Bacterial vs Eukaryotic Pangenomes: Methods, Sampling & Pitfalls

Bacterial pangenome analysis and eukaryotic pangenome projects share a name but not a playbook. Genome structure, variation types, and study goals differ profoundly. This guide compares objectives, sampling, sequencing, pipelines, and QC. It helps you choose the right strategy for plant, animal, or human pangenomes versus bacterial cohorts.

Biological objectives and genome architecture

Bacterial genomes are compact and gene-dense. Horizontal gene transfer is common. Plasmids, phages, and genomic islands move genes between strains. This shapes presence–absence signals, accessory inflation, and core stability.

Eukaryotic genomes emphasise structural variation and repeats. Transposons, segmental duplications, and centromeres complicate assembly. Many crops are polyploid or highly heterozygous. Gene families expand and contract across lineages.

These architectural contrasts define pangenome goals.

In bacteria, you often map accessory gene flow and clonal structure.
In eukaryotes, you capture SVs, haplotypes, and repeat-resolved sequences.

Definitions shift as well. The "core" in bacteria is often a large, stable set. In eukaryotes, core may shrink when paralogy and copy-number diversity rise. Your interpretation must reflect these realities.

Key takeaway: genome architecture sets the evidence you must collect. Match your analysis to the biology, not the other way around.

Sampling & cohort design

Sampling errors propagate through the entire pangenome. Design the cohort before generating data.

Bacterial projects

Clonal complexes, recombination, and habitat drive gene flow. Sample across time, geography, and source to avoid niche bias. If epidemiology or source attribution matters, add repeated sampling from key locations.

Actionable guidance:

Target 50–300 genomes for initial diversity scans.
Stratify by lineage, host, or environment.
Include replicates for QC and contamination checks.

Heaps’ law–based, subtype-balanced estimates of pangenome openness across 12 microbial pathogens. (Hyun J.C. et al. (2022) BMC Genomics) Subtype-balanced Heaps' Law estimates of pangenome openness for 12 microbial pathogens. (Hyun J.C. et al. (2022) BMC Genomics).

Eukaryotic projects

Diversity, heterozygosity, and ploidy dominate design. For crops and animals, represent major breeds, landraces, or subpopulations. Capture related phenotypes if trait discovery is a goal. For outcrossing species, plan for larger cohorts to stabilise rare SVs.

Actionable guidance:

Begin with 20–50 high-quality, long-read assemblies.
Expand with lower-cost resequencing for breadth.
Track metadata for environment, breeding history, and phenotypes.

Metadata is non-negotiable. Record sample origin, library prep, sequencing run, and assembly caller. Use the same controlled vocabulary across the study. Metadata resolves batch effects and supports downstream statistics.

Sequencing & assembly strategies

Your platform choice decides what you can see. Do not plan assembly after sequencing; design both together.

Bacteria

Short reads remain powerful for many questions. They are economical, and references are compact. However, repeats, plasmids, and phages challenge short reads. Long reads or hybrid strategies improve contiguity and plasmid resolution. For outbreak or source-tracking work, consistent short-read pipelines can still suffice.

Recommended approach:

For routine surveys: Illumina short reads with stringent QC.
For reference-quality builds: HiFi or ONT plus polishing.
For mobile elements: long-read-first to capture plasmids and islands.

Eukaryotes

Long reads are essential for repeat-rich genomes. HiFi or ONT with accurate basecalling yields near-complete contigs. Phasing uncovers haplotypes, SVs, and complex alleles. Telomere-to-telomere (T2T) may be justified for high-value references.

Recommended approach:

For pangenome anchors: 20–50 HiFi or high-accuracy ONT assemblies.
For population breadth: add short-read resequencing to the anchors.
For complex regions: trio binning, Strand-seq, Hi-C, or graph assembly.

Schematic of the Human Pangenome Reference integrating phased diploid assemblies into a graph-based reference that captures novel alleles and complex structural variants across diverse haplotypes. (Liao W.-W. et al. (2023) Nature) Overview of the Human Pangenome Reference: phased diploid assemblies integrated into a graph-based reference that captures novel alleles and complex structural variation across diverse haplotypes. (Liao W.-W. et al. (2023) Nature).

Assembly principles

Lock versions and document the assembler, parameters, and polishing.
Use conservative filtering for low-quality contigs.
Evaluate continuity (N50), completeness (BUSCO-like metrics), and contamination.
For polyploids, validate phasing and copy-number with orthogonal evidence.

Annotation, orthology, and gene families

Annotation drift hides biology. Harmonise before clustering.

Standardise the annotation caller, version, and protein database. Use the same product naming rules across samples. In bacteria, caller choice can split genes inconsistently. In eukaryotes, gene models and UTR decisions affect family boundaries.

Orthology and paralogy are harder in eukaryotes. Expanded families challenge one-to-one matches. Consider species-aware or tree-aware strategies where feasible. In bacteria, mobile elements and recombination upset naive identity thresholds.

Practical steps:

Run a single, locked annotation workflow on all assemblies.
Keep track of gene IDs, product synonyms, and functional hints.
Use adjacency and synteny when possible to stabilise families.
Store a mapping file for each release so teams can cross-walk IDs.

Pipeline & tooling contrasts

The bacterial and eukaryotic stacks diverge for good reasons. Choose tools that reflect your study design and your deliverables.

Bacterial stacks

Curated clustering and PAV: Panaroo merges fragments and corrects artefacts with a graph. Roary provides a fast baseline for presence–absence matrices.
Partitioned views: PPanGGOLiN assigns core, shell, and cloud to stabilise interpretation.
Exploration: PanX links gene families to a phylogeny and offers an interactive browser.

When to prefer what:

Noisy annotations or fragmented genes → Panaroo.
Need speed for baselines or teaching → Roary.
Need interpretable partitions → PPanGGOLiN.
Need shared, explorable results → PanX.

Eukaryotic stacks

Start with long-read assembly and phasing. Graph references then capture haplotype diversity and SVs. Combine gene-level PAV with SV catalogs. The final pangenome often includes a graph-aware browser plus matrices for downstream analyses.

Typical components:

Long-read assembly with polishing and phasing.
SV discovery from long reads; validation with orthogonal data.
Graph construction for locus-level diversity.
Harmonised annotation and cross-sample family tracking.
Export to GWAS/QTL and comparative platforms.

Interoperability matters. Decide early how family IDs, SV IDs, and graph nodes map to each other. Avoid ad-hoc renaming that breaks handoffs.

Quality control & common pitfalls

QC is not a final step. Build it into every stage.

Universal checks

Contamination screens: run before annotation to avoid false accessory inflation.
Version locks: containerise callers and record exact parameters.
Replicates or controls: estimate drift and detect batch effects.
Threshold logs: identity cut-offs, coverage, and minimum lengths.

Bacterial pitfalls

Mixed annotators create artificial accessory expansions.
Recombinant lineages confuse identity thresholds and split families.
Plasmid misassignment mixes mobile genes with chromosomal content.

Mitigations:

Harmonise annotation and re-run questionable subsets.
Use adjacency and synteny to aid family merging.
Separate plasmid contigs during assembly and annotation.

Eukaryotic pitfalls

Polyploidy causes paralog conflation and spurious "presence" calls.
Collapsed repeats hide genuine SVs and create false core calls.
Heterozygosity inflates contig counts and confuses family tracking.

Mitigations:

Use phased assemblies and validate copy-number with coverage.
Apply long reads to resolve repeats and segmental duplications.
Track homoeologs and paralogs with tree-aware or synteny-aware tools.

Soft-core definitions

Be explicit about thresholds for "soft core" in both domains. Changing the cutoff from 95% to 99% can rewrite the narrative. Publish the rule in your methods and parameter files.

From pangenome to downstream analyses

A pangenome is a hub, not an endpoint. Design exports to match downstream teams.

Bacterial use cases

Epidemiology and surveillance: track accessory genes, AMR islands, and mobile elements.
Population structure: integrate PAV with trees or strain graphs.
Source tracing: combine gene flow signatures with metadata.

Deliverables: a stable PAV matrix, partition labels, and parameter files. Include an interactive browser if groups must review families together.

Unrooted phylogeny derived from core-genome alignments for the Xanthomonas strains analysed in this study. (Jha V. et al. (2023) Frontiers in Microbiology) Unrooted core genome phylogenetic tree of the Xanthomonas strains analyzed in this study. (Jha V. et al. (2023) Frontiers in Microbiology).

Eukaryotic use cases

SV-GWAS and QTL mapping: add SV features and PAV to association models.
Trait discovery: relate haplotypes and CNVs to measurable phenotypes.
Comparative genomics: examine gene family expansions and lineage-specific innovations.

Deliverables: phased assemblies, an SV catalog, graph-aware references, and harmonised PAV. Provide crosswalk tables so analysts can link gene families to SVs and graph nodes.

Workflow from multi-species WGS and their respective references to an integrated super-pangenome spanning accessions and species. (van Workum D.-J.M. et al. (2024) BMC Plant Biology) Overview of the process of going from WGS data for multiple species with their respective reference genomes to a super-pangenome in which all data is integrated across accessions and species. (van Workum D.-J.M. et al. (2024) BMC Plant Biology).

Handoffs and documentation

Provide README files describing all IDs and formats.
Store software versions, containers, and parameter YAMLs.
Publish QC summaries with outlier decisions and exclusions.

Practical planning checklist

Use this one-page list before you kick off:

Study design

Clarify biological objectives and success metrics.
Define the sampling frame and stratification.
Lock the metadata schema and controlled vocabulary.

Data generation

Select sequencing platforms based on genome architecture.
Plan long-read anchors and short-read breadth as needed.
Allocate budget for validation and re-runs.

Pipeline

Choose bacterial or eukaryotic stacks accordingly.
Harmonise annotation; freeze versions in containers.
Decide family, SV, and node ID conventions.

QC & reporting

Pre-register thresholds and filters.
Include replicates or controls.
Produce PAV matrices, partitions, SV catalogs, and parameter files.
Archive all intermediate files for auditability.

How we support institutional projects

CD Genomics provides Pan Genome Sequencing and analysis for research institutes, universities, and R&D teams. We design sampling plans, generate high-quality long-read anchors, and deliver reproducible pangenomes with stable IDs, PAV matrices, partitions, and graph-aware exports. Services are non-clinical and not for individuals. If you need a scoped, end-to-end plan for bacterial or eukaryotic pangenomes, we can help align methods, timelines, and deliverables with your scientific goals.

Related reading:

References

Liao, W.W., Asri, M., Ebler, J. et al. A draft human pangenome reference. Nature 617, 312–324 (2023).
Hyun, J.C., Monk, J.M., Palsson, B.O. Comparative pangenomics: analysis of 12 microbial pathogen pangenomes reveals conserved global structures of genetic and functional diversity. BMC Genomics 23, 7 (2022).
van Workum, D.-J.M., Mehrem, S.L., Snoek, B.L. et al. Lactuca super-pangenome reduces bias towards reference genes in lettuce research. BMC Plant Biology 24, 1019 (2024).
Agarwal, V., Stubits, R., Nassrullah, Z., Dillon, M.M. Pangenome insights into the diversification and disease specificity of worldwide Xanthomonas outbreaks. Frontiers in Microbiology 14, 1213261 (2023).
Tonkin-Hill, G., MacAlasdair, N., Ruis, C. et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biology 21, 180 (2020).
Gautreau, G., Bazin, A., Gachet, M. et al. PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph. PLOS Computational Biology 16(3), e1007732 (2020).
Tettelin, H., Masignani, V., Cieslewicz, M.J. et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial "pan-genome". Proceedings of the National Academy of Sciences of the USA 102, 13950–13955 (2005).

* Designed for biological research and industrial applications, not intended for individual clinical or medical purposes.