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If code throws Bakta annotation incompatible, GFA file inconsistent, vg index version mismatch, the bacterial pangenome ecosystem expects consistent annotation; re-annotate all input genomes with the same tool and version before pangenome analysis.
Pangenome Analysis
"What genes are universal vs accessory across this set of genomes?" -> The pangenome is the union of all genes across a sampled group; the Tettelin partition (Tettelin 2005 PNAS 102:13950) splits it into core (universal), shell (in many but not all), cloud (rare), and species-specific (private) genes. The fundamental dichotomy is bacterial pangenome (clusters genes into orthogroups; Panaroo / PPanGGOLiN / PEPPAN for compact genomes) vs eukaryotic pangenome (graph-based; Minigraph-Cactus / PGGB / vg for haplotype-resolved sequences). The choice depends on what's being represented: bacterial pangenome captures gene-content variation in a species/genus; eukaryotic pangenome graph captures haplotype-level structural and sequence variation. Roary (Page 2015) is now deprecated in favor of Panaroo, which handles annotation-error noise that previously inflated bacterial pangenomes substantially.
Minimizer Anchored Pangenome (MAP) graph + principal bundle decomposition
Multiscale pangenome graph; bundle SVGs; AGC-backed sequence db
Designed for repetitive / clinically-relevant genes (MHC class II, DAZ1-4, OPN1LW/OPN1MW); decomposes tangled graph into interpretable bundles; complements Minigraph-Cactus by exposing fine-grained allele structure
Repo archived April 2026 -> PANGEA succession; pinned to Peregrine-assembler-derived workflow; not a drop-in for variant-calling pipelines
PANGEA (in development by DGI / Diploid Genomics; succeeds PGR-TK; check cschin/pgr-tk for current pointer)
Next-generation MAP-graph framework
Same conceptual outputs as PGR-TK with modernized API
Active development 2026+; expected to add tighter integration with HPRC / T2T workflows
API surface in flux; pin specific version when scripting
Heaps law / Tettelin (Tettelin 2005 PNAS 102:13950)
Statistical model of pangenome openness
Open / closed pangenome classification
Foundational framework
Class boundaries depend on sampling
Scoary (Brynildsrud 2016)
Pan-GWAS on gene presence/absence
Phenotype-gene associations
Standard bacterial pan-GWAS tool
Limited to binary phenotypes
pyseer (Lees 2018 Bioinformatics 34:4310)
Continuous + binary phenotype association on k-mers/genes
Pan-GWAS with k-mer / gene-content units
More flexible than Scoary
Computational cost
pirate (Bayliss 2019)
Bacterial pangenome from multiple methods
Cross-method consensus
Alternative to GET_HOMOLOGUES
Less popular now
Methodology evolves; verify the current Panaroo and PPanGGOLiN manuals + the 2024-2025 microbial pangenome reviews. The HPRC draft pangenome (Liao 2023 Nature 617:312) sets the modern eukaryotic pangenome standard; for bacterial work, Panaroo + PPanGGOLiN is the standard combination.
Decision Tree by Experimental Scenario
Scenario
Recommended approach
Why
Bacterial strain set (5-1000 genomes) of one species
Trigger: Running Panaroo on Prokka- vs Bakta- vs RefSeq- annotated genomes mixed.
Mechanism: Different annotation tools predict different gene boundaries; the same gene is annotated slightly differently across tools, appearing as separate orthogroups. Roary's tolerance of these differences inflated the bacterial accessory genome substantially -- nearly an order of magnitude on the clonal M. tuberculosis benchmark (Tonkin-Hill 2020 GB 21:180). Panaroo's graph-based correction reduces this but cannot eliminate it.
Symptom: Per-strain "accessory" gene count is inflated relative to known biology; comparison to a single-pipeline reference reveals substantial spurious gene-content differences.
Fix: Re-annotate ALL genomes with one pipeline (currently Bakta 1.10.4+ for bacteria; Bakta is GenBank-compliant and faster than Prokka). Use panaroo --clean-mode strict --remove-invalid-genes to apply graph cleaning. Document annotation pipeline + version in methods.
Tettelin partition class boundary artifacts
Trigger: Reporting "core genome" vs "accessory" as percent-of-strains thresholds (e.g. 99% = core).
Mechanism: Tettelin 2005 used 100%-presence = core; pragmatic studies use 95%-99%. The class boundary is arbitrary; small variation in the threshold dramatically changes core/accessory ratio.
Symptom: Core genome size varies 10-30% depending on whether threshold is 95% or 99%.
Fix: Report core/shell/cloud at multiple thresholds; PPanGGOLiN's HMM partition is more principled but still has tunable parameters. Standard reporting: core = present in >=95% (or >=99%); shell = 15-95%; cloud = < 15%. Document threshold.
Roary's annotation-error noise (DEPRECATED tool)
Trigger: Using Roary in new analyses.
Mechanism: Roary tolerates annotation errors (low-identity matches; protein-vs-DNA matches), producing thousands of artifactual accessory genes. Panaroo's introduction (Tonkin-Hill 2020) demonstrated this by re-analyzing 413 Mtb genomes and finding Panaroo's accessory genome was nearly an order of magnitude smaller than the inflated tools' (Roary included).
Symptom: Roary output has many "lineage-specific genes" with poor evidence (single-strain hits, short proteins, no functional annotation).
Fix: Migrate to Panaroo. Panaroo can read Roary's input format; for legacy projects, re-run with Panaroo and compare. Panaroo's --clean-mode strict enforces strict graph-based quality control.
Pangenome graph reference bias (eukaryote)
Trigger: Using Minigraph-Cactus with a single reference; calling variants against the "reference" path.
Mechanism: Minigraph-Cactus is reference-anchored; the chosen reference appears throughout the graph as a privileged path. Variants are called relative to the reference path; non-reference haplotypes are under-represented in the variant calling.
Symptom: Variant call density on non-reference haplotypes is lower than on reference; allele frequencies skewed toward reference.
Fix: Use PGGB (reference-free) for less reference-biased analysis. Alternatively, treat the reference choice as a methodological parameter and document. For HPRC, multiple references can be used and results pooled.
Heaps law misapplied
Trigger: Concluding "open pangenome" from a single Heaps-law fit on insufficient data.
Mechanism: Heaps law parameter alpha distinguishes open (alpha < 1) from closed (alpha > 1) pangenome; estimation requires sampling many genomes. Few genomes give unstable estimates.
Symptom: Heaps-law alpha varies by > 0.2 across resampling; conclusions about pangenome openness flip.
Fix: Require >= 50 genomes (preferably > 100) for Heaps-law estimation; report 95% CI from resampling. Tettelin 2005 demonstrated open Streptococcus agalactiae; Vernikos 2015 reviews open vs closed across taxa.
PGGB memory exhaustion at many genomes
Trigger: Running PGGB with > 30 large eukaryotic genomes on a single node.
Mechanism: PGGB's wfmash all-vs-all step has O(N^2) memory pattern; large eukaryotic genomes (> 1 Gb) make memory prohibitive for > 30 input genomes.
Symptom: PGGB OOMs at the wfmash stage; cluster job killed by OOM-killer.
Fix: Use Minigraph-Cactus for > 30 genomes (it scales better); or split PGGB into chromosomes/regions. PGGB recommendation is <= 20 large genomes per run.
vg index version mismatch breaking giraffe
Trigger: Pre-built vg index used with a different vg version for read alignment.
Mechanism: vg index format evolved; pre-built indexes from one version may not be compatible with another.
Symptom: vg giraffe fails with "index version" error.
Fix: Rebuild vg index with current version; or pin vg version for an analysis. Future vg releases promise backward compatibility but verify.
PanGenie genotype false positives in repetitive regions
Trigger: PanGenie on highly repetitive regions (centromeres, segmental duplications).
Mechanism: Pangenome-graph-based genotyping requires unique paths in the graph; highly repetitive regions create graph-spaghetti paths that are difficult to genotype reliably.
Symptom: PanGenie calls many heterozygous SVs in known-repetitive regions; quality scores low.
Fix: Restrict PanGenie to non-repetitive regions; combine with traditional read-based SV callers (DELLY, Manta) for repeat regions. The HPRC paper documents this limitation (Liao 2023).
Bacterial pangenome with frequent gene-content recombination
Trigger: Building a pangenome of a species with extensive recombination (e.g. Neisseria, Streptococcus pneumoniae).
Mechanism: Frequent recombination breaks the "vertical inheritance" assumption underlying ortholog clustering; the same gene appears in many phylogenetic positions across strains, complicating orthology and inflating accessory genome.
Symptom: Phylogenetic trees from core genome are unstable; per-gene trees show extensive incongruence; pangenome accessory genome appears artificially large.
Fix: Use ClonalFrameML (Didelot 2015 PLoS Comp Biol 11:e1004041) to identify recombinant regions; mask them before pangenome analysis. Restrict core genome analysis to non-recombinant regions.
Annotation density variation across genomes
Trigger: Mixing well-annotated reference genomes with newly assembled, draft-annotation genomes.
Mechanism: Draft annotations miss small genes, pseudogenes, and lineage-specific genes; well-annotated genomes have these. Comparing them inflates "accessory" in draft genomes.
Symptom: Draft genomes have 200-500 fewer accessory genes than expected; per-genome BUSCO completeness > annotation completeness.
Fix: Re-annotate all genomes consistently with Bakta + Prodigal; document BUSCO completeness for each. Exclude genomes with > 5% lower BUSCO than median.
Quantitative Thresholds
Quantity
Threshold
Source / Rationale
Core genome threshold
>=95% (relaxed) to 100% (strict) of strains
Tettelin 2005; pragmatic
Shell genome
15-95% (or 5-95% per PPanGGOLiN)
PPanGGOLiN docs
Cloud genome
< 15% of strains
Tettelin 2005
Heaps law alpha (open)
< 1
Tettelin 2005
Heaps law alpha (closed)
> 1
Tettelin 2005
Minimum genomes for Heaps law fit
>= 50; >= 100 preferred
Vernikos 2015
Panaroo gene cluster identity
>=70% (default); stricter for clonal
Panaroo defaults
PPanGGOLiN coverage
80% gene-length coverage in clustering
Default
PEPPAN BLAT threshold
identity >= 70%
Zhou 2020
Mycobacterium tuberculosis core
~3500-3700 genes (Tonkin-Hill 2020 Mtb benchmark)
Bench results
E. coli pangenome (open)
core ~2400; pangenome >15000
Reference
Plasmodium falciparum core
~5300 genes (eukaryotic prokaryote-like)
Reference
Minimum strains for bacterial pangenome
>= 5; >= 20 for shell/cloud meaningful
Empirical
HPRC pangenome size
90 haplotypes, ~6.4M variants
Liao 2023
PGGB recommended max genomes
<= 20 large eukaryotic; 100+ for compact
Garrison 2024
PanGenie minimum k-mer
k = 31 default
Ebler 2022
vg index Haplotype Sampling
--haplotype-sampling YES for multi-pop graph
Sirén J et al 2024 Nat Methods 21:2017
Scoary pan-GWAS p-value threshold
Bonferroni-corrected p < 0.05
Brynildsrud 2016
pyseer continuous-trait power
requires > 1000 isolates for solid signal
Lees 2018
anvi'o pangenome minimum
5+ genomes for non-trivial visualization
Eren 2021
Panaroo Bacterial Pangenome Workflow
Goal: Construct a high-quality bacterial pangenome with annotation-error correction.
Approach: Annotate genomes consistently with Bakta -> run Panaroo strict mode -> partition with PPanGGOLiN.
# 1. Annotate all genomes with Bakta (consistent annotation)
mkdir -p annotated
for fa in genomes/*.fa; do
name=$(basename $fa .fa)
bakta --db /path/to/bakta-db --threads 16 \
--output annotated/${name} --prefix $name \
--genus Escherichia --species coli \
$fa
done
# 2. Run Panaroo
panaroo -i annotated/*.gff -o panaroo_out -t 16 \
--clean-mode strict --remove-invalid-genes
# 3. Extract pangenome matrix
# panaroo_out/gene_presence_absence.csv strains x genes matrix
# panaroo_out/core_gene_alignment.aln core gene MSA for phylogeny
# panaroo_out/pan_genome_reference.fa consensus pangenome sequence
# 4. Tettelin partition (custom)
python tettelin_partition.py \
--presence panaroo_out/gene_presence_absence.csv \
--core-threshold 0.99 --shell-threshold 0.15 \
--output panaroo_out/tettelin_classification.tsv
# 5. PPanGGOLiN HMM partition (alternative)
# PPanGGOLiN expects a TSV index: `genome_name<TAB>path/to.gff3` per row
for f in annotated/*.gff3; do
printf "%s\t%s\n" "$(basename "$f" .gff3)" "$(realpath "$f")"
done > gff_list.tsv
ppanggolin all --anno gff_list.tsv -o ppanggolin_out --threads 16
# Output: ppanggolin_out/pangenome.h5 (HDF5 with HMM-partitioned genes)
'''Tettelin core/shell/cloud partition from Panaroo gene presence/absence matrix.'''
import pandas as pd
def tettelin_partition(presence_matrix, core_threshold=0.99,
shell_threshold=0.15):
'''Returns DataFrame[gene_name] -> Tettelin class.'''
# presence_matrix: rows = genes, cols = strains, 0/1 entries
n_strains = presence_matrix.shape[1]
fraction = presence_matrix.sum(axis=1) / n_strains
classes = pd.cut(fraction,
bins=[-0.01, shell_threshold, core_threshold, 1.01],
labels=['cloud', 'shell', 'core'])
return pd.DataFrame({'fraction': fraction, 'class': classes})
def heaps_law(presence_matrix, n_iters=100):
'''Estimate Heaps law alpha from genome sampling order.'''
import numpy as np
n_strains = presence_matrix.shape[1]
pan_sizes = []
for _ in range(n_iters):
order = np.random.permutation(n_strains)
pan = set()
sizes = []
for i in order:
genes_in_i = presence_matrix.iloc[:, i] == 1
pan.update(genes_in_i.index[genes_in_i].tolist())
sizes.append(len(pan))
pan_sizes.append(sizes)
pan_array = np.array(pan_sizes) # n_iters x n_strains
n_sampled = np.arange(1, n_strains + 1)
# Fit log-log
mean_pan = pan_array.mean(axis=0)
log_n = np.log(n_sampled)
log_pan = np.log(mean_pan)
alpha = np.polyfit(log_n, log_pan, 1)[0]
return alpha
Minigraph-Cactus for Eukaryotic Pangenome
Goal: Build pangenome graph from haplotype-resolved assemblies.
Approach: Provide reference + haplotypes -> Minigraph-Cactus produces GFA + VCF + GBZ for downstream genotyping.
For HPRC-scale eukaryotic pangenome, use cluster with >= 500 GB RAM and HPC scheduler integration via Toil (see [[whole-genome-alignment]]).
References
Tettelin H et al 2005 PNAS 102:13950 (core/accessory pangenome framework)
Tonkin-Hill G et al 2020 Genome Biol 21:180 (Panaroo)
Gautreau G et al 2020 PLoS Comp Biol 16:e1007732 (PPanGGOLiN)
Zhou Z et al 2020 Genome Res 30:1667 (PEPPAN)
Page AJ et al 2015 Bioinformatics 31:3691 (Roary; DEPRECATED)
Contreras-Moreira B & Vinuesa P 2013 Appl Environ Microbiol 79:7696 (GET_HOMOLOGUES)
Eren AM et al 2021 Nat Microbiol 6:3 (anvi'o pangenomics)
Hickey G et al 2024 Nat Biotech 42:663 (Minigraph-Cactus)
Garrison E et al 2024 Nat Methods 21:2008 (PGGB)
Sirén J et al 2024 Nat Methods 21:2017 (vg pangenome update)
Ebler J et al 2022 Nat Genet 54:518 (PanGenie)
Liao W-W et al 2023 Nature 617:312 (HPRC draft pangenome)
Brynildsrud O et al 2016 Genome Biol 17:238 (Scoary)
Lees JA et al 2018 Bioinformatics 34:4310 (pyseer)
Didelot X & Wilson DJ 2015 PLoS Comp Biol 11:e1004041 (ClonalFrameML)
Schwengers O et al 2021 Microb Genom 7 (Bakta)
Vernikos GS et al 2015 Curr Opin Microbiol 23:148 (pangenome openness review)
Brown CL et al 2022 Appl Environ Microbiol 88:e0099122 (mobileOG-db)
Mikheenko A et al 2018 Bioinformatics 34:i142 (QUAST-LG; long-read assembly evaluation -- earlier "pangenome review" attribution was incorrect; QUAST-LG benchmarks large-scale assembly QC).
Chin C-S et al 2023 Nat Methods 20:1213 (PGR-TK; MAP graphs + principal bundle decomposition for repeat-rich / clinical genes; MHC, DAZ1-4, OPN1LW/OPN1MW examples)
cschin/pgr-tk GitHub (repo; archived April 2026, transitioning to PANGEA developed by DGI / Diploid Genomics; consult upstream README for PANGEA pointer)
Related Skills
comparative-genomics/whole-genome-alignment - Minigraph-Cactus builds on Cactus; PGGB underlies eukaryotic pangenome
comparative-genomics/ortholog-inference - Pangenome clusters are bacterial orthologs at species/genus level
comparative-genomics/hgt-detection - Accessory genes often HGT-derived; mobile-element annotation cross-references
comparative-genomics/gene-family-evolution - CAFE5 modeling on Panaroo presence/absence matrix
genome-annotation/prokaryotic-annotation - Bakta annotation is pangenome input
genome-annotation/repeat-annotation - Repeat masking before eukaryotic pangenome
