Phylogenetic Inference from Core Genomes

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

Quick Answer

• Choose maximum likelihood with IQ-TREE or RAxML for large core genome alignments when computational speed and model testing are your primary constraints.
• Choose Bayesian inference with BEAST when you need calibrated divergence times, relaxed molecular clocks, or explicit prior distributions on evolutionary parameters.
• Both approaches require careful model selection and convergence assessment, and neither method can recover true phylogeny from alignment artifacts or recombination without additional analysis.

At a Glance

Core Genome Alignment as the Foundation

Core genome phylogenetic inference begins with the alignment of orthologous genes shared across all analyzed bacterial isolates. The core genome represents the set of genes present in every genome within a study population, excluding accessory genes that vary in presence or absence across strains. This distinction matters because the core genome provides a stable framework for evolutionary comparisons, while accessory genome content can reflect recent horizontal gene transfer or lineage-specific adaptations.

The construction of a core genome alignment requires several sequential steps. First, genome assemblies must be quality checked for completeness and contamination. Second, orthologous gene prediction identifies shared genes across all isolates. Third, individual gene alignments are generated and then concatenated into a single supermatrix. The final alignment length depends on the number of core genes and the average gene length, which for bacterial genomes typically ranges from several hundred thousand to several million nucleotide positions.

The quality of the core genome alignment directly determines the reliability of downstream phylogenetic inference. Misaligned regions, poorly annotated genes, or incomplete assemblies introduce systematic errors that can mislead both maximum likelihood and Bayesian analyses. Researchers should therefore assess alignment quality using metrics such as the proportion of variable sites, the distribution of gaps, and the consistency of gene content across isolates.

The choice between whole-genome alignment and core genome alignment represents a fundamental tradeoff. Whole-genome alignments include all conserved regions, including intergenic sequences, and can provide more information for closely related strains. Core genome alignments focus on protein-coding genes, which are generally more conserved and easier to align unambiguously. For bacterial species with substantial genomic plasticity, core genome alignments often produce more reliable phylogenetic signal than whole-genome approaches.

Maximum Likelihood Phylogenetic Inference

Maximum likelihood methods evaluate the probability of observing the aligned sequence data given a specific tree topology, branch lengths, and substitution model parameters. The method searches for the tree that maximizes this likelihood value, providing a statistical framework for comparing alternative hypotheses about evolutionary relationships.

IQ-TREE Implementation

IQ-TREE has become a widely used maximum likelihood program for bacterial phylogenomics because of its computational efficiency and integrated model selection. The program implements the ModelFinder algorithm, which automatically evaluates multiple substitution models and selects the best-fitting one based on information criteria such as the Bayesian Information Criterion. This automated model selection reduces the risk of using an inappropriate substitution model, which can lead to biased tree topologies and inaccurate branch lengths.

For core genome alignments, IQ-TREE offers several practical advantages. The program supports multicore processing and can analyze alignments with hundreds of thousands of sites within reasonable timeframes. The ultrafast bootstrap approximation provides rapid support values for internal branches, although researchers should verify that the approximation produces results consistent with standard bootstrap analysis for their specific dataset.

The standard IQ-TREE workflow begins with the alignment file in FASTA or PHYLIP format. The command typically specifies the substitution model, the number of bootstrap replicates, and the number of CPU threads. For large bacterial datasets, the researcher should consider partitioning the alignment by gene, which allows different substitution models for different genes and can improve the fit of the model to the data.

RAxML Implementation

RAxML remains a standard for maximum likelihood phylogenetic inference, particularly for large alignments. The program implements the general time-reversible model with rate heterogeneity, which is often appropriate for bacterial core genome alignments. RAxML uses a rapid bootstrap algorithm that is computationally efficient and provides reliable support values for most datasets.

The choice between IQ-TREE and RAxML depends on the specific requirements of the analysis. IQ-TREE offers more flexible model selection and faster runtimes for many datasets. RAxML provides a well-established pipeline with extensive documentation and a long history of use in bacterial phylogenomics. Both programs produce trees that can be visualized and interpreted using standard phylogenetic software.

For core genome alignments with thousands of genomes, the computational demands of maximum likelihood analysis become significant. The researcher should consider whether the entire core genome alignment is necessary or whether a subset of informative sites can provide sufficient resolution. Some studies use only the variable sites in the core genome, which reduces the computational burden while retaining most of the phylogenetic signal.

Bayesian Phylogenetic Inference

Bayesian inference approaches phylogenetic reconstruction as a problem of estimating the posterior distribution of trees and parameters given the observed data and a prior distribution. The method uses Markov chain Monte Carlo (MCMC) sampling to approximate the posterior distribution, providing a measure of uncertainty for each branch and parameter.

BEAST Implementation

BEAST is the most widely used Bayesian phylogenetic program for bacterial genomics. The program implements a range of evolutionary models, including strict and relaxed molecular clocks, and can estimate divergence times when calibration information is available. BEAST is particularly valuable for phylodynamic studies that combine phylogenetic inference with epidemiological data.

The BEAST workflow requires the user to specify the substitution model, the clock model, and the tree prior. The choice of tree prior is important because it influences the estimated divergence times and the shape of the posterior distribution. The coalescent model is commonly used for bacterial populations, while the birth-death model may be more appropriate for datasets that include multiple species.

The MCMC analysis in BEAST requires careful attention to convergence. The researcher must run the chain long enough to obtain a sufficient effective sample size for all parameters, typically above 200. The program provides diagnostic tools to assess convergence, including trace plots and the effective sample size statistic. Multiple independent runs should be performed to ensure that the results are reproducible.

Calibration and Divergence Time Estimation

Bayesian methods provide a natural framework for estimating divergence times when calibration information is available. Calibration can be based on known isolation dates for samples, which is common in bacterial phylodynamics, or on fossil or geological evidence for deeper evolutionary events. The choice of calibration points and the prior distributions assigned to them can substantially affect the estimated divergence times.

For bacterial pathogens, tip calibration using the isolation dates of sequenced strains is a common approach. This method, known as tip dating, uses the sampling times of the genomes to calibrate the molecular clock. The approach requires accurate metadata for each genome, including the date and location of isolation. The quality of the metadata directly affects the reliability of the divergence time estimates.

The relaxed molecular clock model allows the substitution rate to vary across branches of the tree, which is often more realistic than a strict clock that assumes a constant rate. BEAST implements several relaxed clock models, including the uncorrelated lognormal and the uncorrelated exponential models. The choice of clock model should be informed by the data and the biological context of the study.

Model Selection and Testing

The choice of the substitution model is a critical step in both maximum likelihood and Bayesian phylogenetic inference. The model describes the relative rates of different nucleotide substitutions and the degree of rate heterogeneity across sites. Using an inappropriate model can lead to biased branch lengths and incorrect tree topologies.

Substitution Model Families

The general time-reversible model is the most flexible substitution model commonly used in bacterial phylogenetics. It allows different rates for each pair of nucleotides and is often combined with a gamma distribution to account for rate variation across sites. The simpler models, such as the Hasegawa-Kishino-Yano model, assume equal rates for transitions and transversions and may be sufficient for closely related strains.

The model selection process evaluates the fit of different models to the data and identifies the model that provides the best balance between complexity and goodness of fit. The information criteria, such as the Akaike Information Criterion and the Bayesian Information Criterion, penalize models with more parameters to avoid overfitting. The ModelFinder algorithm in IQ-TREE automates this process and is a practical choice for most bacterial datasets.

Partitioning Strategies

The core genome alignment is a concatenation of individual gene alignments, and the substitution process may differ among genes. Partitioning the alignment by gene allows each partition to have its own substitution model and rate parameters. This approach can improve the model fit and produce more accurate phylogenetic estimates.

The number of partitions should be balanced against the computational cost. Each partition adds parameters to the model, and the analysis becomes more complex as the number of partitions increases. The researcher should evaluate the improvement in model fit against the additional computational burden. The partition scheme can be based on gene boundaries, codon positions, or a combination of both.

Model Validation

After the phylogenetic analysis is complete, the researcher should validate the model assumptions and the robustness of the results. The bootstrap support values or posterior probabilities provide a measure of the statistical support for each branch. The researcher should also examine the distribution of the branch lengths and the overall tree shape to identify any anomalies.

The model validation should include a comparison of the results from different methods or different model settings. If the maximum likelihood and Bayesian analyses produce conflicting topologies, the researcher should investigate the cause of the discrepancy. The conflict may indicate a problem with the alignment, the model, or the data.

Computational Considerations for Large Datasets

The analysis of large bacterial core genome alignments presents significant computational challenges. The number of genomes in a typical bacterial phylogenomic study has increased substantially with the availability of whole-genome sequencing, and the alignment size can reach millions of sites. The computational resources required for the analysis must be planned carefully.

Memory and Runtime Requirements

The maximum likelihood programs IQ-TREE and RAxML have different memory and runtime requirements. IQ-TREE is generally more memory efficient and can handle larger alignments with less memory. RAxML has a longer runtime for large datasets but is well optimized for the maximum likelihood search.

The Bayesian analysis with BEAST is computationally intensive and requires long runtimes to achieve convergence. The MCMC chain must be run for millions of generations, and the analysis can take days or weeks for large datasets. The researcher should allocate sufficient computational resources and plan the analysis timeline accordingly.

Parallelization and High-Performance Computing

Both IQ-TREE and RAxML support parallel processing across multiple CPU cores. The analysis of large datasets can be accelerated by using a high-performance computing cluster. The researcher should be aware of the memory requirements of the analysis and the available resources on the cluster.

BEAST also supports parallel processing through the use of multiple chains and the BEAGLE library for the likelihood computation. The BEAGLE library can significantly accelerate the analysis by using graphics processing units (GPUs) for the likelihood calculations. The researcher should consider the availability of GPUs when planning the Bayesian analysis.

Data Reduction Strategies

When the dataset is too large for a full analysis, the researcher can consider data reduction strategies. The core genome alignment can be filtered to remove invariant sites, which do not contribute to the phylogenetic signal. The alignment can also be reduced to a subset of the most informative genes or sites.

The data reduction should be performed carefully to avoid losing important phylogenetic signal. The researcher should assess the impact of the reduction on the resolution of the tree and the support values. The reduced dataset should still provide sufficient information to answer the research question.

Quality Controls and Validation

The reliability of the phylogenetic inference depends on the quality of the input data and the correctness of the analysis. The researcher should implement quality controls at each stage of the workflow to identify and correct errors.

Alignment Quality Assessment

The core genome alignment should be checked for the presence of misaligned regions, which can introduce errors in the phylogenetic analysis. The alignment can be inspected visually using alignment viewers, and the quality can be assessed using metrics such as the proportion of gaps and the conservation of the alignment.

The alignment should also be checked for the presence of recombination, which can create conflicting phylogenetic signals. Recombination can be detected using programs that identify the breakpoints and the regions of the genome that have been exchanged. The recombination can be removed from the alignment or the analysis can be performed on the recombination-free regions.

Convergence Assessment for Bayesian Analyses

The Bayesian analysis requires a careful assessment of the convergence of the MCMC. The researcher should examine the trace plots of the parameters and the effective sample size for each parameter. The effective sample size should be above 200 for the reliable estimation of the posterior distribution.

The convergence can also be assessed by comparing the results of multiple independent runs. The runs should produce similar posterior distributions and the same tree topology. The researcher should also examine the potential scale reduction factor, which should be close to 1 for all parameters.

Support Value Interpretation

The support values for the branches of the tree should be interpreted in the context of the analysis. The bootstrap support values from maximum likelihood analysis and the posterior probabilities from Bayesian analysis have different interpretations. The bootstrap support is a measure of the reproducibility of the branch under resampling, while the posterior probability is the probability that the branch is correct given the model and the data.

The support values should be interpreted with caution, as high support does not guarantee the correctness of the tree. The support values can be inflated by the use of an inappropriate model or by the presence of systematic errors in the data. The researcher should consider the support values in the context of the biological plausibility of the tree.

Common Failure Patterns in Phylogenetic Inference

The phylogenetic inference can fail in several ways, and the researcher should be aware of the common failure patterns and how to address them.

Long Branch Attraction

Long branch attraction is a systematic error that can cause the tree to be incorrectly reconstructed. The error occurs when the long branches of the tree are incorrectly grouped together, even when they are not closely related. The error is more likely to occur when the model is misspecified or when the data contains a high degree of rate heterogeneity.

The long branch attraction can be detected by comparing the results of the maximum likelihood and Bayesian analyses. The Bayesian analysis is less susceptible to the error because it integrates over the model parameters. The researcher can also use the model that accounts for the rate heterogeneity to reduce the error.

Model Misspecification

The model misspecification occurs when the substitution model does not adequately describe the evolutionary process. The model misspecification can lead to biased tree topologies and branch lengths. The researcher should use the model selection to choose the best-fitting model and should test the sensitivity of the results to the model choice.

The model misspecification can be detected by comparing the results of the different models. The researcher should also examine the residuals of the model fit to identify the regions of the alignment that are poorly described by the model.

Incomplete Lineage Sorting

Incomplete lineage sorting occurs when the gene trees differ from the species tree due to the retention of ancestral polymorphism. The incomplete lineage sorting can cause the phylogenetic tree to be incorrectly reconstructed, particularly for closely related species. The researcher should consider the use of the coalescent-based methods that account for the incomplete lineage sorting.

The incomplete lineage sorting can be detected by comparing the gene trees of the individual genes to the species tree. The researcher can also use the methods that estimate the species tree from the gene trees.

Biosafety and Data Management

The phylogenetic analysis of bacterial genomes involves the handling of the biological data and the use of the computational resources. The researcher should follow the biosafety guidelines for the handling of the bacterial strains and the genomic data.

Biosafety Considerations

The bacterial strains used in the phylogenetic analysis should be handled according to the biosafety guidelines. The guidelines provide the risk assessment and the containment for the handling of the biological materials. The researcher should follow the guidelines for the handling of the bacterial strains and the decontamination of the laboratory.

The Biosafety in Microbiological and Biomedical Laboratories provides the guidelines for the safe handling of the biological materials. The researcher should follow the guidelines for the risk assessment and the containment of the bacterial strains.

Data Management and Security

The genomic data used in the phylogenetic analysis should be managed securely and in accordance with the relevant regulations. The data should be stored in a secure repository and the access should be controlled. The researcher should also ensure that the data is properly annotated and the metadata is accurate.

The American Type Culture Collection provides the authenticated biological materials and the strain information that can be used for the quality control of the analysis. The researcher should use the authenticated strains for the validation of the analysis.

Practical Workflow for Core Genome Phylogenetic Inference

The following workflow provides a practical approach to the core genome phylogenetic inference for bacterial genomes. The workflow is designed to be used by the researchers and the laboratory professionals.

Step 1: Data Collection and Quality Assessment

The first step is to collect the bacterial genome sequences and the associated metadata. The genomes should be of high quality and the metadata should be accurate. The quality of the genomes should be assessed using the standard metrics, such as the completeness and the contamination.

The genomes should be annotated and the core genome should be identified. The core genome can be identified using the orthology clustering tools. The core genome alignment should be generated and the quality of the alignment should be assessed.

Step 2. Model Selection and Phylogenetic Inference

The next step is to select the substitution model and the phylogenetic inference method. The model selection can be performed using the ModelFinder algorithm in IQ-TREE. The maximum likelihood analysis can be performed using IQ-TREE or RAxML.

The Bayesian analysis can be performed using BEAST. The BEAST analysis requires the specification of the substitution model, the clock model, and the tree prior. The MCMC should be run long enough to achieve convergence.

Step 3. Validation and Interpretation

The final step is to validate the results and interpret the phylogenetic tree. The support values should be examined and the tree should be compared to the known biological context. The results should be reported in a clear and reproducible manner.

The researcher should also consider the limitations of the analysis and the potential sources of error. The results should be interpreted in the context of the biological question and the available evidence.

Records and Measurements

The phylogenetic analysis should be documented in a manner that allows the reproducibility of the results. The following records should be maintained:

• The version of the software and the parameters used for the analysis
• The input data and the alignment files
• The model selection results and the chosen model
• The convergence assessment and the effective sample size values
• The output tree and the support values

The records should be stored in a secure repository and the access should be controlled. The records should be maintained for the duration of the research project and the publication of the results.

Professional Escalation Criteria

The researcher should seek the professional advice when the analysis produces unexpected results or when the analysis fails to converge. The following criteria should be used to escalate the issue:

• The MCMC fails to converge after multiple attempts
• The maximum likelihood and Bayesian analyses produce conflicting topologies
• The support values are low for the branches of the tree
• The alignment contains a high proportion of ambiguous sites

The researcher should consult with the colleagues or the bioinformatics support team for the advice. The researcher should also consider the use of the alternative methods or the additional data to resolve the issue.

A Practical Decision Framework for Method Selection

The choice between maximum likelihood and Bayesian inference for core genome alignments is not a single decision but a sequence of decisions that depend on the research question, dataset characteristics, and available computational resources. This section provides a structured framework that researchers can apply before starting an analysis, with explicit criteria for method selection and a record system for documenting the decision process.

Decision Point 1: Define the Primary Analytical Objective

The first decision determines whether the analysis requires only a tree topology or whether it requires additional evolutionary parameters. This distinction separates most bacterial phylogenomic studies into two categories.

For studies that need a tree topology with branch support values, maximum likelihood methods are the appropriate starting point. This category includes routine surveillance studies, strain typing investigations, and comparative genomics projects where the goal is to understand the relationships among isolates. The output of these analyses is a tree that can be visualized, annotated, and compared across studies.

For studies that need divergence time estimates, substitution rates, or population dynamic parameters, Bayesian methods are required. This category includes phylodynamic studies, molecular clock analyses, and investigations of epidemic history. The output of these analyses includes a posterior distribution of trees and parameter estimates that provide measures of uncertainty.

The researcher should write down the primary analytical objective before proceeding. If the objective is not clear, the analysis should not begin until the question is refined. A tree produced without a clear objective often fails to address the biological question and may require reanalysis with a different method.

Decision Point 2: Assess Dataset Size and Computational Budget

The second decision evaluates the computational feasibility of each method for the specific dataset. The researcher should estimate the alignment length, the number of genomes, and the available computational resources before selecting a method.

For alignments with more than 500 genomes or more than one million sites, maximum likelihood methods are the practical choice. IQ-TREE can analyze these datasets within hours or days on a standard server, while RAxML requires more time but remains feasible. The computational cost of Bayesian analysis for datasets of this size is often prohibitive, with MCMC runs requiring weeks or months to achieve convergence.

For alignments with fewer than 200 genomes, both methods are computationally feasible. The researcher can choose based on the analytical objective and the need for divergence time estimates. For datasets between 200 and 500 genomes, the researcher should estimate the runtime of each method using a small test run before committing to the full analysis.

The computational budget should include the time for model selection, the main analysis, and the convergence assessment. The researcher should also consider the memory requirements of each method. IQ-TREE generally requires less memory than RAxML for the same dataset, and both require less memory than BEAST.

Decision Point 3: Evaluate the Need for Calibration and Temporal Information

The third decision determines whether the analysis requires calibration information. Calibration is the process of assigning dates to specific nodes in the tree, which allows the estimation of divergence times and substitution rates.

If the study requires divergence time estimates, the researcher must use a Bayesian method with a molecular clock model. The BEAST program provides the framework for this analysis, with options for strict and relaxed clocks. The researcher must have reliable calibration information, such as isolation dates for the samples or known historical events.

If the study does not require divergence time estimates, the researcher can use maximum likelihood methods. The tree topology and branch lengths from a maximum likelihood analysis provide relative evolutionary distances, but these distances cannot be converted to absolute time without additional calibration.

The researcher should also consider whether the dataset has sufficient temporal signal for a molecular clock analysis. The temporal signal can be assessed using a regression of root-to-tip distances against sampling dates. If the temporal signal is weak, the divergence time estimates will have wide confidence intervals and may not be reliable.

Decision Point 4: Determine the Need for Statistical Uncertainty

The fourth decision determines whether the analysis requires a measure of statistical uncertainty for the tree and the parameters. The Bayesian method provides a posterior distribution that directly quantifies the uncertainty in the tree topology and the parameter estimates. The maximum likelihood method provides bootstrap support values, which are a measure of the reproducibility of the branches under resampling.

The Bayesian approach is appropriate when the researcher needs to make probabilistic statements about the tree or the parameters. For example, the researcher may need to state the probability that a specific clade is correct or the probability that a divergence event occurred within a specific time interval. These statements require the posterior distribution from a Bayesian analysis.

The maximum likelihood approach is appropriate when the researcher needs a point estimate of the tree with support values. The bootstrap support values provide a measure of the confidence in each branch, but they do not provide a direct probability statement. The researcher should be aware that bootstrap support values can be inflated by model misspecification and should interpret them with caution.

Decision Point 5: Consider the Need for Model Comparison

The fifth decision determines whether the researcher needs to compare the fit of different models to the data. The maximum likelihood method provides a framework for model comparison using information criteria, such as the Akaike Information Criterion and the Bayesian Information Criterion. The ModelFinder algorithm in IQ-TREE automates this process and identifies the best-fitting model from a set of candidate models.

The Bayesian method provides a framework for model comparison using the Bayes factor, which is the ratio of the marginal likelihoods of two models. The Bayes factor can be used to compare different substitution models, clock models, and tree priors. The calculation of the Bayes factor is computationally intensive and requires the estimation of the marginal likelihood for each model.

The researcher should consider whether the model comparison is a primary objective of the study or a secondary step in the analysis. If the model comparison is a primary objective, the maximum likelihood approach is more practical because the model selection is automated and computationally efficient. If the model comparison is a secondary step, the researcher can use the maximum likelihood approach for the model selection and then use the chosen model for the Bayesian analysis.

Decision Matrix for Method Selection

The following decision matrix summarizes the method selection based on the five decision points. The researcher should score each decision point and use the matrix to select the appropriate method.

The researcher should use the matrix as a starting point and adjust the decision based on the specific requirements of the study. The matrix is not a substitute for careful consideration of the research question and the data.

Record System for the Decision Process

The decision process should be documented in a manner that allows the reproducibility of the analysis. The following records should be maintained for each analysis:

• The research question and the analytical objective
• The dataset characteristics, including the number of genomes and the alignment length
• The computational resources available and the estimated runtime for each method
• The calibration data and the source of the calibration information
• The model selection results and the chosen model
• The method selection and the rationale for the choice

The records should be stored in a secure repository and the access should be controlled. The records should be maintained for the duration of the research project and the publication of the results.

Troubleshooting the Decision Process

The decision process can fail in several ways, and the researcher should be aware of the common failure patterns and how to address them.

Failure Pattern 1: The dataset is too large for the chosen method. The researcher may choose a Bayesian method for a dataset with more than 500 genomes and find that the analysis does not converge within a reasonable time. The solution is to reduce the dataset size or switch to a maximum likelihood method. The researcher should also consider data reduction strategies, such as removing invariant sites or selecting a subset of informative genes.

Failure Pattern 2: The calibration data is insufficient. The researcher may choose a Bayesian method for divergence time estimation and find that the temporal signal is weak. The solution is to assess the temporal signal before the analysis and to consider alternative calibration strategies. The researcher should also consider the use of a relaxed clock model to account for rate variation across branches.

Failure Pattern 3: The model selection is inconsistent. The researcher may find that the model selection results differ between the maximum likelihood and Bayesian analyses. The solution is to compare the model selection results and to investigate the cause of the discrepancy. The discrepancy may indicate a problem with the alignment or the data.

Failure Pattern 4: The computational resources are insufficient. The researcher may find that the analysis requires more memory or runtime than the available resources. The solution is to use a high-performance computing cluster or to reduce the dataset size. The researcher should also consider the use of parallel processing and the BEAGLE library for the Bayesian analysis.

Professional Escalation Criteria for the Decision Process

The researcher should seek professional advice when the decision process produces unexpected results or when the analysis fails to proceed. The following criteria should be used to escalate the issue:

• The dataset characteristics are not consistent with the chosen method
• The calibration data is not available or is of poor quality
• The computational resources are insufficient for the chosen method
• The model selection results are inconsistent across methods

The researcher should consult with colleagues or the bioinformatics support team for advice. The researcher should also consider the use of alternative methods or the additional data to resolve the issue.

Integration with Laboratory Practice

The decision framework should be integrated with the laboratory practice and the quality management system. The researcher should follow the professional microbiology education and laboratory practice guidelines from the American Society for Microbiology for the handling of the bacterial strains and the genomic data. The researcher should also follow the standards-based antimicrobial susceptibility testing context from the Clinical and Laboratory Standards Institute for the quality control of the analysis.

The researcher should also consider the global context of the antimicrobial resistance and the public-health implications of the analysis. The World Health Organization provides the definitions and the surveillance context for the antimicrobial resistance. The researcher should consider the public-health context when interpreting the results of the phylogenetic analysis.

The decision framework should be documented in the laboratory manual and the standard operating procedures. The documentation should include the decision matrix, the record system, and the troubleshooting methods. The laboratory should also provide the training for the researchers and the laboratory professionals on the use of the decision framework.

Frequently Asked Questions

What is the difference between maximum likelihood and Bayesian phylogenetic inference?

Maximum likelihood inference finds the tree that maximizes the probability of the observed data under a given model. Bayesian inference estimates the posterior distribution of trees and parameters using MCMC sampling, providing a measure of uncertainty for each branch.

How do I choose between IQ-TREE and RAxML for my core genome alignment?

IQ-TREE offers integrated model selection and is generally faster for large datasets. RAxML is a mature program with a long history of use. The choice depends on the specific requirements of the analysis, including the dataset size and the need for model selection.

When should I use BEAST for bacterial phylogenetics?

BEAST is appropriate when you need to estimate divergence times, use a molecular clock, or incorporate calibration data. It is also useful for phylogeographic analyses that combine phylogenetic and epidemiological data.

What is the role of model selection in phylogenetic inference?

Model selection identifies the substitution model that best fits the data while avoiding overfitting. Using an inappropriate model can lead to biased tree topologies and branch lengths.

How can I assess the convergence of a Bayesian MCMC analysis?

Examine the trace plots and the effective sample size for each parameter. The effective sample size should be above 200. Run multiple independent chains and compare the results to ensure consistency.

What are the common sources of error in core genome phylogenetic inference?

Common sources of error include misalignment, model misspecification, long branch attraction, and incomplete lineage sorting. These errors can be detected by comparing results from different methods and by assessing the quality of the alignment.

How should I handle large bacterial genome datasets?

Consider using data reduction strategies, such as removing invariant sites or selecting a subset of informative genes. Use parallel processing and high-performance computing resources to accelerate the analysis.

What quality controls should I implement for the phylogenetic analysis?

Check the alignment quality, assess the convergence of the Bayesian analysis, and interpret the support values in the context of the model. Use authenticated strains from culture collections for validation.

Related Bacteriology Guides

• Bacterial Contamination of Poultry: Salmonella and E. coli
• Bacterial Diseases of Chickens: A Comprehensive Overview
• Bacterial Growth Dynamics in Chicken: From Farm to Refrigeration
• Bacterial Pathogens in Poultry: Comprehensive Clinical Reference
• Bacterial Poultry Diseases: Comprehensive Overview and Classification

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.
• Genome distance and phylogenetic inference accommodating gene duplication, loss and new gene input.. Molecular phylogenetics and evolution, 2023.
• Fusarium pathogenomics.. Annual review of microbiology, 2013.
• IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies.. Molecular biology and evolution, 2015.
• Resolving Phylogenetic Relationships for Streptococcus mitis and Streptococcus oralis through Core- and Pan-Genome Analyses.. Genome biology and evolution, 2019.

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