Best Practices for Data Sharing in Microbial Genomics

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-21

Quick Answer

• Select a public repository that matches your data type and funder requirements, then prepare metadata that describes strain origin, collection date, and isolation source before upload.
• Follow the repository's minimum submission standards and include assembly quality metrics so that other researchers can assess data reliability.
• Recognize that sharing raw sequence data does not replace the need for ethical review, pathogen risk assessment, and compliance with applicable access policies.

At a Glance

Why Data Sharing Matters in Microbial Genomics

Microbial genomics has shifted from a specialized research activity to a core component of public health surveillance, food safety investigation, and clinical diagnostics. Whole genome sequencing of bacterial isolates enables the most detailed comparison of individual strains currently available, and the two principal approaches for strain discrimination, single nucleotide polymorphism analysis and genomic multi-locus sequence typing, produce concordant results for phylogenetic clustering and complement each other. These methods allow laboratories to determine whether bacterial isolates from different patients, foods, or environments are related, which directly supports outbreak detection and source attribution.

The value of genomic data depends on its accessibility. A sequence deposited in a public database becomes part of a growing comparative framework that benefits all subsequent users. The National Center for Biotechnology Information operates the NCBI Pathogen Detection system, which integrates bacterial pathogen genomic data and provides researchers and public health laboratories with tools for identifying clusters and monitoring antimicrobial resistance determinants. When researchers deposit quality sequences with complete metadata, they contribute to a resource that supports real-time surveillance and retrospective analysis.

Global genomic surveillance efforts during the COVID-19 pandemic demonstrated both the power and the limitations of large-scale sequencing. These efforts provided critical data on the ongoing evolution of the virus to inform best practices in clinical care and public health throughout the pandemic. Effective surveillance strategies generally follow a multidisciplinary pipeline involving clinical sample collection, viral genotyping, metadata linkage, data reporting, and public health responses. The same pipeline logic applies to bacterial pathogens, where each step from isolation to public database deposition must function reliably for the overall system to produce actionable information.

The scientific publishing landscape has moved toward open access, a paradigm that mandates the availability of research outputs such as data, code, materials, and publications. Open access increases reproducibility and allows reuse of these resources. Journals increasingly require data deposition as a condition of publication, and funders often mandate data sharing plans in grant applications. Researchers who understand these requirements and build data sharing into their workflows avoid last-minute compliance problems and gain the practical benefit of increased visibility and citation of their work.

Core Principles of Responsible Data Sharing

Data Quality Begins at the Bench

The quality of shared genomic data depends on decisions made long before sequence upload. Culture collections such as the American Type Culture Collection provide authenticated biological materials and strain information that support reproducibility in microbiology research. Using authenticated reference strains for validation experiments and quality control ensures that comparisons between laboratories rest on a common foundation. When researchers deposit sequences derived from poorly characterized isolates, downstream users cannot distinguish genuine biological variation from technical artifacts.

Laboratory practices directly influence sequence quality. The Bacteriological Analytical Manual from the U.S. Food and Drug Administration describes official methods for food microbiology testing, including isolation and identification procedures that produce reliable starting material for sequencing. Following standardized methods for culture, DNA extraction, and library preparation reduces batch effects and improves the comparability of data generated across different laboratories and time periods.

Metadata Is Half the Value

A genome sequence without context has limited utility. Metadata describes the conditions under which an isolate was collected and processed, and this information determines whether other researchers can use the sequence for meaningful comparisons. Essential metadata fields for bacterial isolates include strain or isolate identifier, host species, geographic location of collection, collection date, isolation source, and any relevant clinical or environmental context.

The absence of standardized metadata creates bottlenecks in data processing and complicates interpretation. Limitations and inconsistencies in clinical and demographic data collection and sharing can slow the compilation and limit the utility of comprehensive datasets. Researchers should record metadata at the time of isolation instead of attempting to reconstruct it months later from laboratory notebooks or memory. A simple spreadsheet maintained alongside the culture collection prevents data loss and ensures that the information needed for repository submission is available when needed.

Access Models Match Data Sensitivity

Not all genomic data should be freely downloadable by anyone. Human clinical isolates may contain information that could identify individuals, and some pathogen sequences raise dual-use concerns. The Global Alliance for Genomics and Health has proposed a data access policy model called registered access to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy enables a range of categories of users to gain access, starting with researchers and clinical care professionals, while facilitating general use and reuse of data within the bounds of consent restrictions and other ethical obligations.

Researchers should consider which access model fits their data before submission. Open access maximizes utility and aligns with funder mandates, but registered access may be appropriate when data involve human subjects or when consent agreements restrict use. The choice of access model should be documented in the data management plan and explained in the associated publication.

Selecting a Repository for Bacterial Genomic Data

Public Sequence Databases

The primary repositories for bacterial genomic data are the international nucleotide sequence databases, which include NCBI GenBank, the European Nucleotide Archive, and the DNA Data Bank of Japan. These databases exchange data daily, so submission to any one of them makes sequences available through all three. For bacterial pathogens, NCBI Pathogen Detection provides an integrated platform that combines sequence data with antimicrobial resistance gene identification and cluster analysis. This system supports public health surveillance by enabling rapid comparison of clinical, environmental, and food-associated isolates.

When selecting a repository, researchers should consider the following factors:

• Whether the repository accepts the data type being generated, such as short reads, assembled genomes, or raw sequencing files
• Whether the repository provides tools for analysis and visualization that add value beyond simple data storage
• Whether the repository meets funder and journal requirements for data accessibility
• Whether the repository has a clear process for updating data when assemblies are improved or metadata corrections are needed

Domain-Specific and Specialized Repositories

Some research communities maintain specialized databases that provide additional context or analysis tools beyond the general nucleotide archives. For example, repositories dedicated to specific pathogen groups may offer curated metadata, standardized typing schemes, or integration with epidemiological data. Researchers working on foodborne pathogens should check whether their target journal or funding agency specifies a particular repository.

The choice between a general and a specialized repository is not always exclusive. Some researchers deposit raw sequence data in the international nucleotide databases and also submit analysis results to a specialized platform. This dual deposition increases discoverability but requires attention to version control so that the different records remain consistent.

What to Submit

A complete genomic data submission includes more than the assembled genome sequence. The following components should be deposited:

• Raw sequencing reads, which allow other researchers to reproduce the assembly and perform alternative analyses
• The assembled genome in FASTA format
• Annotation files when available
• Metadata describing the isolate and its provenance
• Quality metrics for the assembly

Raw reads are particularly important because they preserve information that may be lost during assembly, such as plasmid content or minor variants. Researchers who deposit only assembled genomes limit the ability of others to verify results or apply new analytical methods that require access to primary data.

Preparing Metadata for Submission

Essential Fields for Bacterial Isolates

Repository submission forms typically require specific metadata fields, and preparing these in advance streamlines the submission process. The following fields are commonly required or strongly recommended for bacterial genomic data:

Controlled Vocabularies and Ontologies

Using standardized terms for metadata fields improves data interoperability. Free-text descriptions of isolation sources can be difficult to compare across submissions, while controlled vocabularies enable automated searching and aggregation. Many repositories provide lists of accepted terms for common fields, and researchers should consult these lists before submission.

The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Standardized metadata reduces this problem by ensuring that all submissions describe their data in a common language. When a repository offers a controlled vocabulary, use it even if a free-text description seems more precise.

Linking Metadata to Publications

The connection between a genomic data submission and the associated publication should be explicit. Most journals require a data availability statement that includes accession numbers, and repositories provide mechanisms for linking submissions to publications. Researchers should include accession numbers in the manuscript before submission and update the repository record when the paper is published.

Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants, and the same principle applies to bacterial populations. Metadata that describes sampling strategy helps downstream users understand the limitations of the data. A collection of clinical isolates from a single hospital does not represent the national distribution of a pathogen, and the metadata should make this clear.

Quality Control Before Submission

Assembly Quality Assessment

The reliability of downstream analyses depends on assembly quality. Poor assemblies can produce incorrect phylogenetic placements, false antimicrobial resistance predictions, and misleading typing results. Researchers should assess the following metrics before submission:

• Number of contigs, with fewer contigs generally indicating a more complete assembly
• N50 value, which describes the contig length at which half of the assembled bases are contained
• Total assembly length compared with the expected genome size for the species
• Presence of complete and single-copy core genes
• Coverage depth, which indicates how many times each base was sequenced on average

These metrics should be reported in the submission and in the associated publication. Journals increasingly require assembly quality information as part of the data availability statement, and reviewers may check whether the reported metrics are consistent with the stated sequencing approach.

Contamination Detection

Cross-contamination between samples during DNA extraction or sequencing can produce chimeric assemblies that appear to be novel organisms. Screening assemblies for contamination before submission prevents the dissemination of misleading data. Several computational tools can detect contamination by comparing the GC content distribution, coverage uniformity, and taxonomic composition of the assembly against expected values for the target species.

When contamination is detected, researchers should trace the source before resubmitting. Common causes include reagent contamination, carryover between samples during extraction, and index hopping during multiplexed sequencing. Corrective actions depend on the identified source and may include repeating DNA extraction, resequencing the sample, or using different indexing strategies.

Validation Against Reference Materials

Sequencing runs should include appropriate controls to validate the entire workflow from extraction to bioinformatic analysis. Authenticated reference strains with known genome sequences serve as positive controls, and their sequences should match the expected reference within the tolerance of the sequencing platform. Discrepancies between the control sequence and the expected reference indicate a problem in the workflow that may also affect sample data.

The American Type Culture Collection provides authenticated biological materials that are suitable for this purpose. Using reference strains with documented provenance ensures that quality control results are interpretable and that any problems can be traced to specific steps in the workflow.

The Submission Workflow

Step-by-Step Process for NCBI Pathogen Detection

The submission process for bacterial pathogen genomes follows a defined workflow that researchers should understand before beginning. The following steps describe the general process for submitting to NCBI Pathogen Detection:

1. Prepare the isolate metadata and assembly files according to the repository specifications
2. Create an account with the submission system and begin a new submission
3. Upload the assembled genome in FASTA format along with the metadata table
4. Review the automatically generated quality metrics and correct any flagged issues
5. Submit the record for processing and receive accession numbers
6. Include the accession numbers in the manuscript and data availability statement
7. Update the record if the assembly is improved or metadata corrections are needed

The NCBI Pathogen Detection system integrates submitted data with analysis tools that identify antimicrobial resistance genes and detect clusters of related isolates. This integration means that submissions become part of an active surveillance resource instead of static database records.

Common Submission Errors

Researchers frequently encounter the following problems during submission:

• Metadata fields that do not match the repository's controlled vocabulary
• Assembly files that fail format validation
• Missing required fields such as collection date or geographic location
• Duplicate submissions of the same isolate
• Inconsistent strain identifiers between the metadata and the assembly file

Most of these errors can be avoided by reviewing the repository's submission guide before beginning and by preparing all files in the required format in advance. When errors occur, the repository staff can provide guidance, but resolving issues takes time and delays the availability of the data.

Version Control and Updates

Genome assemblies are not static objects. Improved assembly algorithms, additional sequencing data, or corrected metadata may warrant updating a submitted record. Repositories provide mechanisms for updating records, and researchers should use these when appropriate.

Version control matters for reproducibility. When a record is updated, the repository should preserve the version history so that researchers who used the earlier version can understand what changed. Publications should cite the specific version of the assembly used in the analysis, and the data availability statement should include the accession number and version identifier.

Integrating Data Sharing with Laboratory Workflows

Building Data Management into the Research Process

Data sharing is most effective when it is integrated into the research workflow instead of treated as an afterthought at the time of publication. Researchers should develop a data management plan at the start of a project that addresses the following questions:

• What data will be generated and in what formats?
• Where will the data be deposited and when?
• What metadata will be collected and who is responsible for recording it?
• How will data quality be assessed before submission?
• What access model is appropriate for the data?

A data management plan that is written at the project start and updated as the research progresses prevents the common problem of incomplete metadata and missing quality information at the time of submission.

Laboratory Records and Sample Tracking

The connection between physical samples and sequence data must be maintained throughout the research process. A sample tracking system that links culture collection identifiers to DNA extraction records, sequencing runs, and final accession numbers ensures that the provenance of each sequence is documented. This linkage is essential for responding to questions from reviewers, collaborators, or public health authorities about specific isolates.

Laboratory records should include the following information for each isolate:

• Unique identifier and any aliases
• Source and date of isolation
• Storage location and passage history
• DNA extraction method and date
• Sequencing library preparation details
• Sequencing platform and run identifier
• Assembly method and version
• Accession numbers and submission dates

Training and Workforce Development

Effective data sharing requires personnel who understand both the biological and the computational aspects of genomic data. The American Society for Microbiology provides professional microbiology education and laboratory practice resources that support workforce development in this area. Researchers and laboratory professionals should seek training in bioinformatics, data management, and repository submission procedures to build the skills needed for responsible data sharing.

The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Investing in training reduces these bottlenecks by ensuring that multiple team members can perform quality assessment, metadata preparation, and repository submission instead of relying on a single individual.

Antimicrobial Resistance Data and Public Health Surveillance

The Role of Genomic Data in AMR Surveillance

Antimicrobial resistance is a global public health threat that requires coordinated surveillance across clinical, agricultural, and environmental settings. The World Health Organization defines antimicrobial resistance as a condition in which microorganisms change in ways that render medications used to treat infections ineffective, and it has identified AMR as a priority for global action. Genomic data contribute to AMR surveillance by enabling the detection of resistance genes, the tracking of resistant clones, and the identification of transmission pathways.

When researchers share genomic data from resistant isolates, they contribute to a global resource that supports the detection of emerging resistance mechanisms and the monitoring of known resistance determinants. The NCBI Pathogen Detection system integrates antimicrobial resistance gene identification with cluster analysis, allowing public health laboratories to identify resistant isolates that are genetically related and may represent outbreaks.

Standards-Based Susceptibility Context

Genomic prediction of antimicrobial resistance complements phenotypic susceptibility testing, but the two approaches serve different purposes. The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing that define methods and interpretive criteria for phenotypic testing. Genomic data can identify resistance genes, but phenotypic testing remains the reference method for determining whether an isolate is susceptible or resistant to specific antimicrobial agents.

Researchers who share genomic data from isolates with known phenotypic resistance profiles should include the susceptibility data in the metadata when possible. This linkage between genotype and phenotype strengthens the evidence base for using genomic data in clinical and public health decision-making.

Data Sharing for Outbreak Response

During outbreak investigations, timely data sharing can directly inform public health responses. Gaps and delays in the implementation of genomic surveillance data in the public health sphere can prevent officials from formulating effective mitigation strategies to prevent outbreaks. Researchers who generate genomic data during an outbreak should prioritize rapid deposition of quality data with complete metadata to support real-time analysis.

The balance between speed and quality is important. Rapidly deposited data with incomplete metadata may be less useful than slightly delayed data with complete provenance information. Researchers should establish streamlined workflows for outbreak response that maintain quality standards while minimizing delays.

Biosafety and Biosecurity Considerations

Risk Assessment for Shared Data

Sharing genomic data from pathogenic microorganisms requires consideration of biosafety and biosecurity. The Biosafety in Microbiological and Biomedical Laboratories publication from the CDC and NIH provides principles for risk assessment, containment, and decontamination that apply to laboratory work with pathogenic microorganisms. Researchers should conduct a risk assessment for their isolates before sharing genomic data and consider whether the data could pose risks if misused.

The risk assessment should consider the pathogenicity of the organism, the presence of virulence factors or antimicrobial resistance genes, and the potential for the data to enable harmful applications. Most genomic data from bacterial pathogens can be shared openly, but some data may require restricted access or additional review.

Responsible Communication of Findings

The way genomic data are described in publications and presentations affects how the data are interpreted and used. Researchers should avoid overstating the implications of genomic findings and should clearly describe the limitations of their data. For example, the presence of an antimicrobial resistance gene in a genome does not necessarily mean that the isolate is phenotypically resistant, and genomic relatedness does not prove epidemiological linkage.

Responsible communication also involves considering the potential consequences of publishing data that could be misused. Researchers should consult institutional biosafety committees or other relevant authorities when they have concerns about the dual-use implications of their data.

Decontamination and Waste Disposal

Laboratory practices that generate genomic data must follow established biosafety procedures for handling and decontaminating biological materials. The Biosafety in Microbiological and Biomedical Laboratories publication provides guidance on decontamination methods and waste disposal that apply to cultures, DNA extracts, and other laboratory materials. Following these procedures protects laboratory workers and prevents environmental release of pathogenic microorganisms.

Common Failure Patterns in Data Sharing

Incomplete Metadata at Submission Time

The most common failure in genomic data sharing is incomplete metadata. Researchers who do not record collection date, geographic location, or isolation source at the time of sample collection often cannot recover this information later. The result is a sequence record that has limited value for epidemiological analysis and may not meet repository requirements.

Prevention requires a change in laboratory practice. Metadata should be recorded when the sample is collected, not when the submission is prepared. A standardized metadata form that is completed at the time of isolation prevents data loss and ensures that all required fields are captured.

Delayed Submission Until Publication

Many researchers delay data submission until a manuscript is accepted for publication. This practice creates a bottleneck that can delay publication and reduces the availability of data for ongoing surveillance. Journals increasingly require data availability statements at the time of submission, and reviewers may check whether data have been deposited.

Submitting data before manuscript submission has several advantages. The accession numbers can be included in the manuscript, the data are available for reviewers to examine, and any submission problems are identified before publication deadlines. Researchers should plan to submit data when the manuscript is ready for submission, not after acceptance.

Poor Assembly Quality

Assemblies that are fragmented, contaminated, or incomplete produce misleading results when analyzed by downstream users. Researchers who do not assess assembly quality before submission may unknowingly share data that support incorrect conclusions. The impact of poor assemblies extends beyond the original study because other researchers may use the data for comparative analyses.

Quality assessment should be a standard step in the submission workflow. Researchers should run quality metrics, screen for contamination, and compare the assembly against expected values for the species before submission. When quality problems are identified, the assembly should be improved or the limitations should be documented in the submission.

Ignoring Repository-Specific Requirements

Each repository has specific requirements for file formats, metadata fields, and submission procedures. Researchers who assume that all repositories are the same may encounter unexpected problems during submission. Reviewing the repository's documentation before preparing files saves time and prevents errors.

The NCBI Pathogen Detection system, for example, has specific requirements for metadata formatting and file naming that differ from the requirements for general GenBank submissions. Researchers should consult the relevant documentation for their chosen repository and prepare files accordingly.

Evidence Limitations and Interpretation

What Genomic Data Can and Cannot Show

Genomic data provide information about the genetic content of an organism, but they do not directly measure phenotypic characteristics such as virulence, transmissibility, or antimicrobial susceptibility. The presence of a resistance gene does not guarantee phenotypic resistance, and the absence of known resistance genes does not guarantee susceptibility. Researchers who share genomic data should clearly state these limitations in their publications and data descriptions.

Phylogenetic relationships inferred from genomic data describe genetic relatedness, not epidemiological linkage. Two isolates with nearly identical genomes may or may not be part of the same transmission chain, depending on the genetic diversity of the organism and the sampling context. Researchers should avoid overinterpreting genomic relationships in the absence of epidemiological data.

The Problem of Convenience Sampling

Biases from convenience-based sampling methods can obfuscate the true distribution of circulating variants. Many genomic datasets are derived from samples of convenience, such as clinical isolates from a single institution or environmental samples from accessible locations. These datasets may not represent the true diversity of the organism in the broader population.

Researchers should describe their sampling strategy in the metadata and publication so that downstream users can assess the generalizability of the findings. Data from convenience samples remain valuable, but their limitations must be acknowledged.

The Need for Standardization

The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Different laboratories may use different methods for assembly, typing, and resistance gene identification, making direct comparisons difficult. Researchers should use standardized methods when available and clearly describe their methods when standardization is not possible.

The Clinical and Laboratory Standards Institute provides standards for antimicrobial susceptibility testing that support standardization in phenotypic testing. Similar standardization efforts are needed for bioinformatic analyses to ensure that genomic data from different laboratories are comparable.

Professional Escalation Criteria

When to Consult Repository Staff

Repository staff can provide guidance on submission procedures, metadata requirements, and data format issues. Researchers should contact repository staff when they encounter problems that are not addressed in the documentation, such as:

• Unusual data formats that do not match standard submission templates
• Questions about whether a particular data type is appropriate for the repository
• Technical errors that persist after following the documented procedures
• Questions about updating or correcting previously submitted records

When to Consult Institutional Biosafety or Ethics Committees

Some genomic data raise biosafety or ethical concerns that require institutional review. Researchers should consult their institutional biosafety committee when:

• The organism is classified at a biosafety level that requires special containment
• The data could enable harmful applications or dual-use research of concern
• The isolates were obtained from human subjects and may raise privacy concerns
• The research involves genetically modified organisms that require additional oversight

When to Seek Specialized Bioinformatics Support

Researchers who lack the bioinformatics expertise to assess assembly quality or interpret genomic data should seek support from collaborators, core facilities, or specialized consultants. The lack of standardization in genotyping strategies and bioinformatic expertise can create bottlenecks in data processing and complicate interpretation. Investing in bioinformatics support improves data quality and reduces the risk of sharing misleading results.

Records and Measurements for Data Sharing Programs

Tracking Submission Metrics

Laboratories that regularly share genomic data should track submission metrics to identify trends and improve workflows. Useful metrics include:

• Number of submissions per month or quarter
• Time from sample collection to data submission
• Proportion of submissions that require corrections or resubmission
• Common metadata errors and their frequency
• Time from submission to accession number assignment

These metrics can identify bottlenecks in the workflow and guide improvements in training, documentation, or laboratory practices.

Maintaining a Data Inventory

A data inventory that lists all genomic datasets generated by the laboratory provides a basis for tracking submissions and ensuring compliance with funder and journal requirements. The inventory should include the project name, isolate identifiers, sequencing date, submission status, accession numbers, and associated publications.

The inventory should be reviewed regularly to identify datasets that have not been submitted and to ensure that all published data are accessible. This review is particularly important when personnel change or when projects end, as data may otherwise be lost.

Documentation for Audits and Compliance

Funding agencies and journals may request documentation of data sharing practices during audits or compliance reviews. Laboratories should maintain records that demonstrate:

• The data management plan for each project
• The metadata collected for each isolate
• The quality assessment results for each assembly
• The submission records and accession numbers
• The data availability statements in associated publications

These records should be retained for the duration required by the funding agency or institution, which may extend beyond the end of the research project.

Frequently Asked Questions

What is the difference between raw reads and assembled genomes for data sharing?

Raw sequencing reads are the primary output of the sequencing instrument, while assembled genomes are the contigs or scaffolds produced by assembling those reads. Raw reads preserve all the information from the sequencing run and allow other researchers to reproduce the assembly or apply alternative methods. Assembled genomes are smaller and easier to analyze but may have lost information during the assembly process. Most repositories accept both, and depositing both provides the greatest value to the research community.

How do I choose between open access and registered access for my genomic data?

Open access makes data freely available to anyone, which maximizes utility and aligns with most funder mandates. Registered access requires users to agree to terms and conditions before accessing data, which is appropriate when data involve human subjects, consent restrictions, or dual-use concerns. The Global Alliance for Genomics and Health has proposed registered access as a model that enables broader access while respecting ethical obligations. Consider the sensitivity of your data and the requirements of your funder and journal when making this choice.

What metadata fields are required for bacterial genome submissions?

Required metadata fields vary by repository, but common fields include strain or isolate identifier, organism name, collection date, geographic location, and isolation source. The NCBI Pathogen Detection system and other repositories provide specific lists of required and recommended fields. Recording metadata at the time of sample collection prevents data loss and ensures that all required information is available at submission.

How do I assess the quality of my genome assembly before submission?

Assembly quality is assessed using metrics such as the number of contigs, the N50 value, total assembly length compared with the expected genome size, and the presence of complete core genes. Coverage depth indicates how thoroughly the genome was sequenced. Screening for contamination by examining GC content distribution and taxonomic composition is also important. These metrics should be reported in the submission and in the associated publication.

Can genomic data predict antimicrobial resistance without phenotypic testing?

Genomic data can identify antimicrobial resistance genes, but the presence of a resistance gene does not guarantee phenotypic resistance. The Clinical and Laboratory Standards Institute provides standards for phenotypic antimicrobial susceptibility testing that remain the reference method for determining susceptibility. Genomic prediction of resistance is a useful screening tool, but phenotypic testing is needed for clinical decision-making.

What should I do if I discover an error in a submitted genome record?

Repositories provide mechanisms for updating records when errors are identified. Contact the repository staff to request a correction or update, and provide the corrected data and a description of the change. The repository should preserve the version history so that researchers who used the earlier version can understand what changed. Update the data availability statement in any associated publications to reflect the corrected accession number or version.

How does data sharing support antimicrobial resistance surveillance?

Data sharing enables the aggregation of genomic data from multiple laboratories, which supports the detection of emerging resistance mechanisms and the tracking of resistant clones. The NCBI Pathogen Detection system integrates antimicrobial resistance gene identification with cluster analysis, allowing public health laboratories to identify related resistant isolates. The World Health Organization has identified antimicrobial resistance as a global priority, and genomic surveillance depends on broad data sharing.

What are the biosafety considerations for sharing genomic data from pathogens?

Sharing genomic data from pathogenic microorganisms requires consideration of biosafety and biosecurity. The Biosafety in Microbiological and Biomedical Laboratories publication from the CDC and NIH provides principles for risk assessment and containment. Researchers should conduct a risk assessment for their isolates and consider whether the data could pose risks if misused. Most bacterial genomic data can be shared openly, but some data may require restricted access or institutional review.

Related Bacteriology Guides

• Food Safety Quiz: Poultry Preparation and Microbiology
• Avian Influenza: CDC Guidelines and Poultry Pandemic Preparedness
• Cooking Chicken to Kill Bacteria: Food Safety Temperatures and Practices
• Avian Influenza in Poultry: Clinical Signs, Surveillance, and CDC Guidelines
• Poultry Salmonella and Food Safety: FSIS Guidelines and Public Health

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.
• Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era.. Viruses, 2022.
• Open Access and Reproducibility in Plant Pathology Research: Guidelines and Best Practices.. Phytopathology, 2024.
• Registered access: authorizing data access.. European journal of human genetics : EJHG, 2018.
• The use of next generation sequencing for improving food safety: Translation into practice.. Food microbiology, 2019.

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