How to build a phylogenetic tree from whole genome sequences

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

Quick Answer

• Build a phylogenetic tree from whole genome sequences by moving through read quality control, assembly or mapping, variant calling, alignment, and tree inference with tools such as FastQC, Trimmomatic, SPAdes, Prokka, Roary, and IQ-TREE.
• Choose a reference-based approach for closely related bacterial isolates and a reference-free approach for diverse or novel genomes, then validate the tree with bootstrap support values.
• The main limitation is that phylogenetic inference depends on the quality of input data, so poor assemblies or contaminated reads produce misleading trees regardless of the inference method used.

At a Glance

Understanding Phylogenetic Trees in Bacterial Genomics

A phylogenetic tree is a branching diagram that represents the inferred evolutionary relationships among bacterial isolates based on their genetic sequences. In bacterial genomics, researchers construct these trees to understand how strains are related, track transmission chains, identify outbreaks, and study the emergence of antimicrobial resistance. The World Health Organization identifies antimicrobial resistance as a global public health concern, and genomic surveillance of resistant bacteria depends on accurate phylogenetic reconstruction to trace the spread of resistance determinants across populations.

The fundamental principle behind phylogenetic tree construction is that organisms with more similar genetic sequences share a more recent common ancestor. Bacterial genomes evolve through the accumulation of point mutations, insertions, deletions, and recombination events. When you compare whole genome sequences from multiple isolates, the differences and similarities in their DNA sequences provide the signal needed to reconstruct their evolutionary history.

For bacterial genomes, the scale of analysis matters. A single bacterial genome contains millions of base pairs, and the number of differences between closely related isolates may be small. This means that the methods you choose for each step of the pipeline must be sensitive enough to detect true biological variation while robust enough to avoid introducing artifacts from sequencing errors or assembly mistakes.

The practical outcome of building a phylogenetic tree is a visual representation of relationships that you can use to make decisions. In a clinical or public health setting, the tree can show whether a group of patients is infected with the same bacterial strain, which supports outbreak investigations. In a research setting, the tree can reveal how a bacterial species has evolved over time or how it has acquired new traits such as antimicrobial resistance genes.

Core Principles of Phylogenetic Inference

The Relationship Between Sequence Data and Evolutionary History

Phylogenetic inference relies on the assumption that the genetic sequences you compare are homologous, meaning they share a common evolutionary origin. When you align homologous sequences, the differences between them reflect the mutations that have accumulated since they diverged from a common ancestor. The more mutations that have accumulated, the longer the time since divergence, and the more distantly related the isolates are.

For bacterial genomes, the evolutionary signal is distributed across the entire genome. Some regions of the genome evolve more slowly because they encode essential functions, while other regions evolve more rapidly because they are under less selective pressure. When you build a phylogenetic tree, you are making a choice about which parts of the genome to include in the analysis. This choice has a direct effect on the resulting tree.

Core Genome Versus Whole Genome Analysis

A core genome is the set of genes that are present in all isolates being compared. The accessory genome is the set of genes that are present in some isolates but not others. When you build a phylogenetic tree from the core genome, you are comparing the shared genetic content of the isolates. This approach reduces the noise from gene presence and absence differences and focuses on the single nucleotide changes that accumulate in shared genes.

A whole genome approach includes both the core and accessory genome. This can provide more information, but it also introduces complications. The accessory genome can be affected by horizontal gene transfer, which can obscure the vertical evolutionary signal. For example, if one isolate has acquired a large genomic island from an unrelated species, that region will appear to be very different from the same region in other isolates, even though the isolates are closely related.

For most bacterial phylogenetic analyses, the core genome approach is preferred because it provides a more reliable signal of the evolutionary relationships among isolates. The decision to use a whole genome approach should be based on the specific research question. If you are interested in the distribution of accessory genes, such as antimicrobial resistance genes, you can map those genes onto a core genome tree.

Substitution Models and Their Role

A substitution model describes the relative rates at which different types of nucleotide changes occur. For example, a transition, which is a change from a purine to another purine or a pyrimidine to another pyrimidine, is often more common than a transversion, which is a change from a purine to a pyrimidine or the reverse. The substitution model you choose affects the calculation of the genetic distance between sequences and therefore the branch lengths in the tree.

The simplest substitution model is the Jukes-Cantor model, which assumes that all nucleotide changes occur at the same rate. More complex models, such as the General Time Reversible model, allow different rates for different types of changes. The choice of model is often made by the software based on the data, but you can also specify a model manually.

For bacterial whole genome sequences, the number of variable sites is often large, and the choice of substitution model has a smaller effect on the tree topology than it does on the branch lengths. However, the model choice can still affect the support values for the branches, so it is important to use a model that fits the data well.

Practical Workflow for Building a Phylogenetic Tree

The workflow for building a phylogenetic tree from whole genome sequences can be divided into several distinct stages. Each stage has its own inputs, outputs, and quality checks. The following sections describe each stage in the order you would perform them.

Step 1: Read Quality Control

The first step in the pipeline is to assess the quality of the raw sequencing reads. Sequencing platforms produce reads with associated quality scores that indicate the probability of an error at each base position. Low quality reads can introduce errors into the assembly or the variant calls, which will then propagate into the phylogenetic tree.

The quality control process begins with a tool such as FastQC, which generates a report for each read file. The report includes metrics such as the per-base quality score, the GC content, the sequence length distribution, and the presence of adapter sequences. You should review these metrics for each sample before proceeding.

The next step is to trim the reads to remove low quality bases and adapter sequences. Trimming tools such as Trimmomatic use a sliding window approach to identify regions of the read that fall below a quality threshold and remove them. The trimming parameters you choose depend on the sequencing platform and the quality of the data. A common approach is to trim bases with a quality score below 20 and to remove reads that become shorter than 36 bases after trimming.

After trimming, you should run FastQC again to confirm that the quality has improved. The output of this stage is a set of trimmed read files that are ready for assembly or mapping.

Step 2: Assembly or Reference Mapping

The next decision is whether to assemble the reads into a genome or to map the reads to a reference genome. This decision depends on the availability of a closely related reference genome and the research question.

Reference-Based Mapping

If a high quality reference genome is available, you can map the reads to the reference and identify variants. This approach is faster and requires less computational resources than assembly. The output of the mapping is a set of variant calls that can be used to build a phylogenetic tree.

The mapping is performed with tools such as BWA-MEM. The reads are aligned to the reference genome, and the alignment is processed to identify single nucleotide variants and insertions and deletions. The variant calls are then used to construct a multiple sequence alignment of the variable sites.

The main limitation of the reference-based approach is that it is biased toward the reference genome. If the isolates are distantly related to the reference, the reads may not map well, and the variant calls will be incomplete. This approach is best used when the isolates are closely related to the reference, such as in an outbreak investigation.

Reference-Free Assembly

If no suitable reference genome is available, or if you want to analyze the entire genome without reference bias, you can assemble the reads into a genome. The assembly process uses tools such as SPAdes or KMPLER to construct a set of contiguous sequences, called contigs, from the reads.

The quality of the assembly is critical for the downstream analysis. A fragmented assembly with many small contigs can make it difficult to identify orthologous genes and can introduce errors into the alignment. You should assess the assembly quality using metrics such as the number of contigs, the N50 value, and the total assembly length.

After assembly, you can annotate the genome to identify the genes. The annotation process uses tools such as Prokka to identify open reading frames and assign functions to the genes. The annotation is required for the core genome analysis.

Step 3: Genome Annotation

Genome annotation is the process of identifying the functional elements in the assembled genome. For bacterial genomes, this includes identifying the protein coding genes, the transfer RNA genes, and the ribosomal RNA genes. The annotation is performed with tools such as Prokka, which uses a database of known protein sequences to identify the genes.

The output of the annotation is a set of files in formats such as GFF and GenBank. These files contain the coordinates of the genes and the predicted functions. The annotation is required for the core genome analysis, because the core genome is defined by the set of genes that are present in all isolates.

The quality of the annotation depends on the quality of the assembly. A fragmented assembly can result in genes that are split across multiple contigs, which can cause the annotation to miss genes or to annotate them incorrectly. You should review the annotation statistics, such as the number of genes and the number of complete genes, to identify potential problems.

Step 4: Core Genome Alignment

The core genome alignment is the process of identifying the set of genes that are present in all isolates and aligning the sequences of those genes. This step is performed with tools such as Roary or Panaroo.

The input to the core genome alignment is the set of annotated genomes in GFF format. The tool identifies the orthologous genes across all the genomes and extracts the sequences of those genes. The output is a multiple sequence alignment of the core genes.

The core genome alignment is a critical step because it determines the amount of data that is used for the phylogenetic inference. The core genome size is the number of genes that are present in all isolates. For a set of closely related isolates, the core genome may be a large fraction of the total genome. For a set of distantly related isolates, the core genome may be smaller.

The core genome alignment can be used to build a phylogenetic tree directly, or it can be used to identify the variable sites for a more focused analysis. The choice depends on the research question and the computational resources available.

Step 5: Phylogenetic Tree Inference

The final step is to infer the phylogenetic tree from the core genome alignment. This step is performed with tools such as IQ-TREE or RAxML. The input is the multiple sequence alignment, and the output is a tree in Newick format.

The phylogenetic inference process involves two main steps. First, the software estimates the substitution model parameters from the data. Second, the software searches for the tree that best explains the data under the model. The search is performed using a heuristic algorithm, because the number of possible trees is too large to evaluate all of them.

The support for the branches of the tree is estimated using a method such as bootstrap. The bootstrap method involves resampling the alignment and inferring a tree from each resampled dataset. The support value for a branch is the percentage of the resampled trees that contain that branch. A branch with a high support value is considered to be well supported.

The output of the phylogenetic inference is a tree file in Newick format. This file can be visualized and edited with tools such as FigTree or the Interactive Tree of Life.

Choosing the Right Approach for Your Data

The choice between reference-based and reference-free approaches is one of the most important decisions in the workflow. The following factors should be considered when making this decision.

Reference-Based Approach

The reference-based approach is the best choice when you have a high quality reference genome that is closely related to your isolates. This approach is faster and requires less computational resources than the assembly-based approach. It is also more sensitive for detecting small numbers of variants, which is important for outbreak investigations.

The main limitation of the reference-based approach is that it is biased toward the reference. If the isolates are distantly related to the reference, the reads may not map well, and the variant calls will be incomplete. This can lead to a tree that is not representative of the true relationships.

The reference-based approach is also limited by the quality of the reference. If the reference genome has errors, those errors will be propagated into the variant calls and the tree.

Reference-Free Approach

The reference-free approach is the best choice when you do not have a suitable reference genome or when you want to avoid the bias of a reference. This approach is more computationally intensive than the reference-based approach, but it provides a more complete picture of the genome.

The reference-free approach is also the best choice when you are working with a diverse set of isolates. The assembly process can capture the full diversity of the genomes, including the accessory genome, which is not possible with the reference-based approach.

The main limitation of the reference-free approach is the quality of the assembly. A fragmented assembly can lead to errors in the annotation and the core genome alignment, which can then lead to errors in the tree.

Hybrid Approaches

Some workflows combine the reference-based and reference-free approaches. For example, you can assemble the genomes and then map the assembled contigs to a reference to identify the core genome. This approach can provide the benefits of both approaches while minimizing the limitations.

The choice of approach should be documented in the methods section of any report or publication. The documentation should include the version of the software, the parameters used, and the reference genome used.

Tools and Software for Each Step

The following table summarizes the tools that are commonly used for each step of the pipeline. The choice of tool depends on the specific requirements of the analysis, such as the size of the dataset and the computational resources available.

Quality Control Tools

FastQC is a widely used tool for assessing the quality of sequencing reads. It provides a report that includes the per-base quality scores, the GC content, and the presence of adapter sequences. The report is an HTML file that can be viewed in a web browser.

MultiQC is a tool that aggregates the results of FastQC across multiple samples. This is useful when you are working with a large number of samples, because it allows you to review the quality of all samples in a single report.

Trimmomatic is a tool for trimming the reads. It uses a sliding window approach to remove low quality bases and can also remove adapter sequences. The parameters for Trimmomatic are specified on the command line.

Assembly Tools

SPAdes is a de novo assembler that is designed for bacterial genomes. It uses a De Bruijn graph approach and can handle multiple read sizes. The output of SPAdes is a set of contigs in FASTA format.

KMPLER is a tool that is designed for the assembly of bacterial genomes from short reads. It is a hybrid assembler that uses both the De Bruijn graph and the overlap layout consensus approaches.

The choice of assembler depends on the specific data. SPAdes is a good choice for most bacterial genomes, while KMPLER is a good choice for genomes with a high GC content.

Annotation Tools

Prokka is a tool for the annotation of bacterial genomes. It uses a database of known protein sequences to identify the genes. The output of Prokka is a set of files in GFF and GenBank format.

Bakta is a newer tool for the annotation of bacterial genomes. It uses a more comprehensive database and provides more detailed annotations than Prokka.

The choice of annotation tool depends on the specific needs of the workflow. Prokka is a good choice for most workflows, while Bakta is a good choice when you need more detailed annotations.

Core Genome Alignment Tools

Roary is a tool for the identification of the core genome and the pan genome. It takes the GFF files as input and produces a core genome alignment as output.

Panaroo is a newer tool for the core genome alignment. It is designed to be more accurate than Roary, especially for large datasets.

The choice of core genome alignment tool depends on the size of the dataset and the specific needs of the workflow.

Phylogenetic Inference Tools

IQ-TREE is a tool for the inference of phylogenetic trees. It uses a maximum likelihood approach and can automatically select the best substitution model for the data.

RAxML is another tool for the inference of phylogenetic trees. It is a widely used tool that is known for its speed and accuracy.

The choice of phylogenetic inference tool depends on the size of the dataset and the computational resources available. IQ-TREE is a good choice for most datasets, while RAxML is a good choice for very large datasets.

Records and Measurements

The documentation of the workflow is an essential part of the phylogenetic analysis. The records should include the following information for each sample:

• The sample identifier and the source of the sample
• The sequencing platform and the sequencing run identifier
• The quality control metrics, including the number of reads and the average quality score
• The assembly statistics, including the number of contigs and the N50
• The annotation statistics, including the number of genes
• The core genome size
• The phylogenetic tree file in Newick format
• The software versions and the parameters used for each step

The records should be stored in a structured format, such as a spreadsheet or a database. The records should be reviewed at each step of the workflow to identify any potential issues.

The records are also important for the reproducibility of the analysis. If the analysis is repeated, the records should be sufficient to reproduce the same results.

Common Failure Patterns and How to Avoid Them

The following are common failure patterns that can occur during the phylogenetic analysis workflow.

Low Quality Reads

Low quality reads can introduce errors into the assembly or the variant calls. The errors can then propagate into the phylogenetic tree. The quality control step is designed to identify and remove low quality reads. If the quality control is not performed, the resulting tree may be inaccurate.

Poor Assembly Quality

A poor assembly can produce a fragmented genome with many contigs. The fragmented assembly can lead to errors in the annotation and the core genome alignment. The assembly quality should be assessed before proceeding to the annotation step.

Incorrect Core Genome Definition

The core genome is defined as the set of genes that are present in all isolates. If the core genome is defined too strictly, the core genome may be too small to provide a reliable phylogenetic signal. If the core genome is defined too loosely, the core genome may include genes that are not truly orthologous.

Incorrect Substitution Model

The substitution model is used to calculate the genetic distances between the sequences. If the model is incorrect, the branch lengths in the tree can be inaccurate. The model selection is performed by the phylogenetic inference software, but the user can also specify the model manually.

Bootstrap Support Values

The bootstrap support values indicate the confidence in the branches of the tree. A branch with a low support value is not well supported and should be interpreted with caution. The bootstrap support values can be affected by the amount of data and the model.

Biosafety and Laboratory Practice

The workflow for building a phylogenetic tree from whole genome sequences involves the handling of bacterial samples and the use of laboratory equipment. The biosafety considerations are important for the safety of the laboratory personnel and the environment.

The Biosafety in Microbiological and Biomedical Laboratories provides the guidelines for the safe handling of microorganisms. The guidelines include the risk assessment, the containment, and the decontamination procedures. The risk assessment should be performed before the start of the work, and the containment should be appropriate for the risk level of the organism.

The American Society for Microbiology provides the professional education and the laboratory practice guidelines for the microbiology laboratory. The guidelines include the quality control procedures and the safety practices.

The Bacteriological Analytical Manual provides the official methods for the food microbiology laboratory. The methods include the quality control procedures and the safety practices.

The Clinical and Laboratory Standards Institute provides the standards for the antimicrobial susceptibility testing. The standards are used to ensure the quality of the testing.

The American Type Culture Collection provides the authenticated biological materials and the strain information. The authenticated materials are used for the quality control of the laboratory.

Professional Escalation Criteria

The following are the criteria for the escalation of the analysis to a professional:

• The quality of the sequencing data is consistently low across multiple samples
• The assembly quality is poor, with a high number of contigs and a low N50
• The core genome alignment is not consistent across the samples
• The phylogenetic tree has a large number of branches with low bootstrap support
• The results of the analysis are not consistent with the known biology of the organism

In these cases, the analysis should be reviewed by a professional with expertise in the phylogenetic analysis.

Decision Framework for Selecting a Phylogenetic Inference Strategy

The choice of inference strategy determines how much evolutionary signal you extract from your data and how much computational time you spend. Many researchers default to a single approach without considering how the number of isolates, the genetic diversity, and the research question interact. This section provides a practical decision framework that maps your data characteristics to a specific inference strategy, along with a record system for tracking decisions and a troubleshooting method for diagnosing poor tree support.

Step 1: Classify Your Dataset by Three Key Variables

Before you run any inference software, classify your dataset along three axes. These three variables drive every downstream decision.

Variable 1: Isolate count. Count the number of genomes in your analysis. Small datasets contain fewer than 50 isolates. Medium datasets contain 50 to 500 isolates. Large datasets contain more than 500 isolates. The isolate count determines which inference tools can finish in a reasonable time and which bootstrap strategies are computationally feasible.

Variable 2: Genetic diversity. Estimate the average pairwise nucleotide diversity across your core genome alignment. You can calculate this with a simple distance matrix before running a full tree inference. Low diversity means isolates differ at fewer than 0.5 percent of core genome sites. Medium diversity means 0.5 to 2 percent of sites differ. High diversity means more than 2 percent of sites differ. This measurement directly affects whether a reference-based or reference-free approach is appropriate and whether recombination correction is needed.

Variable 3: Research question type. Define whether your question is about outbreak tracking, population structure, or deep evolutionary relationships. Outbreak tracking requires maximum resolution among very closely related isolates. Population structure analysis requires a balanced view of both core and accessory genome variation. Deep evolutionary questions require robust model selection and careful handling of recombination.

Step 2: Match Dataset Characteristics to an Inference Strategy

Use the following decision rules to select your inference strategy. These rules are based on the practical constraints of the tools and the statistical properties of the data.

Rule A: Low diversity and small to medium isolate count. Use a reference-based variant calling approach followed by maximum likelihood inference with IQ-TREE. Map reads to a closely related reference genome, call variants, and extract the core single nucleotide polymorphism alignment. This strategy maximizes sensitivity for detecting the small number of differences that distinguish outbreak isolates. The reference genome should be from the same species and ideally the same lineage. The American Type Culture Collection provides authenticated reference strains that can serve as mapping references when you need a well-characterized baseline.

Rule B: Low diversity and large isolate count. Use a reference-based approach with a fast inference tool such as FastTree or a maximum likelihood tool with a fast bootstrap option. The large isolate count means that the alignment is large, and the low diversity means that the evolutionary signal is concentrated in a small number of variable sites. You can reduce the alignment to variable sites only to speed up the inference. This strategy is common in national surveillance programs that track thousands of isolates.

Rule C: Medium diversity and any isolate count. Use a reference-free core genome approach. Assemble the genomes, annotate them, and extract the core genome alignment with Roary or Panaroo. This approach avoids the bias of a single reference genome and captures the full set of shared genes. The core genome alignment provides a robust signal for population structure analysis. The Clinical and Laboratory Standards Institute emphasizes the importance of standardized methods for reproducibility, and the core genome approach provides a standardized basis for comparison across laboratories.

Rule D: High diversity or suspected recombination. Use a reference-free approach with recombination filtering. High diversity means that the isolates may have accumulated recombination events that obscure the vertical evolutionary signal. Tools such as Gubbins or ClonalFrameML can identify and remove recombinant regions before tree inference. The resulting tree reflects the clonal frame of the isolates. This strategy is essential for species with high rates of horizontal gene transfer, such as Streptococcus pneumoniae or Neisseria meningitidis.

Rule E: Mixed diversity within a single dataset. This situation occurs when you have a set of closely related isolates plus a few more distantly related outgroups. Use a two stage approach. First, build a core genome tree with all isolates to establish the overall structure. Second, build a separate reference-based tree for the closely related cluster to resolve the fine scale relationships. This approach prevents the distant outgroups from dominating the tree topology and obscuring the relationships among the closely related isolates.

Step 3: Record the Decision and Its Rationale

The decision framework is only useful if you record the rationale for each choice. Create a decision log that captures the following fields for every analysis:

• Dataset identifier and the number of isolates
• Average nucleotide diversity calculated from the alignment
• The inference strategy selected
• The reference genome used, if applicable
• The software versions and the parameters used
• The date of the analysis and the analyst name

Store this log in a structured format such as a spreadsheet or a text file that accompanies the analysis. The log serves two purposes. First, it allows you to reproduce the analysis exactly. Second, it allows you to compare the results of different strategies on the same dataset. The American Society for Microbiology provides professional guidance on laboratory record keeping and quality assurance, and the same principles apply to computational analyses.

Step 4: Troubleshoot Poor Tree Results

When the tree has low bootstrap support or produces relationships that contradict known biology, use the following troubleshooting method to identify the cause.

Check the alignment quality first. The most common cause of a poor tree is a poor alignment. Review the core genome alignment for regions with excessive gaps or ambiguous characters. Remove columns that are predominantly gaps or that have a high proportion of ambiguous bases. The alignment should be reviewed visually with a tool such as Jalview or AliView before inference.

Check the recombination signal. If the tree has long branches that connect distantly related isolates, recombination may be present. Run a recombination detection tool and compare the tree built from the full alignment with the tree built from the recombination filtered alignment. If the trees differ substantially, recombination is the cause of the poor support.

Check the model fit. The substitution model can be a source of error. IQ-TREE can select the best model using the Bayesian Information Criterion. If the model selection indicates a complex model, the data may have heterogeneous evolutionary rates. Consider partitioning the alignment by gene or by codon position.

Check the bootstrap method. The standard bootstrap resamples the alignment columns. The ultrafast bootstrap in IQ-TREE is faster but can be less accurate for small datasets. If the bootstrap support is low, run a standard bootstrap with a higher number of replicates to confirm the result.

Check the isolate set. The tree may be poor because the isolates are too diverse or too similar. If the isolates are too diverse, the core genome may be too small to provide a reliable signal. If the isolates are too similar, there may be too few variable sites to resolve the relationships. In both cases, the dataset may need to be expanded or reduced.

Step 5: Validate the Tree Against Independent Data

The final step in the decision framework is to validate the tree against independent data. This validation is a quality check that is often overlooked. The validation can take several forms.

Compare with a different inference method. Build a tree with a different method, such as a neighbor joining tree or a Bayesian tree, and compare the topology. If the major clades are the same, the tree is robust to the inference method.

Compare with a different data subset. Build a tree from a different subset of the data, such as the accessory genome or a different set of core genes. If the major clades are the same, the tree is robust to the data choice.

Compare with known epidemiological data. If the isolates come from an outbreak, the tree should be consistent with the epidemiological data. For example, isolates from the same ward or the same time period should cluster together. The World Health Organization emphasizes the importance of integrating genomic data with epidemiological data for antimicrobial resistance surveillance, and the same principle applies to phylogenetic validation.

Compare with a reference tree. If a well established tree exists for the species, compare your tree with the reference. The reference tree can be obtained from a public database or from a published study. The comparison can be done visually or with a tree comparison metric.

Step 6: Document the Validation Results

The validation results should be documented in the same decision log as the inference strategy. The documentation should include the validation method, the result, and the conclusion. The validation documentation is important for the interpretation of the tree and for the publication of the results.

The decision framework and the troubleshooting method are not a substitute for professional judgment. If the tree has persistent low support or if the validation fails, the analysis should be escalated to a professional with expertise in phylogenetic analysis. The escalation criteria include a tree with a large number of branches with low bootstrap support, a tree that contradicts the known biology of the organism, and a tree that is not reproducible with different methods.

Frequently Asked Questions

What is the difference between a core genome tree and a whole genome tree?

A core genome tree is built from the set of genes that are present in all isolates being analyzed. A whole genome tree is built from the entire genome, including both the core and the accessory genes. The core genome tree is more reliable for inferring the evolutionary relationships because it is not affected by the presence or absence of genes that can be acquired or lost through horizontal gene transfer.

How many isolates do I need to build a reliable phylogenetic tree?

The number of isolates needed depends on the research question and the diversity of the isolates. For an outbreak investigation, a small number of isolates may be sufficient to identify the source of the outbreak. For a population study, a larger number of isolates is needed to capture the diversity of the population. The bootstrap support values can be used to assess the reliability of the tree.

What is the best substitution model for bacterial whole genome data?

The best substitution model depends on the data. The software can select the best model based on the data. The General Time Reversible model is a common choice for bacterial whole genome data. The model selection is performed by the software, and the user can also specify the model manually.

How do I interpret the bootstrap support values?

The bootstrap support value for a branch is the percentage of the resampled trees that contain that branch. A branch with a support value of 95 percent or higher is considered to be well supported. A branch with a support value below 70 percent is considered to be poorly supported.

Can I build a phylogenetic tree from raw reads without assembling the genome?

Yes, you can build a phylogenetic tree from raw reads by mapping the reads to a reference genome and identifying the variants. This approach is faster than the assembly-based approach, but it is biased toward the reference genome.

What is the role of the reference genome in the phylogenetic analysis?

The reference genome is used as a basis for the mapping and the variant calling. The reference genome is also used for the annotation of the assembled genome. The choice of the reference genome can affect the results of the analysis.

How do I choose the reference genome for the analysis?

The reference genome should be closely related to the isolates being analyzed. The reference genome should be of high quality and should be annotated. The reference genome can be obtained from a public database or from a previous study.

What is the difference between a maximum likelihood tree and a neighbor joining tree?

A maximum likelihood tree is inferred using a statistical model of the sequence evolution. A neighbor joining tree is inferred using a distance-based approach. The maximum likelihood tree is more accurate than the neighbor joining tree, but it is more computationally intensive.

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• Poultry Diseases Caused by Bacteria: Pathogenesis and Management Strategies
• Bacterial Diseases in Chickens: A Comprehensive Veterinary Reference
• Bacterial Contamination of Poultry Meat: Salmonella, Campylobacter, and E. coli in Chickens
• Bacterial Contamination of Poultry: Salmonella and E. coli
• Bacterial Diseases of Chickens: A Comprehensive Overview

References and Further Reading

• Antimicrobial Resistance. World Health Organization.

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