# From Population Genomics to Public Health ## Quick Answer - Use this template to structure a genomic epidemiology manuscript that connects bacterial whole-genome sequencing data to population-level transmission, resistance, and surveillance conclusions. - Organize methods around isolate selection, sequencing platforms, bioinformatics pipelines, phylogenetic inference, and quality thresholds before presenting results as clusters, resistance determinants, and temporal trends. - Recognize that genomic findings require integration with epidemiological metadata and laboratory quality controls, and that sequencing alone cannot establish causation or replace standardized susceptibility testing. ## At a Glance | Manuscript Section | Core Content | Common Evidence Source | Typical Limitation | | --- | --- | --- | --- | | Introduction | Disease burden, pathogen biology, surveillance gap, study objective | WHO antimicrobial resistance reports, peer-reviewed outbreak studies | Overstating genomic utility without epidemiological context | | Methods | Isolate collection, DNA extraction, sequencing, bioinformatics, quality control | NCBI Pathogen Detection, CLSI susceptibility standards, BAM methods | Insufficient detail for replication or inter-laboratory comparison | | Results | Phylogenetic clusters, resistance determinants, temporal and geographic signals | Local outbreak investigations, wastewater surveillance datasets | Cluster interpretation without case linkage or sampling completeness | | Discussion | Transmission inference, public health implications, study limitations | Systematic reviews of genomic epidemiology, biosafety guidance | Confusing genomic relatedness with direct transmission events | ## Introduction to Genomic Epidemiology Manuscripts Genomic epidemiology applies whole-genome sequencing to pathogen populations to understand transmission dynamics, antimicrobial resistance dissemination, and outbreak origins. For bacterial pathogens, this approach has become a standard component of public health surveillance and outbreak investigation. Researchers preparing manuscripts in this field face the challenge of translating complex sequence data into conclusions that are useful to laboratory professionals, epidemiologists, and public health decision-makers. The core problem addressed by this template is the need for a structured writing framework that moves logically from pathogen biology through laboratory methods to population-level interpretation. A well-organized genomic epidemiology paper allows readers to assess the validity of the findings, reproduce the analytical methods, and apply the conclusions to their own surveillance or clinical context. The template presented here draws on established practices in bacterial genomics, pathogen surveillance, and molecular epidemiology. It incorporates principles from official laboratory biosafety guidance, professional microbiology standards, and published genomic epidemiology investigations. The structure emphasizes transparency in methods, rigor in quality control, and caution in interpreting phylogenetic relationships. ## Core Principles of Genomic Epidemiology Writing ### Linking Pathogen Biology to Population Questions A genomic epidemiology manuscript must begin with a clear statement of the biological question. For bacterial pathogens, this question often concerns how strains spread through a population, which genetic determinants contribute to antimicrobial resistance, or how genomic variation relates to clinical outcomes. The introduction should establish the pathogen's relevant biological features, including its transmission routes, environmental persistence, and known resistance mechanisms. The World Health Organization defines antimicrobial resistance as a global health threat requiring coordinated surveillance and stewardship efforts. Manuscripts addressing resistance should frame their genomic findings within this broader public health context, connecting bacterial genotype data to the practical problem of treatment failure and resistance dissemination. ### Matching Study Design to Genomic Methods The study design determines which genomic analyses are appropriate and how results should be interpreted. A local outbreak investigation requires different sampling strategies and analytical approaches than a population-level surveillance study. The systematic review of SARS-CoV-2 genomic epidemiology investigations found that most early studies were conducted in healthcare settings or high-income countries, with phylogenetic trees as the most common analytical approach. Bacterial genomics manuscripts should specify whether the study aims to confirm suspected transmission links, detect multiple introductions, or characterize resistance gene dissemination. ### Integrating Genomic and Epidemiological Data Genomic data gain interpretive power when combined with epidemiological metadata. A phylogenetic cluster becomes meaningful when linked to patient locations, exposure histories, or temporal patterns. The manuscript should describe how genomic and epidemiological data were integrated, including any case definitions, time periods, and geographic boundaries used in the analysis. ## Structuring the Methods Section ### Isolate Selection and Sampling Strategy The methods section must describe how bacterial isolates were selected for sequencing. This includes the source of isolates, whether they came from clinical specimens, environmental samples, food products, or wastewater, and the sampling frame that determined which isolates were included. The Bacteriological Analytical Manual provides official context for food-related bacterial isolation methods, while culture collections such as ATCC offer authenticated reference strains for quality control. Describe the inclusion and exclusion criteria for isolates, the time period of collection, and the geographic scope. If the study used convenience sampling or a subset of available isolates, state this clearly and discuss the implications for generalizability. ### DNA Extraction and Sequencing Platforms Detail the DNA extraction method, including the commercial kit or laboratory protocol used, and any quality checks performed on the extracted DNA. For sequencing, specify the platform, library preparation method, and target coverage. The SARS-CoV-2 systematic review noted that Illumina and Oxford Nanopore platforms with tiled-amplicon protocols were the most common sequencing methods in early genomic epidemiology investigations. Bacterial studies may use whole-genome shotgun sequencing or targeted amplicon approaches depending on the research question. Include information about sequencing depth, read length, and any multiplexing strategies. This level of detail allows other laboratories to replicate the methods and enables readers to assess data quality. ### Bioinformatics Pipeline and Quality Control The bioinformatics section should describe the computational steps from raw sequencing reads to final analysis. This includes read trimming, quality filtering, assembly or read mapping, variant calling, and species confirmation. Specify the software tools and versions used, as well as any parameters that could affect results. Quality control metrics are essential for reproducible genomic epidemiology. Report the thresholds used for read depth, genome coverage, and assembly quality. The NCBI Pathogen Detection database provides a framework for standardized analysis of pathogen sequences, and manuscripts should indicate whether sequences were submitted to public databases and whether they meet database quality standards. ### Phylogenetic and Cluster Analysis Describe the methods used to construct phylogenetic trees or identify genetic clusters. Common approaches include single-nucleotide polymorphism analysis, core genome multilocus sequence typing, and whole-genome phylogenetic inference. Specify the reference genome used, the alignment method, and the tree-building algorithm. For cluster identification, state the genetic distance threshold used to define relatedness and how this threshold was selected. The systematic review of genomic epidemiology investigations found that phylogenetic trees were the most common analytical approach, but the choice of method should be justified based on the study question and the genetic diversity of the pathogen. ## Presenting Results Effectively ### Describing Genomic Diversity and Population Structure The results section should begin with a description of the sequenced isolates, including the number of isolates, their sources, and basic genomic characteristics. Report the species confirmation results, the distribution of sequence types or lineages, and the overall genetic diversity observed. For population-level studies, describe the phylogenetic structure of the pathogen population. This may include the identification of major clades, the relationships between lineages, and any geographic or temporal clustering. Use figures such as phylogenetic trees, minimum spanning networks, or temporal trees to visualize these relationships. ### Reporting Antimicrobial Resistance Determinants When the study addresses antimicrobial resistance, report the resistance genes and mutations identified in the sequenced genomes. Connect these genetic findings to phenotypic susceptibility data where available. The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context, and manuscripts should indicate whether phenotypic testing was performed and which standards were followed. Present resistance determinants in a way that distinguishes between genes known to confer resistance, genes with uncertain significance, and mutations that may contribute to resistance. Avoid overinterpreting the presence of a resistance gene as definitive evidence of phenotypic resistance without supporting susceptibility data. ### Presenting Cluster and Transmission Findings For outbreak investigations, present the genomic clusters identified and their relationship to epidemiological data. Describe whether clusters corresponded to suspected transmission events, whether multiple introductions were detected, and whether asymptomatic or presymptomatic transmission was identified. The SARS-CoV-2 systematic review found that genomic data enabled confirmation of suspected transmission links and detection of multiple introductions, and bacterial studies should report similar findings with appropriate caveats. Use tables to summarize cluster characteristics, including the number of isolates per cluster, the time period, the geographic locations, and the epidemiological links. This allows readers to assess the strength of the transmission inferences. ## Interpreting Phylogenetic Relationships ### Distinguishing Genomic Relatedness from Transmission A fundamental principle in genomic epidemiology is that genomic relatedness does not automatically demonstrate direct transmission. Two isolates may share a recent common ancestor without being part of the same transmission chain, particularly in pathogens with large population sizes or environmental reservoirs. The discussion should acknowledge this limitation and interpret clusters in light of epidemiological data. Consider the genetic diversity of the pathogen when interpreting cluster thresholds. A pathogen with a low mutation rate may show identical genomes across unrelated cases, while a pathogen with a high mutation rate may show substantial diversity within a single transmission chain. The manuscript should discuss how the pathogen's biology affects the interpretation of genetic distances. ### Temporal and Geographic Considerations Phylogenetic trees can be interpreted in a temporal context using molecular clock methods, which estimate the time of divergence between lineages. If temporal analysis was performed, describe the calibration approach and the assumptions made. Geographic interpretation should consider sampling bias, as isolates from well-sampled regions may appear more closely related than isolates from undersampled areas. The wastewater surveillance data from 147 treatment plants across 40 states demonstrates how pathogen nucleic acid data can be used to study infectious disease epidemiology at a population level. Bacterial genomic studies may similarly benefit from environmental sampling to understand the distribution of strains beyond clinical cases. ## Integrating Epidemiological Context ### Case Definitions and Metadata Collection The manuscript should describe the epidemiological data collected for each isolate, including patient demographics, clinical outcomes, exposure history, and geographic location. Specify the case definitions used and the methods for data collection, such as medical record review, patient interviews, or public health surveillance systems. The quality of epidemiological metadata directly affects the interpretation of genomic clusters. Manuscripts should report the completeness of metadata and discuss how missing data may have influenced the analysis. ### Linking Genomic Clusters to Outbreak Investigations When genomic clusters are identified, the discussion should connect them to the outbreak investigation context. Describe whether the genomic findings confirmed or refuted suspected transmission links, whether they identified previously unrecognized transmission events, and how they informed public health actions. The systematic review of local genomic epidemiology investigations found that familiarity with the STROME-ID guidelines for molecular epidemiology studies would have improved overall quality. Bacterial genomics manuscripts should consider these reporting guidelines when describing their methods and results. ## Quality Control and Validation ### Laboratory Quality Controls Describe the quality control measures used throughout the laboratory workflow. This includes the use of authenticated reference strains from culture collections, negative controls to detect contamination, and positive controls to verify assay performance. The American Type Culture Collection provides authenticated biological materials that serve as quality references for bacterial strains. The Biosafety in Microbiological and Biomedical Laboratories guidance from CDC and NIH provides principles for risk assessment, containment, and decontamination that apply to the laboratory work described in genomic epidemiology manuscripts. Describe the biosafety level at which work was performed and any specific containment measures used. ### Bioinformatics Validation Bioinformatics analyses should be validated to ensure reproducibility. This includes confirming that the analysis pipeline produces consistent results across runs, that parameter choices do not dramatically alter conclusions, and that the results are robust to different analytical approaches. Report any validation steps performed, such as running the analysis with different parameters, using multiple reference genomes, or comparing results from different bioinformatics tools. The NCBI Pathogen Detection database provides a standardized framework for pathogen sequence analysis, and manuscripts should indicate whether results were consistent with database analyses. ## Common Failure Patterns in Genomic Epidemiology Manuscripts ### Overinterpreting Phylogenetic Clusters A frequent error is concluding that isolates in the same phylogenetic cluster are part of the same transmission chain without supporting epidemiological evidence. This overinterpretation can lead to incorrect public health conclusions. The manuscript should explicitly acknowledge that genomic clusters represent genetic relatedness, not necessarily direct transmission. ### Inadequate Methods Detail Manuscripts that omit key details about sequencing platforms, bioinformatics parameters, or quality thresholds cannot be replicated or properly evaluated. The methods section should provide sufficient detail for another laboratory to reproduce the analysis. This includes software versions, database access dates, and parameter settings. ### Confusing Genetic Presence with Phenotypic Resistance Reporting the presence of a resistance gene without phenotypic confirmation can mislead readers about the clinical significance of the finding. Resistance gene expression depends on promoter mutations, gene copy number, and other factors that cannot be determined from sequence data alone. Manuscripts should clearly distinguish between genotypic predictions and phenotypic susceptibility results. ### Ignoring Sampling Bias Genomic epidemiology studies are often based on convenience samples that do not represent the full pathogen population. Manuscripts that fail to acknowledge sampling bias may present misleading conclusions about the prevalence of specific lineages or the structure of the pathogen population. ## Biosafety and Regulatory Context ### Laboratory Biosafety Considerations Genomic epidemiology research involves handling bacterial pathogens that may be hazardous to laboratory workers. The Biosafety in Microbiological and Biomedical Laboratories guidance from CDC and NIH provides the framework for risk assessment, containment, and decontamination in microbiological laboratories. Manuscripts should describe the biosafety practices used, including the biosafety level, personal protective equipment, and decontamination procedures. The American Society for Microbiology provides professional microbiology education and laboratory practice context that supports safe and competent laboratory work. Researchers should follow professional standards for laboratory practice and biosafety. ### Data Sharing and Public Health Reporting Genomic epidemiology manuscripts should address data sharing practices, including deposition of sequences in public databases such as NCBI Pathogen Detection. Public data sharing enables other researchers to compare their isolates with published sequences and supports global surveillance efforts. The World Health Organization emphasizes the importance of surveillance and stewardship in addressing antimicrobial resistance. Manuscripts addressing resistance should discuss how their genomic findings contribute to surveillance efforts and inform stewardship decisions. ## Practical Implementation Steps ### Step 1: Define the Study Question and Population Before writing, clearly define the study question and the pathogen population being studied. Determine whether the study addresses outbreak investigation, population surveillance, resistance dissemination, or evolutionary dynamics. This decision shapes the sampling strategy, analytical methods, and interpretation. ### Step 2: Assemble Isolate Collection and Metadata Collect the bacterial isolates and associated epidemiological metadata. Ensure that isolate selection is documented and that metadata are complete and accurate. Consider whether additional isolates are needed to address the study question. ### Step 3: Perform Laboratory Work with Quality Controls Conduct DNA extraction, sequencing, and any phenotypic testing using established protocols. Include appropriate quality controls, including authenticated reference strains and negative controls. Document all laboratory procedures for the methods section. ### Step 4: Run Bioinformatics Analysis with Documented Parameters Process the sequencing data using a documented bioinformatics pipeline. Record software versions, parameters, and quality metrics. Perform quality control checks on the assemblies and variant calls. ### Step 5: Construct Phylogenetic Trees and Identify Clusters Build phylogenetic trees using appropriate methods and identify genetic clusters using predefined thresholds. Document the reference genome, alignment method, and tree-building algorithm. ### Step 6: Integrate Genomic and Epidemiological Data Combine the genomic results with epidemiological metadata to identify clusters with epidemiological significance. Assess whether clusters correspond to suspected transmission events and whether multiple introductions were detected. ### Step 7: Interpret Results with Appropriate Caveats Interpret the findings in light of the pathogen's biology, sampling strategy, and study limitations. Distinguish between genomic relatedness and direct transmission, and acknowledge the limitations of the analysis. ### Step 8: Write the Manuscript Following the Template Structure the manuscript according to the template, ensuring that each section addresses the relevant content and that methods are described in sufficient detail for replication. ## Records and Measurements ### Essential Records for Genomic Epidemiology Studies | Record Type | Specific Data | Purpose | | --- | --- | --- | | Isolate Registry | Source, date, location, patient identifier | Links genomic data to epidemiological context | | Sequencing Log | Platform, library method, coverage, quality scores | Documents data generation for reproducibility | | Bioinformatics Parameters | Software versions, thresholds, reference genomes | Enables replication and quality assessment | | Quality Control Results | Contamination checks, coverage metrics, assembly statistics | Demonstrates data reliability | | Susceptibility Data | MIC values, interpretation categories, testing standards | Connects genotypes to phenotypes | | Cluster Assignments | Genetic distance thresholds, cluster membership | Documents cluster identification methods | ### Measurement Standards and Quality Metrics Report the quality metrics used to assess sequencing data, including read depth, genome coverage, and assembly completeness. The NCBI Pathogen Detection database provides quality standards for pathogen sequences, and manuscripts should indicate whether their sequences meet these standards. For antimicrobial resistance findings, report the susceptibility testing methods and the standards used for interpretation. The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context, and manuscripts should reference the relevant standards without reproducing paywalled breakpoints. ## Common Failure Patterns in Data Interpretation ### Failure to Account for Recombination Bacterial populations frequently undergo homologous recombination, which can distort phylogenetic relationships inferred from whole-genome data. Manuscripts that do not account for recombination may present misleading phylogenetic trees. Consider whether recombination detection methods are appropriate for the study question. ### Failure to Distinguish Colonization from Infection Genomic epidemiology studies often include isolates from both colonization and infection. These represent different biological states with different implications for transmission and clinical outcomes. The manuscript should clearly distinguish between colonization and infection isolates and discuss the implications of this distinction. ### Failure to Address Mixed Infections Clinical specimens may contain multiple bacterial strains, and sequencing of mixed populations can produce misleading results. Manuscripts should describe how mixed infections were detected and handled, including any methods used to separate or deconvolute mixed sequences. ## Limitations and Evidence Gaps ### Sampling Completeness Genomic epidemiology studies are limited by the completeness of sampling. Isolates that are not collected or sequenced cannot be included in the analysis, and this may bias the interpretation of clusters and population structure. The manuscript should discuss the sampling frame and its limitations. ### Bioinformatics Pipeline Variability Different bioinformatics pipelines can produce different results from the same sequencing data. Manuscripts should acknowledge that the choice of pipeline may affect the findings and that cross-laboratory comparisons require standardized approaches. ### Genotype-Phenotype Discordance The presence of antimicrobial resistance genes does not always correlate with phenotypic resistance, and phenotypic resistance can occur without known resistance genes. Manuscripts should acknowledge this discordance and present both genotypic and phenotypic data where available. ### Generalizability of Findings Findings from a specific geographic region, time period, or patient population may not generalize to other settings. The manuscript should discuss the generalizability of the findings and any factors that may limit their applicability. ## Professional Escalation Criteria ### When to Consult a Bioinformatics Specialist If the analysis requires advanced phylogenetic methods, recombination detection, or population genetics inference beyond standard pipelines, consult a bioinformatics specialist. Complex analyses require specialized expertise to ensure appropriate method selection and interpretation. ### When to Consult an Epidemiologist If the interpretation of genomic clusters requires linking to epidemiological data, or if the study involves outbreak investigation, consult an epidemiologist. Epidemiological expertise is essential for interpreting clusters in the context of transmission dynamics and public health actions. ### When to Consult a Clinical Microbiologist If the study involves clinical isolates and antimicrobial resistance interpretation, consult a clinical microbiologist. Clinical expertise is needed to interpret the clinical significance of resistance determinants and to connect genomic findings to patient management. ### When to Consult a Biosafety Officer If the laboratory work involves pathogens requiring enhanced containment, or if there are questions about biosafety practices, consult a biosafety officer. The Biosafety in Microbiological and Biomedical Laboratories guidance provides the framework for risk assessment and containment decisions. ## Safety and Welfare Context ### Laboratory Worker Safety Genomic epidemiology research involves handling bacterial pathogens that may pose risks to laboratory workers. The Biosafety in Microbiological and Biomedical Laboratories guidance from CDC and NIH provides principles for risk assessment, containment, and decontamination. Manuscripts should describe the biosafety practices used and acknowledge the importance of laboratory safety. ### Public Health Welfare Genomic epidemiology research has direct implications for public health, including outbreak detection, resistance surveillance, and infection control. The World Health Organization emphasizes the importance of surveillance and stewardship in addressing antimicrobial resistance. Manuscripts should frame their findings within this public health context and discuss the implications for population health. ### Ethical Considerations Genomic epidemiology research involving human clinical isolates raises ethical considerations related to patient privacy, data sharing, and the use of human specimens. Manuscripts should describe the ethical approvals obtained and the measures taken to protect patient privacy. ## Building a Genomic Epidemiology Manuscript Review and Revision Protocol ### Purpose and Scope of the Review Protocol A structured review protocol serves a different function than the writing template. The template guides the initial composition of a genomic epidemiology manuscript, while the review protocol provides a systematic method for evaluating a completed draft before submission. Researchers often struggle to identify weaknesses in their own writing because they are too close to the data and the analytical decisions. A formal review protocol creates distance between the author and the manuscript, allowing for a critical assessment of methods transparency, interpretive claims, and alignment with reporting standards. The review protocol described here draws on the observation from the systematic review of SARS-CoV-2 genomic epidemiology investigations that study quality and adherence to common observational study reporting guidelines varied widely across published work. That review identified that familiarity with the STROME-ID guidelines for molecular epidemiology studies would have improved overall quality. A structured review protocol operationalizes this finding by translating reporting principles into concrete checklist items that can be applied to any genomic epidemiology manuscript. This protocol is designed for use by research teams before submission, by peer reviewers evaluating manuscripts, and by laboratory professionals who need to assess whether a genomic epidemiology paper provides sufficient detail for replication or public health action. The protocol focuses on the specific challenges of genomic epidemiology writing, including the integration of sequencing data with epidemiological metadata, the transparent reporting of bioinformatics parameters, and the cautious interpretation of phylogenetic relationships. ### The Five Domain Review Framework The review protocol organizes manuscript evaluation into five domains that correspond to the core components of a genomic epidemiology study. Each domain contains specific review questions that address the common failure patterns identified in published genomic epidemiology literature. The five domains are study framing, methods transparency, results presentation, interpretation validity, and public health relevance. #### Domain One: Study Framing and Objective Clarity The first review domain assesses whether the manuscript clearly defines the study question and establishes the public health context. A genomic epidemiology manuscript that does not articulate its specific objective cannot be properly evaluated, because the reader has no basis for judging whether the methods and results address the intended question. Review questions for this domain include whether the introduction states the pathogen's relevant biological features, whether the study objective distinguishes between outbreak investigation, population surveillance, resistance dissemination, or evolutionary dynamics, and whether the public health context is established with reference to current surveillance priorities. The World Health Organization antimicrobial resistance framework provides relevant context for manuscripts addressing resistance, and the review should assess whether the manuscript connects its findings to this broader public health problem. The review should also evaluate whether the study question is matched to an appropriate study design. A manuscript that claims to describe population-level resistance trends but uses a convenience sample of clinical isolates from a single hospital has a fundamental mismatch between objective and design. The review protocol should flag this mismatch as a major concern that requires either revision of the stated objective or acknowledgment of the design limitation. #### Domain Two: Methods Transparency and Reproducibility The second review domain evaluates whether the methods section provides sufficient detail for another laboratory to replicate the analysis. This domain addresses the common failure pattern of inadequate methods detail, which was identified as a significant weakness in published genomic epidemiology studies. Review questions for this domain include whether the isolate selection criteria are clearly stated, whether the sampling frame and time period are specified, whether the DNA extraction method and sequencing platform are identified, and whether the bioinformatics pipeline is described with software versions and parameter settings. The review should also assess whether quality control metrics are reported, including read depth, genome coverage, and assembly quality thresholds. The NCBI Pathogen Detection database provides a standardized framework for pathogen sequence analysis, and the review should assess whether the manuscript indicates whether sequences were submitted to public databases and whether they meet database quality standards. The review should also evaluate whether the manuscript describes the use of authenticated reference strains from culture collections such as the American Type Culture Collection for quality control purposes. For antimicrobial resistance findings, the review should assess whether the manuscript specifies the susceptibility testing methods and the standards used for interpretation. The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context, and the review should verify that the manuscript references the relevant standards without reproducing paywalled breakpoints. #### Domain Three: Results Presentation and Data Completeness The third review domain evaluates whether the results section presents the data in a way that supports the stated study objective. This domain addresses the common failure pattern of presenting phylogenetic trees or cluster analyses without sufficient supporting data for the reader to assess the strength of the findings. Review questions for this domain include whether the manuscript reports the number of isolates sequenced, their sources, and basic genomic characteristics, whether the distribution of sequence types or lineages is described, and whether the overall genetic diversity is characterized. The review should assess whether phylogenetic trees or other visualizations are accompanied by sufficient annotation to interpret the relationships shown. For cluster analyses, the review should evaluate whether the genetic distance threshold used to define relatedness is stated and justified. The review should also assess whether cluster characteristics are summarized in a way that allows readers to evaluate the epidemiological significance of the clusters, including the number of isolates per cluster, the time period, the geographic locations, and any epidemiological links. The review should evaluate whether antimicrobial resistance determinants are presented in a way that distinguishes between genes known to confer resistance, genes with uncertain significance, and mutations that may contribute to resistance. The review should flag any instances where the presence of a resistance gene is presented as definitive evidence of phenotypic resistance without supporting susceptibility data. #### Domain Four: Interpretation Validity and Caveats The fourth review domain evaluates whether the discussion interprets the findings with appropriate caution and acknowledges the limitations of the study. This domain addresses the common failure patterns of overinterpreting phylogenetic clusters and confusing genetic presence with phenotypic resistance. Review questions for this domain include whether the manuscript explicitly acknowledges that genomic relatedness does not automatically demonstrate direct transmission, whether the discussion considers the pathogen's mutation rate and population biology when interpreting genetic distances, and whether temporal and geographic considerations are addressed, including sampling bias. The review should assess whether the manuscript discusses the completeness of sampling and how missing isolates may have affected the interpretation of clusters and population structure. The review should also evaluate whether the manuscript acknowledges that different bioinformatics pipelines can produce different results from the same sequencing data and whether cross-laboratory comparisons require standardized approaches. For antimicrobial resistance findings, the review should assess whether the manuscript acknowledges the potential discordance between genotypic predictions and phenotypic resistance. The review should flag any instances where the manuscript overstates the clinical significance of resistance gene presence without phenotypic confirmation. #### Domain Five: Public Health Relevance and Actionability The fifth review domain evaluates whether the manuscript connects its findings to public health action and surveillance priorities. This domain addresses the question of whether the genomic epidemiology study has practical value beyond the specific dataset analyzed. Review questions for this domain include whether the manuscript discusses how the findings contribute to outbreak detection, resistance surveillance, or infection control, whether the manuscript frames its findings within the broader context of antimicrobial resistance surveillance and stewardship as emphasized by the World Health Organization, and whether the manuscript identifies specific public health actions that could be taken based on the findings. The review should also assess whether the manuscript discusses the generalizability of the findings and any factors that may limit their applicability to other settings. The wastewater surveillance data from 147 treatment plants across 40 states demonstrates how pathogen nucleic acid data can be used to study infectious disease epidemiology at a population level, and the review should assess whether the manuscript considers how its findings might contribute to broader surveillance efforts. ### Implementing the Review Protocol #### Step One: Prepare the Manuscript and Supporting Materials Before beginning the review, assemble the complete manuscript, all figures and tables, the bioinformatics pipeline documentation, and any supplementary materials. The review should be conducted against the full manuscript, beyond the main text, because important methods details are often placed in supplementary materials. Create a review worksheet that lists the five domains and the specific review questions for each domain. This worksheet serves as the structured record of the review and ensures that all domains are systematically evaluated. #### Step Two: Conduct the Domain-by-Domain Review Work through each domain in order, recording specific observations and identifying any concerns. For each review question, note whether the manuscript fully addresses the question, partially addresses it, or does not address it at all. Record specific examples from the manuscript to support each assessment. For the methods transparency domain, pay particular attention to whether software versions and parameter settings are reported. The systematic review of SARS-CoV-2 genomic epidemiology investigations found that standardized protocols and open-source tools were important enablers of early implementation, and manuscripts should provide sufficient detail for other laboratories to use the same tools and protocols. For the interpretation validity domain, pay particular attention to whether the manuscript distinguishes between genomic relatedness and direct transmission. This is the most common interpretive error in genomic epidemiology manuscripts and requires careful scrutiny. #### Step Three: Score Each Domain and Identify Priority Revisions Assign each domain a score based on the proportion of review questions that are fully addressed. A domain with all questions fully addressed receives a high score, while a domain with multiple unaddressed questions receives a low score. Prioritize revisions based on the domain scores and the severity of the identified concerns. Major concerns that affect the validity of the conclusions should be addressed before minor concerns about presentation or wording. The review worksheet should clearly distinguish between mandatory revisions and optional improvements. #### Step Four: Document the Review Outcomes Record the review outcomes in a structured format that can be shared with co-authors or used as a record of the manuscript development process. The review record should include the domain scores, the specific concerns identified, the recommended revisions, and the status of each revision. This review record serves as a quality management tool that demonstrates the rigor of the manuscript development process. It also provides a reference for future manuscripts, allowing the research team to identify recurring weaknesses and address them in the writing process. ### Common Review Findings and Recommended Actions #### Finding One: Insufficient Methods Detail The most common review finding is insufficient methods detail, particularly in the bioinformatics section. Manuscripts often state that sequencing was performed and that a bioinformatics pipeline was used, but they do not provide the software versions, parameter settings, or quality thresholds needed for replication. Recommended action: Expand the methods section to include all software tools with version numbers, all parameter settings, and all quality thresholds. If the manuscript is constrained by journal word limits, move detailed methods to supplementary materials and reference them clearly in the main text. #### Finding Two: Overinterpretation of Phylogenetic Clusters Another common finding is the overinterpretation of phylogenetic clusters as evidence of direct transmission. Manuscripts may state that isolates in the same cluster are part of the same outbreak without providing epidemiological evidence to support this conclusion. Recommended action: Revise the discussion to explicitly acknowledge that genomic relatedness does not demonstrate direct transmission. Add epidemiological context, including temporal and geographic information, to support any transmission inferences. If epidemiological data are not available, state this limitation clearly. #### Finding Three: Confusing Genotypic and Phenotypic Resistance Manuscripts that report antimicrobial resistance genes often present the presence of a resistance gene as definitive evidence of phenotypic resistance. This overinterpretation can mislead readers about the clinical significance of the findings. Recommended action: Revise the results and discussion to clearly distinguish between genotypic predictions and phenotypic susceptibility results. If phenotypic testing was performed, report the results and the standards used for interpretation. If phenotypic testing was not performed, state this limitation and avoid making claims about phenotypic resistance. #### Finding Four: Inadequate Sampling Bias Discussion Many genomic epidemiology manuscripts do not adequately discuss the limitations of their sampling strategy. Convenience samples that do not represent the full pathogen population can produce misleading conclusions about the prevalence of specific lineages or the structure of the pathogen population. Recommended action: Add a limitations section that describes the sampling frame, the inclusion and exclusion criteria, and the potential impact of sampling bias on the findings. Discuss the generalizability of the results in light of sampling completeness. #### Finding Five: Missing Public Health Context Some manuscripts present genomic findings without connecting them to public health action or surveillance priorities. These manuscripts may be technically sound but fail to demonstrate the practical value of the research. Recommended action: Revise the discussion to connect the findings to public health action, including outbreak detection, resistance surveillance, or infection control. Frame the findings within the broader context of antimicrobial resistance surveillance and stewardship as emphasized by the World Health Organization. ### Records and Measurements for the Review Protocol | Review Record | Specific Data | Purpose | | --- | --- | --- | | Domain Score Sheet | Scores for each of the five domains | Quantifies manuscript quality across domains | | Concern Log | Specific concerns with manuscript location | Tracks issues requiring revision | | Revision Status | Status of each recommended revision | Documents manuscript development progress | | Methods Checklist | Verification of software versions, parameters, thresholds | Ensures methods transparency | | Interpretation Checklist | Verification of caveats and limitations | Prevents overinterpretation | | Public Health Checklist | Verification of surveillance and action context | Ensures practical relevance | ### Professional Escalation Criteria for the Review Protocol #### When to Consult a Bioinformatics Specialist If the review identifies concerns about the bioinformatics pipeline that cannot be resolved by the manuscript authors, consult a bioinformatics specialist. This includes concerns about the appropriateness of the phylogenetic methods, the handling of recombination, or the interpretation of genetic distance thresholds. #### When to Consult an Epidemiologist If the review identifies concerns about the integration of genomic and epidemiological data, or if the interpretation of clusters requires epidemiological expertise, consult an epidemiologist. This includes concerns about case definitions, metadata completeness, and the validity of transmission inferences. #### When to Consult a Clinical Microbiologist If the review identifies concerns about the interpretation of antimicrobial resistance determinants or the connection between genotypic predictions and phenotypic resistance, consult a clinical microbiologist. This includes concerns about the clinical significance of specific resistance genes and the appropriate use of susceptibility testing standards. #### When to Consult a Biosafety Officer If the review identifies concerns about the description of laboratory biosafety practices, or if the manuscript describes work with pathogens requiring enhanced containment, consult a biosafety officer. The Biosafety in Microbiological and Biomedical Laboratories guidance from CDC and NIH provides the framework for risk assessment and containment decisions. ## Frequently Asked Questions ### What is the difference between genomic epidemiology and traditional epidemiology? Genomic epidemiology adds whole-genome sequencing data to traditional epidemiological methods, allowing researchers to track pathogen transmission at a higher resolution. Traditional epidemiology relies on case counts, contact tracing, and risk factor analysis, while genomic epidemiology uses genetic relatedness to confirm or refute transmission links and to identify the source of outbreaks. ### How do I choose between whole-genome sequencing and targeted amplicon sequencing? Whole-genome sequencing provides the highest resolution for phylogenetic analysis and resistance gene detection, while targeted amplicon sequencing focuses on specific genetic regions. The choice depends on the study question, the pathogen's genetic diversity, and the available resources. Whole-genome sequencing is preferred for outbreak investigations and population-level surveillance, while targeted approaches may be sufficient for specific resistance gene surveillance. ### What quality metrics should I report for sequencing data? Report read depth, genome coverage, assembly quality metrics, and any contamination checks performed. The NCBI Pathogen Detection database provides quality standards for pathogen sequences, and manuscripts should indicate whether their sequences meet these standards. Quality metrics allow readers to assess the reliability of the data and enable replication. ### How do I define a genomic cluster? A genomic cluster is defined by a genetic distance threshold, typically based on the number of single-nucleotide polymorphisms between isolates. The threshold should be selected based on the pathogen's mutation rate and the study question. The manuscript should state the threshold used and justify its selection. ### Can I conclude transmission from phylogenetic relatedness alone? No. Phylogenetic relatedness indicates that isolates share a recent common ancestor, but it does not demonstrate direct transmission. Transmission inference requires integration with epidemiological data, including temporal and geographic information and exposure histories. The manuscript should acknowledge this limitation. ### How should I handle antimicrobial resistance gene predictions? Report resistance genes and mutations identified in the genomes, but distinguish between genotypic predictions and phenotypic susceptibility results. The Clinical and Laboratory Standards Institute provides standards-based antimicrobial susceptibility testing context, and manuscripts should indicate whether phenotypic testing was performed. Avoid overinterpreting the presence of a resistance gene as definitive evidence of phenotypic resistance. ### What are the STROME-ID guidelines? STROME-ID guidelines are reporting recommendations for molecular epidemiology studies. The systematic review of SARS-CoV-2 genomic epidemiology investigations found that familiarity with these guidelines would have improved study quality. Bacterial genomics manuscripts should consider these guidelines when describing their methods and results. ### How do I address sampling bias in my manuscript? Acknowledge the sampling frame and its limitations in the discussion. Describe which isolates were included, which were excluded, and how this may have affected the findings. Discuss the generalizability of the results in light of sampling completeness. ## Related Bacteriology Guides - [Salmonella in Poultry: Pathogenesis, Epidemiology, and Public Health Implications](/knowledge/bacteria/avian-bacteria/salmonella-poultry-pathogenesis-epidemiology) - [Antimicrobial Resistance in Livestock-Associated Staphylococcus aureus: Genomic Epidemiology and One Health Implications](/knowledge/bacteria/livestock-bacteria/antimicrobial-resistance-livestock-associated-staphylococcus-aureus-genomic-epidemiology-one-health) - [Salmonellosis in Poultry: Public Health and Flock Management](/knowledge/bacteria/avian-bacteria/salmonellosis-in-poultry) - [Bacterial Carriage in Ducks: Pathogens of Public Health Concern](/knowledge/bacteria/avian-bacteria/bacteria-carried-by-ducks) - [Campylobacteriosis in Chickens: Food Safety and Public Health Implications](/knowledge/bacteria/avian-bacteria/campylobacteriosis-chickens-food-safety-public-health) ## References and Further Reading - [Biosafety in Microbiological and Biomedical Laboratories](https://www.cdc.gov/labs/bmbl/index.html). CDC and NIH. - [Microbiology Resources](https://asm.org/). American Society for Microbiology. - [Antimicrobial Resistance](https://www.who.int/health-topics/antimicrobial-resistance). World Health Organization. - [Bacteriological Analytical Manual](https://www.fda.gov/food/laboratory-methods-food/bacteriological-analytical-manual-bam). U.S. Food and Drug Administration. - [Antimicrobial Susceptibility Testing](https://clsi.org/standards/products/microbiology). Clinical and Laboratory Standards Institute. - [Culture Collections](https://www.atcc.org/). American Type Culture Collection. - [NCBI Pathogen Detection](https://www.ncbi.nlm.nih.gov/pathogens). National Center for Biotechnology Information. - [Expanded molecular evidence of soil-transmitted helminth and Schistosoma spp. infections in Myanmar schoolchildren: A qPCR update.](https://doi.org/10.1371/journal.pntd.0014384). 2026. - [Local genomic epidemiology investigations of SARS-CoV-2 during the early pandemic response: A global systematic review.](https://doi.org/10.1017/s0950268826101381). 2026. - [Pathogen nucleic acids data in wastewater solids from 147 treatment plants in the United States: 2024-2025.](https://doi.org/10.1016/j.dib.2026.112503). 2026. - [<,i>,Taenia lynciscapreoli<,/i>, in Eurasian Lynx: New Taeniid Record for Romania.](https://doi.org/10.3390/pathogens15050468). 2026. > This article is educational and does not replace validated laboratory procedures, clinical diagnosis, institutional biosafety review, or public-health guidance.