goal is to access raw data for re-analysis, ENA may be the best choice. If the goal is to analyze resistance genes, BV-BRC may be the most useful.

Step 3: Submit Data

If the laboratory has generated new sequence data, it must be submitted to a public database. The submission process involves preparing the data and metadata, and then uploading it to the database. The database will assign an accession number to the data, which can be used to reference the data in publications and other communications.

Step 4: Retrieve and Analyze Data

Once the data is in the database, it can be retrieved and analyzed. The analysis may involve comparing the data to other data in the database, identifying resistance genes, or constructing a phylogenetic tree. The tools available in the database will determine the type of analysis that can be performed.

Step 5: Interpret and Report

The final step is to interpret the results of the analysis and to report the findings. This may involve writing a report, publishing a paper, or communicating the results to public health authorities. The interpretation should be based on the data and the context of the surveillance question.

Records and Measurements in Genomic Surveillance

Metadata Quality and Completeness

The quality of the metadata is a critical factor in the utility of the data. Metadata should include the source of the isolate, the date of collection, the geographic location, and the laboratory. The completeness of the metadata will affect the ability to filter and search the data. Incomplete metadata can lead to the data being excluded from analyses or to incorrect conclusions.

Data Quality and Validation

The quality of the sequence data is also important. The data should be of high quality, with sufficient coverage and accuracy. The database may provide tools for assessing the quality of the data, and researchers should use these tools to ensure that the data is reliable.

Records and Documentation

The records of the submission and analysis should be documented. This includes the accession numbers, the date of submission, the tools used, and the parameters of the analysis. This documentation is important for reproducibility and for the ability to trace the data.

Common Failure Patterns in Database Use

Incomplete Metadata

One of the most common failures is incomplete metadata. This can occur when the submitter does not provide all the required information or when the information is not accurate. Incomplete metadata can make it difficult to find the data or to interpret the results.

Data Quality Issues

Data quality issues can also arise. This can include low-quality sequences, contamination, or errors in the assembly. These issues can lead to incorrect conclusions if they are not identified and addressed.

Misinterpretation of Results

Misinterpretation of results is another common failure. This can occur when the results of the analysis are not properly interpreted or when the limitations of the data are not considered. For example, a cluster of isolates may be identified, but the cluster may not represent a true outbreak.

Lack of Standardization

The lack of standardization can also be a problem. This can occur when different laboratories use different methods for sequencing or analysis, leading to data that is not directly comparable. The use of standardized methods and tools can help to reduce this problem.

Limitations and Evidence Gaps

Data Quality and Completeness

The quality and completeness of the data in public databases are a major limitation. The data is only as good as the data that is submitted, and the quality can vary widely. This can limit the ability to draw conclusions from the data.

The Need for Standardization

The lack of standardization is another limitation. The use of different methods and tools can make it difficult to compare data from different sources. The development of standard protocols and the use of common data formats can help to address this issue.

The Role of the Database in Surveillance

The database is a tool, and its utility depends on the way it is used. The database is not a substitute for a well-designed surveillance program. The database is a component of a larger system that includes sample collection, laboratory testing, and data analysis.

Safety and Regulatory Context

Biosafety and Biosecurity

The use of public databases has implications for biosafety and biosecurity. The data in the databases can be used to identify and track pathogens, and this information can be used for both beneficial and harmful purposes. The databases are designed to be open, but there are concerns about the potential for misuse of the data.

Laboratory Biosafety

The laboratory work that generates the data must be conducted in accordance with biosafety guidelines. The CDC and NIH provide guidance on biosafety in microbiological and biomedical laboratories. This guidance includes recommendations for the containment of pathogens and the safe handling of biological materials.

Professional Standards

The use of public databases should be conducted in accordance with professional standards. The American Society for Microbiology provides resources for professional microbiology education and laboratory practice. These resources can help to ensure that the data is generated and used in a responsible manner.

Professional Escalation Criteria

When to Seek Expert Help

There are situations where it is appropriate to seek expert help. This includes cases where the data is complex, where the analysis requires specialized tools, or where the results are uncertain. In these cases, it is important to consult with a bioinformatician or a public health expert.

When to Report a Problem

It is also important to report problems with the data or the database. This includes cases where the data is incomplete, where the quality is poor, or where the database is not functioning properly. Reporting these problems can help to improve the quality of the database and the data.

When to Escalate to a Public Health Authority

If the results of the analysis suggest a potential public health threat, it is important to escalate the issue to a public health authority. This includes cases where a cluster of isolates is identified, where a new resistance mechanism is detected, or where the data suggests a potential outbreak.

Building a Practical Quality Control and Validation Framework for Database-Derived Surveillance Data

Public genomic databases provide access to vast amounts of sequence data, but the practical value of that data depends entirely on the quality of the records you retrieve and the rigor of your validation process. A researcher who downloads a set of genomes and proceeds directly to analysis without a structured quality control framework risks drawing conclusions from contaminated assemblies, mislabeled isolates, or incomplete metadata. This section provides a concrete decision framework for assessing data quality before analysis, a record system for tracking validation steps, and troubleshooting methods for common data problems.

The Quality Control Decision Framework

Before any comparative analysis begins, each genome retrieved from a public database should pass through a three stage quality control process. The first stage is assembly quality assessment, the second is metadata consistency checking, and the third is biological plausibility verification. Each stage produces a pass or fail outcome that determines whether the record proceeds to analysis, requires correction, or must be excluded.

Stage One: Assembly Quality Assessment

The assembly quality stage examines the technical characteristics of the genome sequence itself. The key metrics are the number of contigs, the total assembly length, the N50 value, and the depth of coverage. A high quality bacterial genome assembly typically has a small number of contigs, often fewer than 100 for a complete or near-complete assembly, and an assembly length that falls within the expected range for the species. The expected genome size varies by species, so you must compare the assembly length to a reference genome for the same species instead of to a universal standard.

The N50 value is a measure of assembly contiguity. It represents the length of the shortest contig such that the sum of all contigs of that length or longer covers at least half of the assembly. A higher N50 indicates a more contiguous assembly. For surveillance purposes, assemblies with very low N50 values may still be useful for detecting the presence of specific genes, but they are less reliable for structural comparisons or for determining the exact genetic context of a resistance marker.

Coverage is the average number of times each nucleotide position is represented in the raw reads. Low coverage can lead to gaps in the assembly and errors in base calling. Most public databases do not require coverage information to be included in the metadata, but the raw reads in the Sequence Read Archive or the European Nucleotide Archive can be used to calculate coverage if the original sequencing data is available.

The practical approach is to establish minimum thresholds for your specific surveillance question. For a study that only needs to detect the presence of a resistance gene, a draft assembly with many contigs may be acceptable. For a study that needs to determine the genetic environment of a resistance gene or to perform a high resolution phylogenetic comparison, only high quality assemblies should be used. The thresholds should be defined before the data retrieval begins and documented in the analysis protocol.

Stage Two: Metadata Consistency Checking

The second stage of the quality control framework checks whether the metadata associated with each genome record is internally consistent and complete. The critical metadata fields are the organism name, the isolation source, the collection date, the geographic location, and the submitter information.

The organism name should match the expected species for the study. A common problem is a genome record that is labeled with one species name but that clusters with a different species in a phylogenetic analysis. This can occur when the original isolate was misidentified by the submitting laboratory or when the genome was contaminated during sequencing. The organism name should be verified by comparing the genome to a reference set of genomes for the expected species.

The isolation source should be consistent with the surveillance question. If the study is tracking a pathogen in a specific food product, the isolation source field should indicate that the isolate came from that food product. Records with missing or vague isolation sources, such as "clinical" or "environmental" without further detail, may still be useful but should be flagged for careful interpretation.

The collection date is essential for temporal analysis. Records with missing collection dates cannot be used in studies that track the spread of a pathogen over time. The geographic location is essential for spatial analysis. Records with missing or imprecise geographic information cannot be used in studies that map the distribution of a pathogen.

The metadata consistency check should also compare the metadata to the sequence data. For example, if the metadata indicates that the isolate was collected in a specific year, the sequence data should be consistent with that time frame. If the metadata indicates a specific serotype or sequence type, the genome should contain the genes that are expected for that type.

Stage Three: Biological Plausibility Verification

The third stage verifies that the genome sequence is biologically plausible. This stage checks for contamination, for the presence of expected genes, and for the absence of unexpected genes.

Contamination is a common problem in public databases. A genome that contains sequences from two different organisms can occur when the original culture was mixed or when the sequencing process introduced foreign DNA. The presence of contamination can be detected by examining the assembly for an unusually large number of genes or by comparing the genome to a reference set and looking for sequences that do not match the expected species.

The presence of expected genes is a positive control. If the isolate is a specific species, the genome should contain the genes that are characteristic of that species. The absence of an expected gene can indicate a problem with the assembly or with the original identification.

The presence of unexpected genes is also a concern. For example, if a genome is identified as a species that is not known to carry a specific resistance gene, the presence of that gene should be verified by examining the raw reads or by performing a targeted analysis. The unexpected gene may be a genuine finding, but it may also be a result of contamination or a misassembly.

The Record System for Data Validation

A record system is needed to track the quality control process for each genome record. The record should include the accession number, the date of retrieval, the assembly quality metrics, the metadata consistency check results, the biological plausibility check results, and the final decision to include or exclude the record.

The record system can be a simple spreadsheet or a laboratory information management system. The key is that the record is complete and that it can be reviewed by other members of the research team. The record should also include the version of the database that was used and the date of the retrieval, because the data in public databases is updated regularly and a record that was valid on one date may be different on another date.

The record system should also track the tools and parameters used for the quality assessment. If the assembly quality was assessed using a specific tool, the version of the tool and the parameters should be recorded. This documentation is essential for reproducibility and for troubleshooting when a problem is identified later in the analysis.

Troubleshooting Common Data Problems

When a genome record fails one of the quality checks, the researcher must decide whether to troubleshoot the problem or to exclude the record. The troubleshooting approach depends on the nature of the problem.

Problem: The Genome Is Too Large or Too Small

If the assembly length is significantly larger than the expected range for the species, the genome may be contaminated with DNA from another organism. The first step is to check the number of contigs. A genome with a large number of contigs and a large assembly length may be a mixed assembly. The next step is to examine the genes that are present. If the genome contains genes from two different species, the record should be excluded.

If the assembly length is significantly smaller than the expected range, the genome may be incomplete. The coverage of the assembly should be checked. If the coverage is low, the assembly may have gaps. The record can be used for gene detection if the genes of interest are present, but it should not be used for phylogenetic analysis.

Problem: The Metadata Does Not Match the Sequence

If the metadata indicates a species that does not match the sequence, the record may be mislabeled. The first step is to verify the species identification by comparing the genome to a reference set. If the genome is clearly a different species, the record should be excluded from the analysis. The mislabeling should be reported to the database maintainers so that the record can be corrected.

Problem: The Collection Date Is Missing

If the collection date is missing, the record cannot be used for temporal analysis. The record can still be used for other types of analysis, such as the detection of resistance genes, but the limitation should be documented. The record should be flagged in the record system so that it is not accidentally included in a temporal analysis.

Problem: The Isolation Source Is Ambiguous

If the isolation source is ambiguous, the record can be used for analysis that does not depend on the source. The record should be flagged so that the limitation is known. If the surveillance question requires a specific isolation source, the record should be excluded.

Problem: The Genome Contains Unexpected Resistance Genes

If the genome contains a resistance gene that is not expected for the species, the finding should be verified. The raw reads should be examined to confirm the presence of the gene. The gene may be a true finding, but it may also be a result of contamination or a misassembly. The verification process should be documented in the record.

The Role of Reference Materials in Validation

The quality control framework is strengthened by the use of authenticated reference materials. The American Type Culture Collection provides authenticated biological materials, including bacterial strains with known characteristics. These strains can be used as positive and negative controls in the validation process.

A positive control is a strain that is known to contain a specific gene or to have a specific sequence type. The positive control is used to verify that the analysis pipeline can detect the gene or the sequence type. A negative control is a strain that is known not to contain the gene. The negative control is used to verify that the analysis pipeline does not produce false positives.

The use of reference materials is a standard practice in microbiology. The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing, and the use of reference strains is a key component of these standards. The same principle applies to genomic analysis. The reference strains are used to validate the analysis pipeline and to ensure that the results are reliable.

The Record System for Validation

The validation process should be documented in a record system that is separate from the data quality record. The validation record should include the reference strains used, the expected results, the observed results, and the date of the validation. The validation record should be reviewed when the analysis pipeline is changed or when a new tool is introduced.

The validation record is also important for the interpretation of the results. If a new resistance gene is detected in a clinical isolate, the validation record provides the evidence that the detection method is reliable. The validation record is a key component of the evidence base for the surveillance findings.

The Decision to Exclude or Include a Record

The decision to exclude a record should be based on the quality assessment and the surveillance question. The decision should be documented in the record system. The exclusion criteria should be defined before the analysis begins.

The exclusion criteria should be specific. For example, a record may be excluded if the assembly length is more than 20 percent larger than the expected range for the species. A record may be excluded if the collection date is missing and the analysis is temporal. A record may be excluded if the isolation source is ambiguous and the analysis is source specific.

The exclusion criteria should be applied consistently to all records. The application of the criteria should be documented in the record system. The number of records that are excluded should be reported in the analysis results.

The Impact of Quality Control on Surveillance Conclusions

The quality control framework has a direct impact on the conclusions of a surveillance study. A study that includes low quality records may produce false conclusions. A study that excludes too many records may produce a biased conclusion.

The quality control framework is a balance between the need for data and the need for reliability. The framework should be designed to include as many records as possible while maintaining the reliability of the conclusions. The framework should be reviewed and adjusted as the study progresses.

The quality control framework is also a component of the professional standards for microbiology. The American Society for Microbiology provides resources for laboratory practice and professional education. The quality control framework is a component of the responsible use of public databases.

The Practical Implementation of the Framework

The implementation of the quality control framework requires a set of tools and a set of procedures. The tools are the software programs that are used for the quality assessment. The procedures are the steps that are followed for each record.

The tools for the quality assessment are available from the public databases and from other sources. The NCBI provides a set of tools for assessing the quality of the assemblies. The BV-BRC provides a set of tools for the annotation and the analysis. The tools are used in a defined order.

The procedures are documented in a standard operating procedure. The standard operating procedure is a written document that describes the steps for the quality assessment. The standard operating procedure is reviewed and updated as the tools and the databases change.

The implementation of the framework is a team effort. The team includes the researchers who perform the analysis, the bioinformaticians who the tools, and the laboratory professionals who the samples. The team works together to ensure that the quality control framework is applied consistently.

The Limitations of the Quality Control Framework

The quality control framework has limitations. The framework cannot detect all problems. The framework is based on the information that is available in the public databases. The information is not always complete or accurate.

The framework cannot detect a problem that is not visible in the data. For example, the framework cannot detect a mislabeled isolate if the metadata is consistent with the sequence. The framework cannot detect a contamination if the contamination is from a closely related species.

The framework is a tool for reducing the risk of errors. The framework does not eliminate the risk. The researcher should be aware of the limitations and should interpret the results with caution.

The Escalation of Quality Issues

When a quality issue is identified, the researcher should decide whether to escalate the issue. The escalation is appropriate when the issue is a problem with the database or when the issue is a problem with the data that is submitted by another laboratory.

The escalation to the database maintainer is appropriate when the issue is a problem with the database. The database maintainer can correct the record or can provide additional information. The escalation to the database maintainer is a professional responsibility.

The escalation to a public health authority is appropriate when the issue is a problem with the data that is submitted by another laboratory. The public health authority can investigate the issue and can take action to correct the problem. The escalation to a public health authority is a professional responsibility.

The escalation to a bioinformatician is appropriate when the issue is a problem with the analysis. The bioinformatician can provide the expertise to resolve the issue. The escalation to a bioinformatician is a professional responsibility.

The Integration of the Quality Control Framework into the Surveillance Workflow

The quality control framework is integrated into the surveillance workflow. The framework is applied at the beginning of the analysis. The framework is applied before the data is used for the analysis.

The integration of the framework into the workflow ensures that the quality control is a routine part of the analysis. The integration of the framework into the workflow ensures that the quality control is not an afterthought.

The integration of the framework into the workflow is a component of the professional standards for the research. The integration of the framework into the workflow is a component of the responsible use of the public databases.

The Record of the Quality Control Process

The record of the quality control process is a component of the research record. The record is used to document the decisions that are made during the analysis. The record is used to support the conclusions of the analysis.

The record is a component of the reproducibility of the research. The record is used to verify the results of the analysis. The record is used to audit the analysis.

The record is a component of the professional standards for the research. The record is used to demonstrate that the analysis is conducted in a responsible manner.

The Future of the Quality Control Framework

The quality control framework is a component of the evolving field of genomic surveillance. The framework is updated as the tools and the databases evolve. The framework is updated as the standards for the research evolve.

The framework is a component of the professional development of the researcher. The researcher is responsible for the quality of the analysis. The researcher is responsible for the quality control framework.

The framework is a component of the public health infrastructure. The framework is used to ensure that the surveillance data is reliable. The framework is used to ensure that the public health decisions are based on reliable data.

Frequently Asked Questions

What is the difference between NCBI, ENA, and BV-BRC?

NCBI is a US-based database that provides a range of tools for sequence analysis and is a major repository for assembled genomes and raw reads. ENA is a European database that focuses on the archival of raw and assembled data and provides a web-based interface for access. BV-BRC is a specialized database that integrates data from multiple sources and provides tools for analysis, including resistance and virulence analysis.

How do I submit data to a public database?

The submission process varies by database, but it typically involves creating an account, preparing the data in the required format, and uploading the data. The database will provide instructions and may require the data to be validated before it is accepted.

What is the role of metadata in genomic surveillance?

Metadata is the information that describes the data, such as the source, date, and location of the isolate. It is essential for searching and filtering data, and it is a key factor in the utility of the data. Incomplete metadata can limit the ability to use the data.

How can I use public databases to detect an outbreak?

Public databases can be used to detect an outbreak by comparing the sequence of an isolate to other isolates in the database. If the isolate is part of a cluster of isolates that are closely related, it may be part of an outbreak. The tools available in the database can help to identify these clusters.

What are the limitations of public databases?

The main limitations are the quality and completeness of the data, the lack of standardization, and the potential for misinterpretation. The data is only as good as the data that is submitted, and the quality can vary widely.

How do I ensure the quality of my data?

The quality of the data can be ensured by following standard protocols for sequencing and analysis, and by using the validation tools provided by the database. The data should be checked for quality before it is submitted.

What is the role of the database in antimicrobial resistance surveillance?

The database is a tool for tracking the spread of resistant pathogens. It allows researchers to identify resistance genes and to track the spread of resistant strains. This information is used to inform public health decisions.

How do I cite a database in a publication?

The database should be cited by the accession number of the data and the database name. The citation should include the date of access and the URL of the database.

Using the Evidence

Related Bacteriology Guides

• Bacterial Pathogens in Poultry: Prevalence, Public Health Risks, and Control Strategies
• Bacterial Carriage in Ducks: Pathogens of Public Health Concern
• Bacterial Pathogens in Chicken Meat: Risk Assessment and Public Health Impact
• Chicken Bacteria Food Poisoning: Pathogens, Clinical Syndromes, and Public Health Implications
• Salmonellosis and Colibacillosis in Poultry: Bacterial Pathogens, Public Health Implications, and Control Strategies

References and Further Reading

• Biosafety in Microbiological and Biomedical Laboratories. CDC and NIH.
• Microbiology Resources. American Society for Microbiology.
• Antimicrobial Resistance. World Health Organization.
• Bacteriological Analytical Manual. U.S. Food and Drug Administration.
• Antimicrobial Susceptibility Testing. Clinical and Laboratory Standards Institute.
• Culture Collections. American Type Culture Collection.
• NCBI Pathogen Detection. National Center for Biotechnology Information.
• Expansion and transmission dynamics of high risk carbapenem-resistant Klebsiella pneumoniae subclones in China: An epidemiological, spatial, genomic analysis.. Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy, 2024.
• CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database.. Nucleic acids research, 2020.
• The transferability and evolution of NDM-1 and KPC-2 co-producing Klebsiella pneumoniae from clinical settings.. EBioMedicine, 2020.
• Genome-scale evolution and phylodynamics of swine influenza A viruses in China: a genomic epidemiology study.. The Lancet. Microbe, 2025.

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