Roary Pangenome

Roary Pan-Genome Pipeline

Overview

Roary is a high-throughput pan-genome pipeline for prokaryotes that takes per-sample GFF3 annotations (typically from Prokka or Bakta) and produces a clustered gene presence/absence matrix across the entire input set. It first reduces redundancy with CD-HIT iterative clustering, then performs an all-vs-all BLASTP within each pre-cluster, and finally applies MCL graph clustering to define orthologous gene families. The output partitions the gene space into core (≥ 99 %), soft-core (95–99 %), shell (15–95 %), and cloud (< 15 %) genes and optionally builds a concatenated core-gene alignment suitable for phylogenetic inference.

When to Use

• Computing a pan-genome from a set of bacterial isolate annotations (10–10,000 genomes)
• Producing a gene_presence_absence.csv matrix for downstream GWAS, accessory-gene mining, or core-gene phylogenetics
• Building a concatenated core-gene multi-FASTA alignment for ML/Bayesian phylogenetic trees
• Generating a pan-genome reference FASTA to use as a non-redundant gene catalog
• Comparative genomics across closely related strains where >95 % nucleotide identity is expected
• Use Panaroo instead when assemblies are highly fragmented or annotations are noisy (Panaroo aggressively cleans annotation errors)
• Use PIRATE instead when paralog-aware clustering with multiple identity thresholds is needed
• Use PPanGGOLiN instead when graph-based, statistically grounded gene-family partitioning is preferred over fixed-frequency cutoffs

Prerequisites

• Software: Roary ≥ 3.13, Perl 5, BLAST+, CD-HIT, MCL, BEDTools, PRANK or MAFFT (for -e core alignment), FastTree (optional)
• Python packages (for output parsing): pandas, matplotlib, seaborn, biopython, dendropy
• Input: per-sample GFF3 files with embedded FASTA at the end (Prokka/Bakta default output)
• Hardware: ≥ 8 GB RAM for ~50 genomes; 32 GB+ recommended for ~500 genomes
• Naming: each GFF3 filename becomes the sample column header in the output matrix; use sample_id.gff style

Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v roary first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run roary rather than bare roary.

# Install Roary via conda/mamba (recommended)
mamba install -c conda-forge -c bioconda roary

# Verify installation
roary --version
# 3.13.0

# Verify dependent tools
which cd-hit blastp mcl bedtools mafft
# /opt/conda/bin/cd-hit
# /opt/conda/bin/blastp
# /opt/conda/bin/mcl
# /opt/conda/bin/bedtools
# /opt/conda/bin/mafft

# Install Python parsing dependencies
pip install pandas matplotlib seaborn biopython dendropy

Quick Start

# Run Roary on all GFF3 files in current directory; emit core gene alignment
roary -e --mafft -p 8 -o pangenome -f roary_out/ *.gff

# Inspect summary statistics
cat roary_out/summary_statistics.txt
# Core genes        (99% <= strains <= 100%) 2823
# Soft core genes   (95% <= strains <  99%)   78
# Shell genes       (15% <= strains <  95%)  1542
# Cloud genes       ( 0% <= strains <  15%)  3104
# Total genes                                 7547

Workflow

Step 1: Install Roary and Verify Dependencies

Install Roary in a dedicated environment to avoid Perl module conflicts.

# Create a dedicated conda environment
mamba create -n roary_env -c conda-forge -c bioconda roary mafft fasttree python=3.11 -y
mamba activate roary_env

# Verify Roary
roary --version
# 3.13.0

# Confirm pipeline dependencies are reachable
for tool in cd-hit blastp mcl bedtools mafft FastTree; do
    if command -v $tool >/dev/null; then
        echo "OK: $tool"
    else
        echo "MISSING: $tool"
    fi
done

# Install Python parsing tools
pip install pandas matplotlib seaborn biopython dendropy

Step 2: Prepare Per-Sample GFF3 Files from Prokka or Bakta

Roary requires GFF3 files with an embedded FASTA section (the ##FASTA block). Both Prokka .gff and Bakta .gff3 outputs satisfy this format.

# Verify GFF3 files include the embedded FASTA section
mkdir -p roary_input/
cp annotations/*/sample*.gff roary_input/

for GFF in roary_input/*.gff; do
    if grep -q "^##FASTA" "$GFF"; then
        echo "OK: $GFF"
    else
        echo "MISSING ##FASTA in: $GFF"
    fi
done

# Roary uses GFF filename (without .gff) as the sample column name.
# Rename or symlink to ensure unique, descriptive names.
cd roary_input/
ls *.gff | head
# strain_A.gff  strain_B.gff  strain_C.gff

# Sanity-check sample size and unique names
from pathlib import Path

gff_files = sorted(Path("roary_input/").glob("*.gff"))
print(f"GFF files: {len(gff_files)}")

names = [g.stem for g in gff_files]
duplicates = [n for n in names if names.count(n) > 1]
assert not duplicates, f"Duplicate sample names: {set(duplicates)}"

# Show first 5
for g in gff_files[:5]:
    size_mb = g.stat().st_size / 1e6
    print(f"  {g.name}: {size_mb:.1f} MB")

Step 3: Run Roary with Core Gene Alignment

The -e --mafft combination produces the concatenated core-gene alignment used downstream for phylogenetics.

# Run Roary on the prepared GFF directory
# -e: extract core gene alignment per gene, then concatenate
# --mafft: use MAFFT for alignment (faster than default PRANK)
# -p 8: 8 parallel processes
# -i 95: min BLASTP percentage identity (default 95)
# -cd 99: % isolates a gene must be in to be "core" (default 99)
roary -e --mafft \
    -p 8 \
    -i 95 \
    -cd 99 \
    -o pangenome \
    -f roary_out/ \
    roary_input/*.gff

# Expected runtime:
#   ~50 genomes:  10–20 min
#   ~500 genomes: 4–8 hours

ls roary_out/
# accessory_binary_genes.fa            gene_presence_absence.csv
# accessory_binary_genes.fa.newick     gene_presence_absence.Rtab
# accessory.header.embl                number_of_conserved_genes.Rtab
# accessory.tab                        number_of_genes_in_pan_genome.Rtab
# blast_identity_frequency.Rtab        number_of_new_genes.Rtab
# clustered_proteins                   number_of_unique_genes.Rtab
# core_accessory.header.embl           pan_genome_reference.fa
# core_accessory.tab                   summary_statistics.txt
# core_gene_alignment.aln

Step 4: Parse the Gene Presence/Absence Matrix

The CSV output is the canonical pan-genome matrix: rows are gene families, columns include metadata and one column per sample (filled with locus tags or empty).

import pandas as pd
import numpy as np

df = pd.read_csv("roary_out/gene_presence_absence.csv", low_memory=False)
print(f"Gene families: {len(df)}")
print(f"Columns: {len(df.columns)}")
# Standard metadata columns end with 'Inference', then sample columns follow
metadata_cols = list(df.columns[:14])
sample_cols = list(df.columns[14:])
print(f"Samples: {len(sample_cols)}")

# Build a binary presence/absence matrix (1 = locus tag present, 0 = empty)
binary = df[sample_cols].notna().astype(int)
binary.index = df["Gene"]
print(f"Binary matrix shape: {binary.shape}")

# Pan-genome partition by frequency
n_samples = len(sample_cols)
freq = binary.sum(axis=1) / n_samples
core      = freq[freq >= 0.99]
soft_core = freq[(freq >= 0.95) & (freq < 0.99)]
shell     = freq[(freq >= 0.15) & (freq < 0.95)]
cloud     = freq[freq < 0.15]

print(f"\nPan-genome partition (n={n_samples} genomes):")
print(f"  Core      (>=99 %): {len(core):>5}")
print(f"  Soft-core (95–99 %): {len(soft_core):>5}")
print(f"  Shell    (15–95 %): {len(shell):>5}")
print(f"  Cloud      (<15 %): {len(cloud):>5}")
print(f"  Total            : {len(freq):>5}")

Step 5: Visualize the Pan-Genome Frequency Distribution

A frequency histogram exposes whether the input set has a U-shaped (open) or core-skewed (closed) pan-genome.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv("roary_out/gene_presence_absence.csv", low_memory=False)
sample_cols = list(df.columns[14:])
n = len(sample_cols)
binary = df[sample_cols].notna().astype(int)
freq = binary.sum(axis=1)

fig, axes = plt.subplots(1, 2, figsize=(11, 4))

# Histogram of gene frequencies
axes[0].hist(freq, bins=range(1, n + 2), color="#2980B9", edgecolor="white")
axes[0].axvline(0.99 * n, color="red", linestyle="--", label="99 % core cutoff")
axes[0].axvline(0.15 * n, color="orange", linestyle="--", label="15 % shell cutoff")
axes[0].set_xlabel("Number of genomes containing the gene")
axes[0].set_ylabel("Number of gene families")
axes[0].set_title("Pan-genome frequency distribution")
axes[0].legend()

# Pie chart of pan-genome partition
freqp = freq / n
core_n      = (freqp >= 0.99).sum()
soft_n      = ((freqp >= 0.95) & (freqp < 0.99)).sum()
shell_n     = ((freqp >= 0.15) & (freqp < 0.95)).sum()
cloud_n     = (freqp < 0.15).sum()
axes[1].pie([core_n, soft_n, shell_n, cloud_n],
            labels=[f"Core ({core_n})", f"Soft-core ({soft_n})",
                    f"Shell ({shell_n})", f"Cloud ({cloud_n})"],
            colors=["#27AE60", "#3498DB", "#F39C12", "#95A5A6"],
            autopct="%1.1f%%", startangle=90)
axes[1].set_title(f"Pan-genome partition (n={n} genomes)")

plt.tight_layout()
plt.savefig("pangenome_distribution.png", dpi=150, bbox_inches="tight")
print(f"Saved pangenome_distribution.png  (total families: {len(freq)})")

Step 6: Build a Phylogenetic Tree from the Core Gene Alignment

Use FastTree on the concatenated core-gene alignment to recover strain relationships.

# FastTree expects an aligned multi-FASTA; Roary produces this with `-e`
FastTree -nt -gtr -nosupport \
    < roary_out/core_gene_alignment.aln \
    > roary_out/core_gene_tree.nwk

echo "Tree built: roary_out/core_gene_tree.nwk"
# (Optional) IQ-TREE for support values:
# iqtree -s core_gene_alignment.aln -m GTR+G -bb 1000 -nt 8

# Visualize the FastTree result alongside the gene presence/absence matrix
import dendropy
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

tree = dendropy.Tree.get(path="roary_out/core_gene_tree.nwk", schema="newick")
leaves = [leaf.taxon.label for leaf in tree.leaf_node_iter()]
print(f"Tree leaves: {len(leaves)}")
print(f"Tree length: {tree.length():.4f} substitutions/site")

# Print first 10 leaves with their root-to-tip distances
for leaf in list(tree.leaf_node_iter())[:10]:
    dist = leaf.distance_from_root()
    print(f"  {leaf.taxon.label:<25}  d={dist:.4f}")

Step 7: Produce Roary's Built-in Summary Plots

Roary ships with a roary_plots.py companion that builds a tree-anchored matrix figure.

# Use the bundled python helper from the Roary install
# (download from: https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py)
python roary_plots.py \
    roary_out/core_gene_tree.nwk \
    roary_out/gene_presence_absence.csv \
    --format png \
    --labels

ls *.png
# pangenome_frequency.png
# pangenome_matrix.png
# pangenome_pie.png

Step 8: Compute Per-Genome Accessory Gene Counts

Identify which genomes carry the most unique (cloud) genes — useful for strain-specific gene investigation.

import pandas as pd

df = pd.read_csv("roary_out/gene_presence_absence.csv", low_memory=False)
sample_cols = list(df.columns[14:])
n = len(sample_cols)
binary = df[sample_cols].notna().astype(int)
binary.index = df["Gene"]

freq = binary.sum(axis=1)
cloud_mask = freq < (0.15 * n)
shell_mask = (freq >= 0.15 * n) & (freq < 0.95 * n)

per_genome = pd.DataFrame({
    "genes_total":   binary.sum(axis=0),
    "cloud_genes":   binary.loc[cloud_mask].sum(axis=0),
    "shell_genes":   binary.loc[shell_mask].sum(axis=0),
})
per_genome = per_genome.sort_values("cloud_genes", ascending=False)
print(per_genome.head(10).to_string())

per_genome.to_csv("per_genome_accessory_counts.csv")
print("\nSaved per_genome_accessory_counts.csv")

Key Parameters

Common Recipes

Recipe: Core Gene Alignment from Existing Roary Output

When to use: You already ran Roary without -e and now need a phylogenetic alignment.

# Re-run only the alignment step using the extracted core genes
# query_pan_genome from the Roary suite extracts gene-family multi-FASTAs
query_pan_genome -a intersection \
    -g roary_out/clustered_proteins \
    -o core_gene_list.txt \
    roary_input/*.gff

echo "Core genes: $(wc -l < core_gene_list.txt)"
# Then re-run Roary with -e to materialize the alignment:
# roary -e --mafft -p 8 -f roary_out2/ roary_input/*.gff

Recipe: Lower Identity Threshold for Diverse Genus-Level Sets

When to use: Comparing genomes across multiple closely related species (e.g., genus-level pan-genome).

# Drop identity to 70 % to capture orthologs across species boundaries
roary -e --mafft \
    -p 8 \
    -i 70 \
    -cd 99 \
    -f roary_genus/ \
    roary_input/*.gff

cat roary_genus/summary_statistics.txt
# Expect a smaller core and a much larger shell + cloud

Recipe: Roary on a Subset of Strains

When to use: You want a pan-genome of a specific subclade without re-annotating all genomes.

# Stage GFF files for the subset
mkdir -p subset/
for SAMPLE in strain_A strain_B strain_E strain_K; do
    cp roary_input/${SAMPLE}.gff subset/
done

roary -e --mafft -p 4 -f roary_subset/ subset/*.gff
echo "Subset pan-genome:"
cat roary_subset/summary_statistics.txt

Recipe: Cross-Tabulate Accessory Genes Against Strain Metadata

When to use: You want to find genes enriched in a specific phenotype or geographic group.

import pandas as pd
from scipy.stats import fisher_exact

# Load presence/absence and strain metadata
pa = pd.read_csv("roary_out/gene_presence_absence.csv", low_memory=False)
sample_cols = list(pa.columns[14:])
binary = pa[sample_cols].notna().astype(int)
binary.index = pa["Gene"]
binary.columns = sample_cols

# Metadata: sample_id, phenotype (e.g., "resistant" / "susceptible")
meta = pd.read_csv("strain_metadata.csv").set_index("sample_id")
group_resistant = meta[meta["phenotype"] == "resistant"].index
group_susceptible = meta[meta["phenotype"] == "susceptible"].index
group_resistant = [g for g in group_resistant if g in binary.columns]
group_susceptible = [g for g in group_susceptible if g in binary.columns]

# Fisher's exact test per gene family
results = []
for gene, row in binary.iterrows():
    a = int(row[group_resistant].sum())
    b = len(group_resistant) - a
    c = int(row[group_susceptible].sum())
    d = len(group_susceptible) - c
    if a + c == 0 or a + c == len(group_resistant) + len(group_susceptible):
        continue  # skip invariant genes
    odds, pval = fisher_exact([[a, b], [c, d]])
    results.append({"gene": gene, "n_resistant": a, "n_susceptible": c,
                    "odds_ratio": odds, "pvalue": pval})

results_df = pd.DataFrame(results).sort_values("pvalue")
print(f"Gene-phenotype associations (top 10):")
print(results_df.head(10).to_string(index=False))
results_df.to_csv("phenotype_associated_genes.csv", index=False)

Expected Outputs

Troubleshooting

References

• Roary GitHub: sanger-pathogens/Roary — source code, parameter reference, and roary_plots.py helper
• Page et al. (2015) Bioinformatics 31(22):3691–3693 — original Roary publication describing the CD-HIT + MCL pipeline
• Roary tutorial — step-by-step worked example with sample data
• Panaroo (alternative tool) — graph-based pan-genome with stricter annotation cleaning
• PIRATE (alternative tool) — paralog-aware pan-genome at multiple identity thresholds
• PPanGGOLiN (alternative tool) — graph-based statistical partitioning of pan-genomes
• FastTree documentation — reference for ML phylogenetic inference from core gene alignments