Pan-genome: Definition, Classification, and Why It Matters (2025 Guide)

Pan-genome is a modern framework for examining all genes found across many individuals of the same species—not just a single reference genome. By combining multiple genomes, it captures variation that one reference misses.

Because of this design, pan-genomes offer two big wins:

• Completeness — reveal core genes plus variable genes unique to lineages or environments.
• Accuracy — reduce single-reference bias and improve detection of structural and presence/absence variants.

In this short guide, you'll learn:

• How pan-genomes are defined and classified (core, soft-core, shell, cloud)
• What open vs closed pan-genomes mean in practice
• Why graph-based references make analyses more reliable

By the end, you'll understand the core ideas behind pan-genomes and why this approach is becoming central to modern genomics.

What is a pan-genome

Here's a plain-English pan genome definition: a pan-genome is the full set of genes observed across all strains or individuals of a species (or a defined population). Instead of relying on one "model" genome, a pan-genome acts like a community reference built from many genomes. This broader view reduces the blind spots that come with a single linear reference—especially for core vs accessory genes, structural variants, and presence–absence differences that shape biology.

Why does that matter? Because genes that are missing from the reference can still drive critical traits—drug resistance in microbes, stress tolerance in crops, or population-specific features in vertebrates. A pan-genome lets researchers see both the shared "toolkit" and the diverse add-ons that enable adaptation.

Flowchart of PPanGGOLiN on a toy example of 4 genomes. (Gautreau G. et al. (2020) PLOS Computational Biology)

Classification

Pan-genome studies often sort genes by how frequently they appear across samples. The goal is to turn presence–absence patterns into easy-to-read bins that connect back to biology:

• Core (~99–100%) — Genes seen in almost every genome. These typically encode essential cellular processes. Think of them as the "always-on" operating system.
• Soft-core (95–99%) — Nearly universal genes that drop out in a few lineages or conditions. They often support common pathways but are not strictly indispensable.
• Shell (15–95%) — Variably present genes tied to environment, niche, or lineage. Shell genes often include transporters, surface proteins, or metabolic modules that help specific groups adapt.
• Cloud (<15%, including singletons) — Rare or group-specific genes. Many arrive via recent acquisition or rapid divergence and can underpin novel capabilities.

These cut-offs are conventions, not hard rules, and different studies may tweak the thresholds. The classification helps researchers prioritise: core for universal biology, shell/cloud for ecology, adaptation, or pathogenesis.

Partitioned pangenome graph of 3 117 Acinetobacter baumannii genomes. (Gautreau G. et al. (2020) PLOS Computational Biology)

Open vs closed pan-genomes

If you keep adding genomes to your dataset, do you keep finding new genes? That is the open vs closed pan-genome question. In an open pan-genome, novel genes continue to appear as sampling grows—common in bacteria with frequent horizontal gene transfer or highly diverse environmental niches. In a closed pan-genome, discoveries plateau, suggesting most gene diversity is already captured.

A simple way to explain this without equations: imagine drawing marbles from a very large jar. If new colours keep showing up as you draw more marbles, you're in an "open" regime. If you stop discovering new colours after a while, you're closer to "closed." Many microbes look open. Some compact, less plastic genomes look more closed. In plants and animals, the answer often depends on taxon, demographic history, and how widely you sample environments and wild relatives.

History

The term "pan-genome" rose from microbial genomics, when researchers saw that each newly sequenced strain contributed additional genes beyond what was in the reference. The insight rapidly spread: a single genome underestimates within-species diversity.

Plant research followed, showing presence/absence variation (PAV) of genes linked to disease resistance, flowering time, and seed composition. As sequencing costs fell and long-read technologies matured, vertebrate and human projects joined in. Today, pan-genomes span microbes, crops, domestic animals, and increasingly complex species, with graph-based references and telomere-to-telomere assemblies pushing completeness and accuracy.

Methods (analysis at a glance)

You don't need to be a bioinformatician to grasp the main steps. Most projects follow a practical path designed to avoid common errors such as misclassifying core vs accessory genes.

1. Consistent annotation

Start by harmonising gene annotations across all genomes. The same gene should be labelled the same way in every sample. Without this, you risk inflating "accessory" content simply because one genome used a different naming or prediction rule.

2. Orthology clustering

Next, group genes into families (orthologous groups) based on sequence similarity and evolutionary relationships. This clustering underpins the presence–absence matrix: each row is a gene family; each column is a sample; entries mark whether a family is present.

3. Pan-genome representation

With families defined, you can summarise core, soft-core, shell, and cloud categories and produce human-readable artefacts:

• A presence–absence matrix that quantifies which genes appear where.
• Variant sets spanning SNPs, small indels, larger structural variants, and PAV.
• When available, a graph-based pan-genome that represents alternate alleles and structures as paths in a graph, like multiple routes on a map. This captures diversity that linear references compress or miss.

4. Sequencing and assemblies

Short reads are accurate and cost-effective for many samples. Long reads (HiFi/ONT) resolve repeats and structural variants that short reads struggle with. Many teams combine them in hybrid assemblies for the best of both worlds. Quality control at each step—coverage, contiguity, completeness, and variant recall—keeps downstream analyses reliable.

5. Downstream summaries

Once the pan-genome is built, researchers link gene categories and variants to biology: which shell genes associate with a phenotype? Which structural variants track with geography or domestication? Which cloud genes mark specific clades or outbreaks?

The complete pangenome construction scheme and visualization. (Andreace F. et al. (2023) Genome Biology)

Applications

The scientific "so what" comes into focus once you view diversity through a pan-genome lens. A few examples across domains:

• Microbial genomics
  • Outbreak tracing and phylogeny: Presence–absence patterns and accessory markers improve resolution among closely related isolates.
  • Virulence and antimicrobial resistance: Genes outside the core often carry toxins, secretion systems, or resistance determinants. Pan-genomes help map these modules across lineages and environments.
• Crop genetics and breeding
  • Trait discovery: Many loci driving agronomic traits live in structurally complex or repeat-rich regions. Pan-genomes and graph references recover these variants, improving GWAS power for PAV and structural variation.
  • Domestication and adaptation: Comparing wild relatives and elite lines reveals gene gains and losses linked to stress tolerance, quality, or yield.
  • Toward prediction: Including structural variants alongside SNPs can lift prediction accuracy for complex traits and explain a portion of "missing heritability."
• Animals and complex organisms
  • Population diversity: Pan-genomes highlight features unique to breeds or ecotypes and reduce bias from one reference strain.
  • Functional insights: Accessory and shell genes, or large structural variants, can affect immune function, development, and adaptation, yet remain invisible in single-reference workflows.

Conclusion

A single reference genome is a useful map, but it shows only one route through the landscape. The pan-genome perspective treats a species as a community of genomes—shared core functions alongside variable soft-core, shell, and cloud features that drive adaptation and phenotype. This view explains why some variants are invisible to linear references, when gene discovery keeps growing in open systems, and how graph-based representations and robust assemblies capture complex diversity more faithfully.

Keep the essentials in mind: define the pan-genome clearly, classify genes by presence–absence, assess openness as sampling expands, and favour methods that recover structural and presence/absence variation. Adopting this mindset delivers a more complete, comparable, and reproducible picture of genomic diversity.

Related reading:

• Pangenome Explained: History, Sequencing Strategies, Databases, and Applications
• Pan-genome vs Single Reference: Why One Genome Isn't Enough
• Pan-genome Sampling Strategy: Humans, Animals, and Plants
• Pan-genome Sequencing & Assembly: Short-reads vs HiFi/ONT and Hybrid
• Pan-genome Pipeline Deep Dive: From Annotation Harmonization to Orthology
• Pan-genome Tools at a Glance: Panaroo, Roary, PPanGGOLiN, PanX
• Bacterial vs Eukaryotic Pangenomes: Methods, Sampling & Pitfalls