name: bio-comparative-genomics-pangenome-analysis description: Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows. origin: openai4s category: bioskills/comparative-genomics metadata: tool_type: cli primary_tool: Panaroo third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
Reference examples tested with: Panaroo 1.5.1+ (Tonkin-Hill 2020 Genome Biol 21:180), PPanGGOLiN 2.2.0+ (Gautreau 2020 PLoS Comp Biol 16:e1007732), PEPPAN 1.0.5+ (Zhou 2020 GR 30:1667), GET_HOMOLOGUES 25102023+, anvi'o 8.0+ (Eren 2021 Nat Microbiol 6:3), Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663; bundled with Cactus 2.5+), PGGB 0.7.5+ (Garrison 2024 Nat Methods 21:2008), vg 1.59.0+ (Sirén J et al 2024 Nat Methods 21:2017), PanGenie 3.1.0+ (Ebler 2022 Nat Genet 54:518), PGR-TK 0.3.6+ (Chin 2023 Nat Methods 20:1213; cschin/pgr-tk; repo archived April 2026 transitioning to PANGEA), PANGEA (in development by DGI / Diploid Genomics as PGR-TK's successor for pangenome graph exploration + analysis -- check https://github.com/cschin/pgr-tk for current repository pointer), Bakta 1.10.4+ (annotation for input), Roary 3.13.0+ (DEPRECATED; use Panaroo), Scoary 1.6.16+, pyseer 1.3.11+, BUSCO 5.7+, FastTree 2.1.11+, RAxML-NG 1.2+. Python 3.10+ required for Panaroo / PPanGGOLiN.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: panaroo --version; ppanggolin --version; peppan --help; cactus-pangenome --help; pggb --version; vg version
- Python: pip show panaroo ppanggolin
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.
"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.
panaroo -i annotated_gffs/ -o panaroo_out --clean-mode strict --remove-invalid-genes -- bacterial pangenomeppanggolin workflow --fasta fasta_list.tsv --output ppanggolin_out -- partitioned bacterial pangenomepeppan -i genomes.gff -o peppan_out -- genus-scale bacterial pangenomecactus-pangenome jobStore seqFile.txt --reference name --vcf --gfa -- Minigraph-Cactus pangenomepggb -i genomes.fa.gz -n 90 -t 32 -o pggb_out -- PGGB pangenome graphvg autoindex --workflow giraffe -r ref.fa -v variants.vcf.gz -- vg pangenome indexing| Tool | Approach | Output | Strength | Fails when |
|---|---|---|---|---|
| Panaroo (Tonkin-Hill 2020 GB 21:180) | Graph-based ortholog clustering + annotation-error correction | Core, shell, accessory pangenome with cleaned annotations | Best for clonal bacteria (Mtb 413-genome benchmark; Tonkin-Hill 2020) | Slow for > 10000 genomes; assumes Prokka/Bakta input |
| PPanGGOLiN (Gautreau 2020 PLoS CB 16:e1007732) | Hidden Markov partition: persistent/shell/cloud | Partitioned pangenome with HMM-based class assignment | Scales to many genomes; interpretable partitions | Probabilistic class boundaries differ from strict Tettelin |
| PEPPAN (Zhou 2020 GR 30:1667) | Bacterial pangenome for diverse genera | Pan + core genomes from 1000s of genomes | Designed for high diversity (whole genus) | Slower than newer alternatives at small scales |
| Roary (Page 2015; DEPRECATED) | Original bacterial pangenome | Same outputs as Panaroo | Legacy; widely cited | Inflates accessory substantially due to annotation-error tolerance; use Panaroo |
| GET_HOMOLOGUES (Contreras-Moreira 2013) | Multi-algorithm consensus (OrthoMCL, BDBH, COG) | Consensus pangenome | Cross-validates across methods | Slower; multi-program output integration |
| anvi'o pangenomics (Eren 2021 Nat Microbiol 6:3) | Interactive pangenome with metadata | Visual pangenome browsing + integration | Standard for interactive microbial pangenome | Less automated; manual curation expected |
| Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663) | Cactus base-level + minigraph SV-graph integration | Pangenome graph (GFA, VCF, GBZ) | Production-grade for HPRC-scale haplotypes | Requires reference; designed for intra-species pangenome |
| PGGB (Garrison 2024 Nat Methods 21:2008) | All-vs-all wfmash + seqwish | Pangenome graph (GFA) | Modern reference-free graph; HPRC-validated | Computationally heavy at > 100 genomes |
| vg pangenome (Sirén J et al 2024 Nat Methods 21:2017) | Pangenome graph indexing + Giraffe / GiraffeY mapping | Mapped reads to graph + variant calling | vg ecosystem standard for graph-based variant calling | Setup complex; learning curve |
| PanGenie (Ebler 2022 Nat Genet 54:518) | Pangenome-graph-based genotyping | SV genotype calls | Efficient genotyping from short reads via graph | Requires pre-built pangenome graph |
| PGR-TK (Chin 2023 Nat Methods 20:1213; cschin/pgr-tk) | 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.
| Scenario | Recommended approach | Why |
|---|---|---|
| Bacterial strain set (5-1000 genomes) of one species | Panaroo + PPanGGOLiN | Cross-validation; Panaroo's annotation-cleaning + PPanGGOLiN's partition |
| Bacterial genus-level pangenome (> 1000 genomes) | PEPPAN | Designed for high genus-level diversity |
| Mycobacterium tuberculosis (clonal) | Panaroo | Tonkin-Hill 2020 benchmark; clonal pangenomes |
| E. coli (highly diverse) | PPanGGOLiN or PEPPAN | High accessory diversity |
| Eukaryotic intra-species pangenome (e.g. human, soybean) | Minigraph-Cactus or PGGB | Graph-based; SV-aware |
| HPRC-style 90 haplotype graph | Minigraph-Cactus | Production-grade for HPRC scale |
| Reference-free eukaryotic pangenome | PGGB | All-vs-all alignment-free graph |
| Pangenome graph for variant calling | vg autoindex -> vg giraffe | Standard graph-aligner ecosystem |
| Bacterial pan-GWAS for phenotype | Panaroo + Scoary or pyseer | Pangene matrix from Panaroo; pan-GWAS tool |
| Visualize pangenome interactively | anvi'o pangenomics workflow | Standard for interactive analysis |
| Distinguish core / shell / cloud genes | PPanGGOLiN (HMM-partitioned) or Tettelin manual partition on Panaroo output | Standard Tettelin framework |
| Open vs closed pangenome (Heaps law) | wgd v2 statistical fit OR custom mclust on Panaroo output | Tettelin 2005 framework |
| Detect HGT-acquired accessory genes | Cross-reference with [[hgt-detection]] | Pangenome + phylogeny |
| Eukaryotic structural-variation indexing | Minigraph-Cactus -> vg + PanGenie | SV-aware genotyping pipeline |
| Bacterial functional pangenome | Panaroo + eggNOG-mapper + KEGG | Functional annotation |
| Pangenome-aware reference for read alignment | vg giraffe with pangenome graph | Reduces reference bias |
| Genome-graph-based fine-mapping | vg + GraphAligner or vg giraffe | SV-aware short-read alignment |
| Repetitive / clinically relevant gene (MHC class II, DAZ1-4, OPN1LW/OPN1MW) | PGR-TK MAP graph + principal bundle decomposition | Built for tangled repeat graphs; bundle decomposition reveals haplotype-allele structure that linear refs collapse |
| Next-gen pangenome graph exploration (2026+) | PANGEA (PGR-TK successor, in development by DGI / Diploid Genomics) | Modernized successor to PGR-TK; check cschin/pgr-tk pointer for current repo |
| HLA / KIR / immune-locus pangenome | PGR-TK + manual bundle inspection | Standard tools collapse repeat alleles; PGR-TK's bundle decomposition preserves them |
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
| 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 |
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
Goal: Build pangenome graph from haplotype-resolved assemblies.
Approach: Provide reference + haplotypes -> Minigraph-Cactus produces GFA + VCF + GBZ for downstream genotyping.
# Prepare seqFile (Cactus convention)
cat > pangenome_seqs.txt << 'EOF'
GRCh38 GRCh38.fa
HG002.hap1 HG002.hap1.fa
HG002.hap2 HG002.hap2.fa
HG003.hap1 HG003.hap1.fa
HG003.hap2 HG003.hap2.fa
EOF
cactus-pangenome jobStore_path pangenome_seqs.txt \
--outDir hprc_pangenome \
--outName hprc_pangenome \
--reference GRCh38 \
--vcf \
--gfa \
--gbz \
--indexCores 32 \
--mapCores 32
# Outputs:
# hprc_pangenome/hprc_pangenome.full.hal Full Cactus HAL
# hprc_pangenome/hprc_pangenome.gfa.gz Graph Fragment Assembly format
# hprc_pangenome/hprc_pangenome.vcf.gz Short variants relative to GRCh38
# hprc_pangenome/hprc_pangenome.gbz GBZ compressed graph
# hprc_pangenome/hprc_pangenome.giraffe.gbz Giraffe-indexed graph for mapping
Goal: Genotype short reads against a pre-built pangenome graph.
Approach: Pre-built pangenome -> vg autoindex -> vg giraffe (fast mapping) -> vg call variants.
# Pre-build index
vg autoindex --workflow giraffe --threads 16 \
--ref-graph hprc_pangenome.gfa.gz \
--output hprc_index
# Map reads
vg giraffe \
--gbz-name hprc_index.giraffe.gbz \
--dist-name hprc_index.dist \
--minimizer-name hprc_index.min \
--fastq-in sample.R1.fq.gz \
--fastq-in sample.R2.fq.gz \
--output-format GAM \
--threads 16 \
> sample.gam
# Pack alignment information
vg pack -x hprc_index.giraffe.gbz -g sample.gam -o sample.pack
# Call variants
vg call hprc_index.giraffe.gbz -k sample.pack -a > sample.vcf
Goal: Identify gene presence/absence associated with a phenotype.
Approach: Panaroo presence/absence matrix + phenotype file -> Scoary -> phenotype-gene associations.
# Run Scoary
scoary \
--gene-presence-absence panaroo_out/gene_presence_absence.csv \
--traits phenotypes.tsv \
--output scoary_out \
--threads 16 \
--upgma-tree
# Output:
# scoary_out/*_results.csv per-trait gene associations
| Pattern | Likely cause | Action |
|---|---|---|
| Panaroo accessory >> PPanGGOLiN accessory | Panaroo "accessory" includes singleton; PPanGGOLiN "cloud" is a separate class | Compare strict mode definitions; Panaroo + PPanGGOLiN cross-validate |
| Roary accessory >> Panaroo accessory | Roary annotation-error inflation | Trust Panaroo; Roary deprecated |
| PEPPAN core != Panaroo core | Different clustering thresholds | Panaroo for clonal; PEPPAN for genus-scale; consistent within method |
| Minigraph-Cactus VCF and Cactus pairwise differ | Pangenome integrates SV; pairwise is direct | Minigraph-Cactus for variant-aware pangenome |
| PGGB and Minigraph-Cactus disagree on graph topology | PGGB reference-free; MC reference-anchored | Both valid; report both for transparency |
| Heaps-law alpha differs across resampling | Stochasticity; insufficient sampling | Require > 100 genomes; report 95% CI |
| PanGenie genotype contradicts read-based SV caller | Repetitive region (graph-spaghetti) | Trust read-based for repetitive; PanGenie for unique regions |
| Bakta and Prokka annotation give different gene counts | Different gene-prediction defaults | Use Bakta (GenBank-compliant); document |
| Scoary and pyseer disagree on top genes | Different statistical assumptions | Cross-validate; trust consensus |
Operational rule for publication: Bacterial pangenome uses Panaroo + PPanGGOLiN cross-validation; eukaryotic pangenome uses Minigraph-Cactus (HPRC scale) or PGGB (reference-free). Document annotation pipeline + version; report core/shell/cloud at multiple thresholds; verify Heaps-law on > 100 genomes for openness claims.
| Pushback | Standard response |
|---|---|
| "Annotation pipeline?" | Bakta 1.10+ on all genomes; consistent settings; BUSCO completeness reported per strain |
| "Why Panaroo over Roary?" | Panaroo's annotation-error correction reduces accessory inflation substantially (nearly an order of magnitude on the Mtb benchmark; Tonkin-Hill 2020) |
| "Tettelin thresholds?" | Reported at multiple thresholds (95%, 99%); PPanGGOLiN HMM partition as cross-validation |
| "Heaps law inference?" | >= 100 genomes; resampling 95% CI reported |
| "Recombination?" | ClonalFrameML applied; recombinant regions masked or analyzed separately |
| "Why Minigraph-Cactus?" | HPRC standard; production-grade for haplotype-resolved pangenomes |
| "Reference bias?" | Reference choice documented; PGGB cross-validation for reference-free comparison |
| "Pan-GWAS multiple testing?" | Bonferroni-corrected across genes; or pyseer with k-mer-based |
| "Open vs closed pangenome?" | Heaps-law alpha reported with CI; openness claim conditional on alpha < 1 |
| "Annotation density consistency?" | BUSCO completeness verified per strain; outliers excluded |
| Error / symptom | Cause | Solution |
|---|---|---|
| Panaroo "input format error" | GFF3 missing required fields | Verify GFF3 from Bakta has correct attribute fields |
| PPanGGOLiN HDF5 unreadable | Version mismatch | Pin PPanGGOLiN version; rebuild |
| Roary used (legacy script) | Roary deprecated | Migrate to Panaroo |
| PEPPAN OOM | Too many genomes | Reduce to representative subset; or use PEPPAN with chunking |
| Cactus pangenome unrelated to expectation | Wrong seqFile syntax | Tabs not spaces; correct file paths |
| PGGB wfmash hangs | Too many large genomes | Reduce to <= 20 eukaryotic genomes |
| vg autoindex memory error | Insufficient RAM | Increase to 200+ GB; or split by chromosome |
| PanGenie genotype empty | Index mismatch between graph + reads | Re-build vg index with same vg version |
| Scoary "no significant traits" | Few strains or low effect | Increase strains; verify phenotype variance |
| pyseer p-values uniform | Population structure inflation | Use --lmm flag with kinship matrix |
| anvi'o display error | Database version mismatch | Re-create with current anvi'o version |
| Minigraph-Cactus runs but VCF empty | All inputs identical | Verify inputs differ |
| Bakta annotation gives 0 genes | Reference data not configured | Set BAKTA_DB env or use --db /path |
# Bacterial pangenome
conda install -c bioconda panaroo ppanggolin peppan get_homologues anvio
# Eukaryotic pangenome
conda install -c bioconda cactus pggb vg pangenie
# PGR-TK (repeat-rich / clinical gene focus)
conda install -c bioconda pgr-tk
# Or: cargo install pgrtk; or Docker quay.io/cschin/pgr-tk
# Note: cschin/pgr-tk archived April 2026; transitioning to PANGEA (developed by DGI / Diploid Genomics; check upstream repo for pointer)
# Annotation
conda install -c bioconda bakta prokka
# Pan-GWAS
conda install -c bioconda scoary pyseer
# Recombination
conda install -c bioconda clonalframeml
# Mobile elements
git clone https://github.com/clb21565/mobileOG-db
# QC
conda install -c bioconda busco compleasm
For HPRC-scale eukaryotic pangenome, use cluster with >= 500 GB RAM and HPC scheduler integration via Toil (see [[whole-genome-alignment]]).