Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
December 7th, 2021
This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.
This analytical protocol allows for the study of pathogenic populations of bacteria, large-scale. That's very important because it enhance how ecological and epidemiological investigations can be done. But for that to happen, what we need is an automated and scalable tool, or a computational platform that allows for many thousands of genome sequences to be analyzed at once.
ProkEvo fits that niche, and it allows for practical bacterial population analysis to be done at scale, while mapping pan-genomic content, that reviews genotypes and unique features about those genotypes for ecological and epidemiological investigation. The main advantage of this protocol is the usage of powerful, automated, and scalable computational platforms, such as ProkEvo to do heuristic mining of hierarchical genotypes in bacterial populations. The analytical protocol being presented here today has several practical implications.
One of them is to facilitate diagnostics in the sense that would allow for bacterial genotypes to be mapped and tracked in real time, in a scalable fashion, which allows for pathogenic lineages of pathogens to be discerned and defined to track and map those pathogens in different settings. Another application is to enhance routine surveillance of public health laboratories and regulatory agencies, which is done to facilitate the tracking of pathogens in different commercial settings. The protocol presented here provides practical guidance for microbiologists, ecologists, epidemiologists, and anyone interested in bacterial population genomics.
ProkEvo is an open source and publicly available platform, and its GitHub page provides detailed usage instructions. The protocol explained here can be found on GitHub as well. With the provided instructions, we want to make ProkEvo and this protocol easy to use and be utilized by novice and advanced researchers.
Start conducting the analyses using Gigi tree to plot a phylogenetic tree along with genotypic information. To do so, optimize the Gigi tree figure size, including the diameter and width of rings by changing the numerical values inside the x-lim and G heat map. When plotting multiple layers of data with the phylogenetic tree, aggregate all metadata into the lowest possible number of categories to facilitate the choice of coloring panel.
Conduct the data aggregation based on the question of interest and domain knowledge. Once done, use a bar plot to assess the relative frequencies by aggregating data for the sequence type or ST lineages, and core genome multilocus sequence typing or cgMLST variants to facilitate visualizations. Choose an empirical or statistical threshold used for data aggregation.
The example code can be used to inspect the frequency distribution of the ST lineages and determine the cutoff. The example code shows how minor or low frequency STs are aggregated. The STs that are not numbered can be grouped as other STs.
Use a similar code for the cgMLST variants. Use the nested approach to calculate the proportion of each ST lineage within each BAPS1 subgroup to identify the STs that belong to the same BAPS1 subgroup. The code exemplifies how the ST-based proportion can be calculated across the BAPS1 subgroups.
To plot the distribution of antimicrobial resistance or AMR loci across the ST lineages, use an empirical or statistical threshold to filter out the most important AMR loci to facilitate visualizations. Provide a raw. csv file containing the calculated proportions of all AMR loci across all the ST lineages.
Next calculate the AMR proportion for each ST using the code. After calculations are done for all STs, combine the data sets as one data frame using the code, and then export the csv file containing the calculated proportions with the code. Before plotting the AMR-based distribution across the ST lineages, filter the data based on a threshold to facilitate visualizations.
Next, plot the core genome phylogeny along with the hierarchical genotypic classifications in AMR data in a single plot using Gigi tree. Then optimize the figure size inside Gigi tree using the parameters mentioned earlier. Optimize the visualizations by aggregating the variables or using binary classification, such as the gene presence or absence.
The hierarchical population structure of Salmonella enterica lineage one in the context of a core genome phylogeny was examined. The relative frequencies of all hierarchical genotypes were then used to evaluate the overall distribution and most frequently observed classifications. Less frequent ST lineages were aggregated as other STs to facilitate data visualization.
Similarly, less frequent cgMLST variants were aggregated as other cgMLSTs. The ancestral relationships between the STs were examined using a nested approach by assessing the relative frequency of ST lineages by the BAPS1 subgroups or haplotypes. The relative frequency of the ST lineage differentiating AMR loci was assessed to identify unique accessory genomic signatures linked to the serovar Newport population structure.
In the results, MDFA and AAC6IAA loci appeared to be ancestrally acquired by the serovar Newport population, whereas ST45 is predicted to be multi-drug resistant. When compared to the ST45, the other major ST lineages, such ST5 and ST118, are more likely to be multi-drug susceptible. Additionally, a phylogeny-anchored visualization was used to integrate the hierarchical population structure data systematically.
This analytical protocol presents a basis for data mining of bacterial populations at scale. What it allows is for genotypes to be mapped and tracked at scale using ProkEvo, but it also can be expanded to answer other questions, such as exploring the distribution of metabolic pathways and virulence factors associated with genotypic information. That is, we can predict the phenotypes that are associated with specific genotypes of interest.
The protocol described here definitely paves the way for researchers to explore new questions in the field of population genomics and infer evolutionary and ecological patterns for pathogenic as well as non-pathogenic bacterial species.
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Overview
This article presents a practical, scalable protocol for bacterial population genomic analysis using the ProkEvo computational platform. By leveraging whole-genome sequencing (WGS) data and hierarchical genotypic mapping, the protocol enables researchers to analyze thousands of bacterial genomes, facilitating ecological and epidemiological investigations. The approach is demonstrated using Salmonella Newport, integrating phylogenetic, genotypic, and accessory genomic data to reveal population structure and antimicrobial resistance patterns.
Key Study Components
Area of Science
- Bacterial population genomics
- Computational biology
- Microbial epidemiology
Background
- Routine WGS is transforming epidemiological investigations in public health.
- Large-scale analysis of bacterial populations requires automated, scalable computational tools.
- ProkEvo is an open-source platform designed for hierarchical population genomic analysis.
- Combining genotypic mapping with accessory genome mining enhances ecological inference.
Purpose of Study
- To provide a reproducible, scalable protocol for bacterial population genomic analysis using ProkEvo.
- To guide researchers in mapping hierarchical genotypes and accessory loci for ecological and epidemiological insights.
- To demonstrate practical applications using a large dataset of Salmonella Newport genomes.
Methods Used
- Automated analysis of WGS data using ProkEvo.
- Phylogeny-guided mapping of hierarchical genotypes (Serovar → BAPS1 → ST → cgMLST).
- Visualization of phylogenetic trees with genotypic and accessory data using Gigi tree in R.
- Aggregation and frequency analysis of sequence types (STs) and core genome MLST variants.
- Assessment of antimicrobial resistance (AMR) loci distribution across lineages.
Main Results
- Hierarchical genotypic mapping revealed the population structure of Salmonella Newport.
- ST5 and ST118 lineages share a more recent common ancestor compared to the highly clonal ST45.
- ST45 is predicted to be multi-drug resistant, while ST5 and ST118 are more likely multi-drug susceptible.
- Phylogeny-anchored visualizations integrated genotypic and AMR data, highlighting lineage-specific genomic signatures.
Conclusions
- The protocol enables scalable, reproducible bacterial population genomic analyses.
- Combining hierarchical genotypic mapping with accessory genome analysis provides ecological and epidemiological insights.
- This approach supports real-time surveillance and tracking of pathogenic lineages in public health and regulatory settings.
Frequently Asked Questions
Executive Industry Relevance
This protocol enables scalable, automated analysis of bacterial population genomics using whole-genome sequencing data, supporting epidemiological surveillance and ecological inference in public health and regulatory settings. By integrating hierarchical genotype mapping with accessory genome mining, it provides a framework for de-risking target validation in antimicrobial resistance and virulence factor studies. The approach enhances predictive confidence in lineage-specific genomic signatures, informing go/no-go decisions in early discovery pipelines for antibacterial interventions.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by mapping hierarchical genotypes (Serovar → BAPS1 → ST → cgMLST) to clarify population structure and identify keystone differentiating genotypes such as sequence types.
- Scientific Value: Supports biological de-risking by assessing frequency distributions of genotypes as a proxy for ecological fitness, helping prioritize targets with stable lineage associations.
- Scientific Value: Determines kinship relationships and genetic diversity using genotypic classifications, reducing mechanistic ambiguity in target selection for antimicrobial or antivirulence strategies.
Screening & Assay Development
- Scientific Value: Prepares validated biological systems for downstream workflows by mapping lineage-specific accessory loci, including antimicrobial resistance (AMR) markers, to enable reliable compound screening against defined genetic backgrounds.
- Operational Value: Addresses assay standardization and reproducibility through phylogeny-anchored visualization of hierarchical genotypes and AMR content, facilitating consistent readouts across laboratories.
- Operational Value: Highlights screening readiness and scalability by demonstrating automated processing of 2,365 Salmonella Newport isolates, supporting high-throughput genotypic profiling.
Translational & Preclinical Research
- Scientific Value: Discusses disease relevance by linking accessory AMR loci (e.g., MDFA and AAC6IAA) to lineage-specific genomic signatures in zoonotic pathogens, supporting translational biomarker alignment.
- Scientific Value: Describes continuity from discovery through preclinical validation by using ST-based differences to track ancestral acquisition of resistance loci, informing risk-adjusted advancement decisions.
- Scientific Value: Focuses on predictive de-risking by identifying lineage-differentiating accessory loci that correlate with phenotypic traits like multi-drug susceptibility or resistance, enabling phenotype prediction from genotype.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from Early Discovery to Lead Identification and Preclinical work by providing scalable genotypic mapping and accessory genome analysis that supports hypothesis testing, pathway clarification, and biological de-risking.
- Discovery Biology: Explains how the method supports hypothesis testing, pathway clarification, or biological de-risking through phylogeny-guided mapping of hierarchical genotypes and frequency-based analysis of ST lineages.
- Screening: Describes assay readiness, reproducibility, or quantitative outputs when supported by the article, such as the proportional distribution of AMR loci across ST lineages for standardized screening panels.
- Analytics: Highlights measurements, readouts, or statistical outputs that help teams compare conditions, including relative frequency calculations of ST lineages within BAPS1 subgroups and AMR locus proportions.
- Translational Research: Connects the method to preclinical continuity or biomarker alignment only when the source supports it, such as mapping lineage-specific AMR content to infer evolutionary and ecological patterns in pathogenic populations.
- Enterprise Reuse: Frames the method as a reusable capability rather than a single-use technique, emphasizing its automation, scalability, and open-source availability via ProkEvo for repeated application across bacterial species and studies.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence, target validation, reduction of mechanistic ambiguity.
- Operational Value: Standardization, reproducibility, and scalability.
- Strategic Value: Better go/no-go decisions, capital efficiency, and reduced late-stage biological risk.
- Portfolio Impact: Risk-adjusted prioritization and advancement decisions.
Implementation Considerations
- Required scientific expertise in microbial genomics, phylogenetics, and R programming for hierarchical population genomic analysis.
- Instrumentation and analytical infrastructure needs include access to whole-genome sequencing data, computational resources for scalable processing, and tools like Gigi tree for phylogenetic visualization.
- Cross-team standardization requirements involve aggregating metadata into minimal categories for consistent coloring panels and applying empirical or statistical thresholds for data filtering and visualization.
- Adaptation considerations across model systems include modifying genotypic hierarchies (e.g., Serovar → BAPS1 → ST → cgMLST) and accessory locus filtering thresholds based on the pathogen and research question.
- Practical limitations supported by source material include the need to aggregate low-frequency STs or cgMLST variants as 'other' to facilitate visualization and the dependence on domain knowledge for data aggregation and threshold selection.
