MLST vs. cgMLST vs. SNP Typing

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

Quick Answer

• Select 7-gene MLST for species identification and broad lineage surveillance, cgMLST for outbreak detection and source tracking, and SNP typing for fine-scale transmission and evolutionary analysis.
• Match resolution to the epidemiological question, since MLST cannot separate outbreak isolates that cgMLST and SNP typing can distinguish.
• All methods depend on sequence quality and bioinformatics choices, and inter-laboratory variability can change cluster assignments even with identical raw data.

At a Glance

Bacterial Typing Methods and Their Purpose

Bacterial typing characterizes isolates to determine genetic relatedness. Public health laboratories, food safety investigators, and clinical researchers use typing to identify outbreak sources, track transmission, and understand population structure. The three methods compared here operate at different resolutions and answer different epidemiological questions.

Traditional 7-gene multilocus sequence typing (MLST) examines internal fragments of seven housekeeping genes. Housekeeping genes are conserved because they perform essential cellular functions, so they accumulate mutations slowly. This slow mutation rate makes MLST useful for identifying species and broad lineages but limits its ability to distinguish isolates from the same outbreak.

Core genome MLST (cgMLST) expands the MLST concept to hundreds or thousands of genes present in the core genome of a species. The core genome includes genes shared by essentially all members of a species. By comparing allele profiles across these many loci, cgMLST achieves strain-level resolution that can differentiate isolates within an outbreak cluster.

Single nucleotide polymorphism (SNP) typing identifies individual nucleotide differences across the genome. SNPs are the smallest possible genetic variation, and comparing SNP profiles between isolates provides the highest resolution available. SNP typing can reveal transmission chains and evolutionary relationships that other methods cannot distinguish.

The choice among these methods depends on the research question, available resources, and the level of discrimination needed. A laboratory investigating a suspected foodborne outbreak requires different resolution than a study examining global population structure of a species.

Core Principles of Each Method

7-Gene MLST

The 7-gene MLST approach targets internal fragments of seven housekeeping genes. Each unique combination of alleles across these seven loci defines a sequence type (ST). The method was developed to provide portable, reproducible typing results that could be compared across laboratories and databases.

The slow mutation rate of housekeeping genes means that isolates from the same outbreak typically share the same ST. This makes MLST useful for species identification and broad epidemiological comparisons. However, isolates that are epidemiologically unrelated can also share the same ST, limiting the method's discriminatory power.

An inter-laboratory study of foodborne pathogens found that 7-gene MLST yielded consistent sequence types across different bioinformatics tools, with one exception. This consistency makes MLST a reliable baseline method for species identification and initial strain characterization.

Core Genome MLST

cgMLST compares allele profiles across the core genome of a species. The number of loci varies by species and schema. For example, a validated cgMLST assay for Yersinia pestis comprises 3,139 gene targets, enabling strain-level resolution for this highly clonal pathogen.

The method assigns each gene a numeric allele based on sequence variation. Isolates are compared by calculating the number of allele differences between their profiles. Closely related isolates differ by few alleles, while distantly related isolates differ by many.

The choice of schema significantly affects cgMLST results. The inter-laboratory study found that for Campylobacter jejuni, cluster assignment was strongly affected by cgMLST schemas that differed by a factor of two in the number of loci. For Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas. For other species, different schemas introduced larger discrepancies than different software.

SNP Typing

SNP typing identifies single nucleotide differences between genomes. The approach typically involves mapping sequencing reads to a reference genome, identifying variable positions, and comparing SNP profiles between isolates.

SNP typing provides the highest resolution of the three methods because it detects individual nucleotide changes instead of allele differences. This resolution is valuable for investigating transmission events and understanding fine-scale evolutionary relationships.

The inter-laboratory study found that SNP calling using the Snippy tool produced concordant results across participants, except for Campylobacter jejuni when recombination was considered. This finding suggests that SNP typing can be reproducible with standardized tools, but recombination can complicate interpretation in some species.

Resolution and Discriminatory Power

The resolution of a typing method determines its ability to distinguish between closely related isolates. MLST provides the lowest resolution because it examines only seven loci. Isolates from the same outbreak frequently share the same ST, making MLST unsuitable for outbreak investigations where fine discrimination is needed.

cgMLST provides substantially higher resolution by examining hundreds to thousands of loci. The Yersinia pestis cgMLST assay demonstrated that closely related or epidemiologically linked strains differed by zero to three alleles. This allele distance range serves as an operational reference for identifying highly similar isolates in that species.

SNP typing provides the highest resolution by detecting individual nucleotide differences. This resolution can reveal transmission chains and identify the most closely related isolates in an outbreak. However, the interpretation of SNP distances requires careful consideration of mutation rates and recombination.

The choice of resolution depends on the epidemiological question. A study examining global population structure might use MLST to identify major lineages. An outbreak investigation requires cgMLST or SNP typing to distinguish between isolates that would share the same ST.

Cost and Resource Considerations

The cost of each method varies with the number of loci analyzed and the bioinformatics complexity. MLST is the least expensive because it requires only seven loci. Traditional MLST can be performed using Sanger sequencing of PCR products, which is accessible to most laboratories.

cgMLST requires whole genome sequencing to obtain the core genome allele profiles. The cost includes sequencing reagents, library preparation, and bioinformatics analysis. The number of loci in the schema affects the computational requirements but not the sequencing cost, since whole genome sequencing captures all loci simultaneously.

SNP typing also requires whole genome sequencing. The bioinformatics analysis is more complex than cgMLST because it requires read mapping, variant calling, and quality filtering. This complexity can increase the time and expertise required for analysis.

The inter-laboratory study highlighted that differences in the interpretation of raw sequence and genome assembly quality influenced sample inclusion and cluster composition. Intra-species contamination was the most significant factor driving variability in decisions on whether to include or exclude samples. These quality considerations affect all three methods but have the greatest impact on cgMLST and SNP typing.

Bioinformatics Workflows

Sequence Quality Assessment

All three methods require high-quality sequence data. The first step in any workflow is assessing raw sequence quality. This assessment includes evaluating read depth, base quality scores, and the presence of contamination.

The inter-laboratory study found that differences in the interpretation of raw sequence quality influenced sample inclusion. Laboratories using different quality thresholds made different decisions about which samples to include in cluster analysis. This variability affected the final cluster composition.

Intra-species contamination was the most significant factor driving variability in sample inclusion decisions. Contamination can introduce alleles or SNPs that do not belong to the isolate, leading to incorrect cluster assignments.

MLST Workflow

The MLST workflow is straightforward. Sequence reads are assembled or analyzed directly to extract the seven housekeeping gene fragments. Each fragment is compared to known alleles in a database, and the combination of alleles defines the sequence type.

The inter-laboratory study found that 7-gene MLST yielded consistent sequence types using different bioinformatics tools, with one exception. This consistency makes MLST a reliable method for species identification and broad comparisons.

cgMLST Workflow

The cgMLST workflow involves extracting allele profiles from the core genome. Reads are assembled, and the assembly is compared to the schema to identify alleles at each locus. The allele profile is then compared to other isolates to calculate allele distances.

The choice of schema and software affects cgMLST results. The inter-laboratory study found that for Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas. For other species, different schemas introduced larger discrepancies than different software.

The Yersinia pestis cgMLST assay demonstrated the importance of schema validation. The assay was validated using 222 publicly available genomes, including outbreak isolates from Madagascar and Mongolia. The validation confirmed that the assay provides reproducible, strain-level resolution.

SNP Typing Workflow

The SNP typing workflow involves mapping reads to a reference genome, identifying variable positions, and filtering to remove low-quality calls. The resulting SNP matrix is used to calculate pairwise distances and construct phylogenetic trees.

The inter-laboratory study found that SNP calling using Snippy produced concordant results across participants, except for Campylobacter jejuni when recombination was considered. This finding suggests that standardized tools can produce reproducible SNP results, but recombination can complicate interpretation.

Schema Selection and Standardization

The choice of schema is a critical decision for cgMLST. Different schemas include different numbers of loci and may produce different clustering results. The inter-laboratory study found that Campylobacter jejuni cluster assignment was strongly affected by cgMLST schemas differing by a factor of two in the number of loci.

Schema selection should consider the species being studied and the intended application. A schema validated for one species may not be appropriate for another. The Yersinia pestis cgMLST assay was specifically developed and validated for that species, comprising 3,139 gene targets.

Standardization is essential for comparing results across laboratories. The inter-laboratory study assessed variability in cluster identification by providing four datasets of 50 raw Illumina paired-end sequences covering Shiga toxin-producing Escherichia coli, Listeria monocytogenes, Salmonella enterica, and Campylobacter jejuni. Participants applied in-house protocols following general rules of a specified guideline.

The study revealed that cgMLST was mainly reproducible regarding bioinformatics. However, the largest influence on cgMLST-defined clusters was the inclusion or exclusion of samples. This finding emphasizes the importance of standardized quality assessment and sample inclusion criteria.

Interpretation of Results

MLST Interpretation

MLST results are interpreted by comparing sequence types. Isolates with the same ST are considered closely related, while isolates with different STs are considered distinct. The interpretation is straightforward because the nomenclature is well established.

The limitation of MLST is that isolates from the same outbreak may share the same ST as unrelated isolates. This limitation makes MLST unsuitable for outbreak investigations where fine discrimination is needed.

cgMLST Interpretation

cgMLST results are interpreted by calculating allele distances between isolates. The number of allele differences determines the relatedness. The Yersinia pestis cgMLST analysis revealed that closely related or epidemiologically linked strains differed by zero to three alleles.

The interpretation of allele distances requires species-specific thresholds. A distance that indicates relatedness in one species may indicate unrelatedness in another. These thresholds should be established through validation studies.

The Yersinia pestis cgMLST assay showed clustering patterns concordant with previously described SNP assays. This concordance provides confidence that cgMLST can serve as a standardized high-resolution typing approach for outbreak investigations, source tracking, and comparative genomic surveillance.

SNP Typing Interpretation

SNP typing results are interpreted by examining the number and distribution of SNP differences between isolates. Phylogenetic trees constructed from SNP matrices reveal evolutionary relationships and transmission chains.

The interpretation of SNP distances requires consideration of mutation rates and recombination. Recombination can introduce multiple SNPs in a single event, complicating the interpretation of SNP distances. The inter-laboratory study found that Campylobacter jejuni SNP calling was complicated when recombination was considered.

Applications in Outbreak Investigation

Outbreak investigations require high-resolution typing to distinguish between isolates that would share the same MLST sequence type. cgMLST and SNP typing provide the resolution needed for these investigations.

The inter-laboratory study assessed cluster detection for foodborne bacterial pathogens, including Shiga toxin-producing Escherichia coli, Listeria monocytogenes, Salmonella enterica, and Campylobacter jejuni. The study found that accurate assignment of whole-genome sequences to clusters in foodborne outbreak investigations remains challenging.

The study revealed that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes. This variability can affect the identification of outbreak clusters and the attribution of cases to a common source.

The Yersinia pestis cgMLST assay demonstrated applicability for outbreak investigations. The assay distinguished strains from different geographic regions, consistent with spatial clustering. This capability supports source tracking and comparative genomic surveillance.

Applications in Genomic Surveillance

Genomic surveillance involves the systematic collection and analysis of pathogen genomes to monitor trends and detect emerging threats. The choice of typing method depends on the surveillance objectives.

MLST is useful for broad surveillance programs that monitor the distribution of lineages over time and geography. The low cost and simple interpretation make MLST accessible to laboratories with limited resources.

cgMLST provides higher resolution for surveillance programs that need to detect emerging clusters or monitor the spread of specific strains. The standardized nomenclature enables comparison across laboratories and databases.

SNP typing provides the highest resolution for surveillance programs that need to understand fine-scale transmission dynamics. The phylogenetic context provided by SNP typing can reveal how strains spread through populations.

The Neisseria gonorrhoeae LIN code system demonstrates an innovative approach to genomic surveillance. This system uses a refined 1430 core gene MLST to develop a hierarchical lineage nomenclature. The LIN codes convey lineage information at multiple levels of resolution within one code, providing immediate context to an isolate's ancestry.

The LIN code system relates to familiar typing schemes such as Ng cgMLST v1, 7-locus MLST, and NG-STAR clonal complex. This compatibility facilitates communication between laboratories using different typing systems. The codes are automatically assigned and publicly accessible via the pubmlst.org database.

Antimicrobial Resistance Surveillance

Antimicrobial resistance surveillance requires typing methods that can link genotypes to resistance phenotypes. The World Health Organization defines antimicrobial resistance as a global health threat and emphasizes the importance of surveillance for stewardship and public health action.

The Neisseria gonorrhoeae LIN code study found that clustering with LIN codes accurately reflects gonococcal diversity and population structure, providing insight into associations between genotype and phenotype for traits such as antibiotic resistance. This finding demonstrates the value of high-resolution typing for antimicrobial resistance surveillance.

The investigation of vanB-gene-dominated resistance in Enterococcus species and silent vanA-gene carriage in phenotypically susceptible isolates used genomic epidemiology to understand resistance in hospital settings. This study illustrates how genomic typing can reveal resistance mechanisms that phenotypic testing might miss.

cgMLST and SNP typing can identify genetic determinants of resistance and track their spread through populations. This capability supports antimicrobial resistance surveillance and informs stewardship efforts.

Quality Control and Assurance

Quality control is essential for reliable typing results. The inter-laboratory study found that differences in the interpretation of raw sequence and genome assembly quality influenced sample inclusion and cluster composition.

Laboratories should implement standardized quality assessment protocols. These protocols should include criteria for read depth, base quality, and contamination detection. The American Society for Microbiology provides professional microbiology education and laboratory practice resources that can support quality assurance efforts.

The use of authenticated biological materials is important for quality control. The American Type Culture Collection provides authenticated strains that can serve as reference materials for validating typing methods and troubleshooting problems.

The Bacteriological Analytical Manual from the U.S. Food and Drug Administration provides official food microbiology method and quality context. This resource can support laboratories performing foodborne pathogen typing.

Common Failure Patterns

Contamination

Intra-species contamination was the most significant factor driving variability in sample inclusion decisions in the inter-laboratory study. Contamination can introduce alleles or SNPs that do not belong to the isolate, leading to incorrect cluster assignments.

Laboratories should implement contamination detection protocols. These protocols should include checks for mixed signals in sequence data and comparison to expected profiles for the species.

Schema Mismatch

The choice of cgMLST schema significantly affects clustering results. The inter-laboratory study found that Campylobacter jejuni cluster assignment was strongly affected by schemas differing by a factor of two in the number of loci.

Laboratories should use validated schemas appropriate for their species and application. Schema selection should consider the number of loci, the species coverage, and the validation evidence.

Software Variability

Different bioinformatics tools can produce different results. The inter-laboratory study found that for Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas.

Laboratories should document their bioinformatics protocols and participate in inter-laboratory comparisons to assess reproducibility. Standardized tools and protocols can reduce variability.

Quality Threshold Differences

Differences in the interpretation of raw sequence quality influenced sample inclusion in the inter-laboratory study. Laboratories using different quality thresholds made different decisions about which samples to include.

Laboratories should implement standardized quality thresholds and document their criteria. These thresholds should be based on evidence and validated for the species being studied.

Limitations and Evidence Gaps

The inter-laboratory study provides valuable evidence about the reproducibility of typing methods, but it has limitations. The study used four datasets of 50 raw Illumina paired-end sequences, which may not represent all species or sequencing platforms.

The study found that cgMLST was mainly reproducible regarding bioinformatics, but the largest influence on cgMLST-defined clusters was the inclusion or exclusion of samples. This finding emphasizes the importance of standardized sample inclusion criteria.

The Yersinia pestis cgMLST assay was validated using 222 publicly available genomes. This validation provides evidence for the assay's reproducibility, but additional validation may be needed for diverse strain collections.

The Neisseria gonorrhoeae LIN code study was published in 2025 and represents a recent development in lineage nomenclature. The system's utility for antimicrobial resistance surveillance requires continued evaluation.

The vanB-gene-dominated resistance study in Enterococcus species was published in 2026 and used genomic epidemiology in two hospitals in Latvia. The findings may not generalize to other settings or species.

Biosafety and Laboratory Practice

Biosafety considerations apply to the laboratory work that produces the sequence data used for typing. The Biosafety in Microbiological and Biomedical Laboratories from CDC and NIH provides principles for risk assessment, containment, decontamination, and laboratory biosafety.

Laboratories should conduct risk assessments for the organisms they handle. The risk assessment should consider the pathogenicity of the organism, the procedures performed, and the laboratory facilities and equipment available.

The American Society for Microbiology provides professional microbiology education and laboratory practice resources. These resources can support training and competency assessment for laboratory personnel.

The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context. These standards support the phenotypic characterization that complements genomic typing.

Professional Escalation Criteria

Laboratories should have criteria for escalating typing results to public health authorities or other relevant parties. These criteria should be established in advance and documented in standard operating procedures.

Escalation is appropriate when typing results indicate a potential outbreak, a novel or emerging strain, or a public health threat. The World Health Organization emphasizes the importance of surveillance for antimicrobial resistance and public health action.

Laboratories should establish communication protocols with public health authorities. These protocols should specify the information to be shared, the timeline for reporting, and the responsible personnel.

The National Center for Biotechnology Information Pathogen Detection provides a platform for sharing pathogen sequence data. This platform supports collaborative surveillance and outbreak detection.

Records and Documentation

Laboratories should maintain records of typing results, including the methods used, the quality metrics, and the interpretation. These records support reproducibility and enable retrospective analysis.

Documentation should include the version of the schema, the software and parameters used, and the quality thresholds applied. This documentation enables other laboratories to reproduce the analysis.

The inter-laboratory study found that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes. Documentation of these variables is essential for interpreting results and comparing across laboratories.

Laboratories should participate in proficiency testing programs to assess the accuracy and reproducibility of their typing methods. These programs provide feedback that can identify areas for improvement.

Practical Implementation Steps

Step 1: Define the Epidemiological Question

The first step in selecting a typing method is defining the epidemiological question. A study examining global population structure requires different resolution than an outbreak investigation.

For species identification and broad lineage assignment, 7-gene MLST is appropriate. For outbreak detection and source tracking, cgMLST or SNP typing is needed. For fine-scale transmission analysis, SNP typing provides the highest resolution.

Step 2: Assess Available Resources

The choice of typing method depends on available resources, including sequencing capacity, bioinformatics expertise, and budget. MLST can be performed with minimal resources, while cgMLST and SNP typing require whole genome sequencing and bioinformatics support.

Step 3: Select the Schema and Tools

For cgMLST, select a validated schema appropriate for the species. Consider the number of loci, the validation evidence, and the compatibility with existing databases. For SNP typing, select a reference genome and a standardized variant calling tool.

Step 4: Implement Quality Control

Implement standardized quality assessment protocols, including criteria for read depth, base quality, and contamination detection. Document these criteria and apply them consistently.

Step 5: Validate the Method

Validate the selected method using authenticated reference strains. The American Type Culture Collection provides authenticated biological materials that can support validation.

Step 6: Participate in Inter-Laboratory Comparisons

Participate in inter-laboratory comparisons to assess reproducibility. The inter-laboratory study demonstrated that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes.

Step 7: Document and Report

Maintain records of typing results, including methods, quality metrics, and interpretation. Report results according to established protocols and escalate when appropriate.

Method Selection Decision Flow

flowchart TD
    A[Define Epidemiological Question] --> B{Resolution Needed?}
    B -->|Species or Lineage| C[7-gene MLST]
    B -->|Outbreak or Cluster| D{Outbreak or Evolution?}
    D -->|Outbreak Detection| E[cgMLST]
    D -->|Evolutionary Analysis| F[SNP Typing]
    C --> G[Interpret Results]
    E --> G
    F --> G
    G --> H{Public Health Concern?}
    H -->|Yes| I[Escalate to Authorities]
    H -->|No| J[Document and Report]

Comparative Performance Across Species

Building a Laboratory Decision Record for Typing Method Selection

A practical decision record transforms the theoretical comparison of MLST, cgMLST, and SNP typing into a documented, repeatable process for your laboratory. The inter-laboratory study of foodborne pathogens demonstrated that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes, and that differences in the interpretation of raw sequence and genome assembly quality influenced sample inclusion and final cluster composition. A structured decision record addresses this variability by forcing explicit documentation of the rationale, quality thresholds, and interpretation criteria applied to each typing project.

Purpose of the Decision Record

The decision record serves as a written justification for method selection that can be reviewed, audited, and improved over time. It captures the epidemiological question, the resolution required, the resources available, and the quality criteria applied. This documentation supports reproducibility because another laboratory can examine your record and understand why you chose a particular method and how you interpreted the results.

The record also provides a mechanism for identifying systematic problems. If your laboratory consistently struggles with cluster assignment for a particular species, the decision records for those projects will reveal whether the issue stems from schema selection, quality thresholds, or interpretation criteria. The inter-laboratory study found that intra-species contamination was the most significant factor driving variability in decisions on whether to include or exclude samples. A decision record that documents contamination checks and their outcomes will help you identify whether contamination is a recurring issue in your workflow.

Components of the Decision Record

A complete decision record contains seven components that correspond to the key decision points in the typing workflow. Each component requires specific information that must be recorded before, during, and after the analysis.

Project Identification and Epidemiological Question

The first component identifies the project and states the epidemiological question in explicit terms. This statement drives all subsequent decisions. A project investigating a suspected foodborne outbreak requires different resolution than a study examining global population structure. The record should state whether the goal is species identification, broad lineage surveillance, outbreak detection, source tracking, fine-scale transmission analysis, or evolutionary study.

The record should also identify the pathogen species involved. This information matters because the inter-laboratory study found that different species respond differently to schema and software choices. For Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas. For other species, different schemas introduced larger discrepancies than different software. Campylobacter jejuni cluster assignment was strongly affected by cgMLST schemas differing by a factor of two in the number of loci.

Method Selection Rationale

The second component documents the method selected and the rationale for that selection. This section should reference the epidemiological question and explain why the chosen method provides the appropriate resolution. If you select 7-gene MLST, the rationale should explain why species or lineage level resolution is sufficient. If you select cgMLST, the rationale should identify the schema and explain why that schema is appropriate for the species. If you select SNP typing, the rationale should identify the reference genome and variant calling tool.

The rationale should also acknowledge the limitations of the chosen method. The inter-laboratory study found that 7-gene MLST yielded consistent sequence types using different bioinformatics tools, with one exception. This consistency makes MLST reliable for species identification, but the method cannot distinguish outbreak isolates that share the same sequence type. The decision record should state these limitations explicitly so that downstream interpretation accounts for them.

Sample Inclusion and Quality Criteria

The third component documents the criteria used to decide which samples enter the analysis. The inter-laboratory study found that the largest influence on cgMLST-defined clusters was the inclusion or exclusion of samples. Differences in the interpretation of raw sequence and genome assembly quality influenced sample inclusion and finally cluster composition. This finding means that sample inclusion decisions can change the final cluster assignments more than any bioinformatics choice.

The record should specify the minimum read depth, the base quality score threshold, and the contamination detection method. The record should also document how you handle samples that fail these criteria. Do you exclude them entirely, or do you include them with a flag indicating reduced confidence? The inter-laboratory study found that intra-species contamination was the most significant factor driving variability in decisions on whether to include or exclude samples. Your record should document the contamination detection approach and the criteria for determining that a sample is contaminated.

Bioinformatics Protocol Documentation

The fourth component documents the exact bioinformatics protocol used. This documentation includes the software versions, the parameters applied, and the reference databases or schemas used. The inter-laboratory study found that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes. For Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas. This finding means that software choice can introduce variability that affects cluster assignments.

The record should specify the version of every tool used, because software updates can change results. The record should also document the parameters applied, such as the minimum allele identity threshold for cgMLST or the minimum mapping quality for SNP calling. This level of detail enables another laboratory to reproduce your analysis exactly.

Interpretation Criteria and Thresholds

The fifth component documents the interpretation criteria applied to the results. For cgMLST, this documentation includes the allele distance threshold used to define clusters. The Yersinia pestis cgMLST analysis revealed that closely related or epidemiologically linked strains differed by zero to three alleles, suggesting this range as an operational reference for identifying highly similar isolates. Your record should state the threshold you use and the evidence supporting that threshold.

For SNP typing, the record should document the SNP distance threshold used to define relatedness and the approach to handling recombination. The inter-laboratory study found that SNP calling using Snippy produced concordant results across participants, except for Campylobacter jejuni when recombination was considered. Your record should state how you handle recombination in species where it is common.

Results Summary and Interpretation

The sixth component summarizes the results and documents the interpretation. This section should include the cluster assignments, the allele or SNP distances between key isolates, and the conclusions drawn from the analysis. The record should also document any discrepancies between expected and observed results.

The interpretation should reference the epidemiological question stated in the first component. If the question was outbreak detection, the interpretation should state whether the typing results support the identification of an outbreak cluster. If the question was source tracking, the interpretation should state whether the results support attribution to a particular source.

Escalation and Reporting Decisions

The seventh component documents any escalation or reporting decisions made based on the typing results. The World Health Organization emphasizes the importance of surveillance for antimicrobial resistance and public health action. If typing results indicate a potential outbreak, a novel strain, or a public health threat, the record should document the escalation pathway and the information shared with public health authorities.

The National Center for Biotechnology Information Pathogen Detection provides a platform for sharing pathogen sequence data. The record should document whether sequence data were submitted to this platform or another database and the accession numbers associated with the submission.

Implementing the Decision Record in Practice

Implementing a decision record system requires establishing a template, training personnel, and integrating the record into the laboratory workflow. The template should include all seven components with fields for the required information. The template should be available in a format that supports version control so that changes to the record can be tracked over time.

Training should cover the purpose of the decision record and the specific information required for each component. Personnel should understand that the record is not paperwork for its own sake but a tool for improving reproducibility and identifying systematic problems. The American Society for Microbiology provides professional microbiology education and laboratory practice resources that can support training efforts.

Integration into the workflow means that the decision record is completed at the same time as the analysis, not after the fact. The epidemiological question should be documented before the analysis begins. The quality criteria should be applied and documented as samples are processed. The interpretation should be documented when the results are reviewed. This real-time documentation ensures that the record accurately reflects the decisions made.

Reviewing Decision Records for Quality Improvement

Decision records should be reviewed periodically to identify patterns and improve laboratory practice. The review should examine whether the documented rationale for method selection aligns with the actual performance of the method. If a particular method consistently fails to provide the resolution needed for the epidemiological question, the rationale should be revised.

The review should also examine whether quality criteria are being applied consistently across projects. The inter-laboratory study found that differences in the interpretation of raw sequence quality influenced sample inclusion. If the decision records show inconsistent application of quality thresholds, this finding indicates a need for additional training or more explicit criteria.

The review should examine whether interpretation thresholds are appropriate for the species being studied. The Yersinia pestis cgMLST assay demonstrated that closely related strains differed by zero to three alleles. If your decision records show that your threshold produces clusters that do not align with epidemiological data, the threshold may need adjustment.

Common Failure Patterns in Decision Records

Several common failure patterns can undermine the utility of decision records. The first pattern is incomplete documentation. If the epidemiological question is not stated clearly, the rationale for method selection cannot be evaluated. If quality criteria are not documented, the sample inclusion decisions cannot be reviewed.

The second pattern is retrospective documentation. If the decision record is completed after the analysis is finished, the record may reflect what the analyst wishes they had done instead of what they actually did. This pattern undermines the value of the record for identifying systematic problems.

The third pattern is inconsistent application of criteria. If different analysts apply different quality thresholds or interpretation criteria, the results cannot be compared across projects. The inter-laboratory study found that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes. Consistent application of documented criteria reduces this variability.

The fourth pattern is failure to update the record when protocols change. If your laboratory changes the cgMLST schema or the SNP calling tool, the decision records for projects using the new protocol should reflect the change. The inter-laboratory study found that for Salmonella enterica, discrepancies due to different software were larger than discrepancies due to different schemas. Documenting software changes helps you understand how protocol changes affect results.

Records and Measurements for Decision Record Implementation

The decision record system requires specific records and measurements to function effectively. The laboratory should maintain a log of all typing projects with the associated decision records. This log should include the project identifier, the species, the method selected, and the date of the analysis.

The laboratory should also maintain records of quality metrics for each sample. These records should include read depth, base quality scores, and contamination check results. The inter-laboratory study found that intra-species contamination was the most significant factor driving variability in sample inclusion decisions. Tracking contamination rates over time can help you identify whether contamination is becoming more or less common in your workflow.

The laboratory should maintain records of interpretation thresholds and the evidence supporting those thresholds. The Yersinia pestis cgMLST assay provides an example of evidence-based threshold selection. Your records should document the source of your thresholds and any validation studies that support them.

The laboratory should maintain records of inter-laboratory comparison results. The inter-laboratory study demonstrated that variability in bioinformatics tools and quality metrics significantly impacts clustering outcomes. Participating in inter-laboratory comparisons and documenting the results helps you assess whether your laboratory's results are consistent with other laboratories.

Professional Escalation Criteria for Decision Record Findings

The decision record should include criteria for escalating findings to public health authorities or other relevant parties. These criteria should be established in advance and documented in standard operating procedures. The World Health Organization emphasizes the importance of surveillance for antimicrobial resistance and public health action.

Escalation is appropriate when typing results indicate a potential outbreak, a novel or emerging strain, or a public health threat. The decision record should document the specific findings that triggered escalation and the information shared with public health authorities.

The National Center for Biotechnology Information Pathogen Detection provides a platform for sharing pathogen sequence data. The decision record should document whether sequence data were submitted to this platform and the associated accession numbers.

Biosafety Context for Decision Record Implementation

The decision record system operates within the broader context of laboratory biosafety. The Biosafety in Microbiological and Biomedical Laboratories from CDC and NIH provides principles for risk assessment, containment, decontamination, and laboratory biosafety. These principles apply to the laboratory work that produces the sequence data used for typing.

Laboratories should conduct risk assessments for the organisms they handle. The risk assessment should consider the pathogenicity of the organism, the procedures performed, and the laboratory facilities and equipment available. The decision record should document any biosafety considerations relevant to the typing project.

The American Society for Microbiology provides professional microbiology education and laboratory practice resources. These resources can support training and competency assessment for laboratory personnel involved in typing and decision record implementation.

The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context. These standards support the phenotypic characterization that complements genomic typing and can inform the interpretation of typing results in the context of antimicrobial resistance surveillance.

Frequently Asked Questions

What is the main difference between MLST and cgMLST?

MLST examines seven housekeeping genes, while cgMLST examines hundreds to thousands of core genome genes. The larger number of loci in cgMLST provides higher resolution and can distinguish isolates that share the same MLST sequence type.

When should I use SNP typing instead of cgMLST?

Use SNP typing when you need the highest possible resolution, such as for fine-scale transmission analysis or evolutionary studies. SNP typing detects individual nucleotide differences, while cgMLST compares allele profiles across core genes.

How does schema choice affect cgMLST results?

Schema choice significantly affects cgMLST clustering results. The inter-laboratory study found that Campylobacter jejuni cluster assignment was strongly affected by schemas differing by a factor of two in the number of loci. Different schemas can produce different clusters for the same data.

What causes variability in typing results across laboratories?

Variability in typing results can be caused by differences in bioinformatics tools, quality metrics, and sample inclusion decisions. The inter-laboratory study found that intra-species contamination was the most significant factor driving variability in sample inclusion decisions.

Can MLST distinguish between outbreak isolates?

MLST generally cannot distinguish between outbreak isolates because they typically share the same sequence type. Higher resolution methods such as cgMLST or SNP typing are needed for outbreak investigations.

How do I choose the right cgMLST schema for my species?

Choose a schema that has been validated for your species and application. Consider the number of loci, the validation evidence, and the compatibility with existing databases. The Yersinia pestis cgMLST assay provides an example of a validated schema for a highly clonal species.

What quality metrics should I assess before typing?

Assess read depth, base quality scores, and the presence of contamination. The inter-laboratory study found that differences in the interpretation of raw sequence quality influenced sample inclusion and cluster composition.

How do LIN codes relate to traditional typing schemes?

LIN codes provide a hierarchical lineage nomenclature based on core gene MLST. The Neisseria gonorrhoeae LIN code system relates to familiar typing schemes such as Ng cgMLST v1, 7-locus MLST, and NG-STAR clonal complex, facilitating communication between laboratories using different systems.

Related Bacteriology Guides

• Bacterial Contamination of Poultry: Salmonella and E. coli
• Bacterial Diseases of Chickens: A Comprehensive Overview
• Avian Influenza in Australia: Epidemiology and Surveillance
• Avian Influenza in Wales: Surveillance and Control
• Bacterial Growth Dynamics in Chicken: From Farm to Refrigeration

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.
• An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens.. Frontiers in microbiology, 2025.
• vanB-Gene-Dominated Resistance in Enterococcus spp. and Silent vanA-Gene Carriage in Phenotypically Susceptible Isolates: Genomic Epidemiology in Two Hospitals in Latvia. 2026.
• Genetic Diversity and Spatial Distribution of &lt,i&gt,Yersinia pestis&lt,/i&gt, by Core Genome-Based Multilocus Sequence Typing Analysis.. 2026.
• &lt,i&gt,Neisseria gonorrhoeae&lt,/i&gt, LIN codes provide a robust, multi-resolution lineage nomenclature.. 2025.

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