name: "prokka-genome-annotation" description: "Annotate prokaryotic genomes (bacteria, archaea, viruses) via Prokka's BLAST/HMM pipeline. Identifies CDS, rRNA, tRNA, tmRNA, signal peptides against Pfam, TIGRFAMs, RefSeq. Outputs GFF3, GenBank, FASTA, TSV. Use PGAP for NCBI GenBank submission; Bakta for faster NCBI-compatible annotation." license: "GPL-3.0"

Prokka Genome Annotation

Overview

Prokka is a command-line pipeline for rapid annotation of prokaryotic genomes (bacteria, archaea, and viruses). It uses a tiered search strategy: protein-coding genes (CDS) are predicted with Prodigal and searched first against a genus-specific database, then RefSeq proteins, then Pfam/TIGRFAMs HMMs. Non-coding RNA genes (rRNA, tRNA, tmRNA) are identified with Barrnap, Aragorn, and Infernal. Prokka processes a single FASTA assembly in minutes and outputs a comprehensive annotation in GFF3, GenBank, FASTA, and tabular formats.

When to Use

• Annotating a newly assembled bacterial or archaeal genome from Illumina, PacBio, or Nanopore assemblies
• Getting functional protein annotations (CDS with product names, EC numbers, GO terms) from a draft or complete genome
• Preparing annotation files for downstream comparative genomics (Roary pan-genome, OrthoFinder)
• Annotating viral or phage genomes when kingdom-specific databases are important
• Performing metagenome-assembled genome (MAG) annotation with the --metagenome flag
• Parsing annotated outputs in Python with BioPython for downstream sequence or feature analysis
• Use PGAP (NCBI Prokaryotic Genome Annotation Pipeline) instead when the goal is NCBI GenBank submission with standards compliance
• Use Bakta instead for faster annotation with built-in NCBI-compatible outputs and a more regularly updated database

Prerequisites

• Software: Prokka ≥ 1.14, Perl 5, Prodigal, Barrnap, HMMER3, BLAST+, Aragorn, Infernal, tbl2asn
• Python packages (for output parsing): biopython, pandas, matplotlib
• Input: assembled genome in FASTA format (complete or draft with multiple contigs)
• Environment: conda strongly recommended to handle the Perl and C dependency stack

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

# Install Prokka via conda/mamba (recommended)
conda install -c conda-forge -c bioconda prokka

# Or with mamba (faster)
mamba install -c conda-forge -c bioconda prokka

# Verify installation and database setup
prokka --version
# prokka 1.14.6

# Check that required tools are on PATH
prokka --depends
# prokka needs: awk, sed, grep, makeblastdb, blastp, hmmscan, ...

# Install Python parsing dependencies
pip install biopython pandas matplotlib

Quick Start

# Annotate a bacterial genome assembly — results in results/ directory
prokka genome.fasta \
    --outdir results/ \
    --prefix sample1 \
    --kingdom Bacteria \
    --cpus 4

# Check output summary
cat results/sample1.txt
# Organism: Genus species strain
# Contigs: 1
# Bases: 4639675
# CDS: 4140
# rRNA: 22
# tRNA: 86

echo "Annotation complete. Key output files:"
ls results/sample1.{gff,gbk,faa,ffn,tsv}

Workflow

Step 1: Install and Verify Prokka

Install Prokka and confirm all dependent tools are accessible in the current environment.

# Create a dedicated conda environment
conda create -n prokka_env -c conda-forge -c bioconda prokka python=3.10 -y
conda activate prokka_env

# Verify Prokka version and all tool dependencies
prokka --version
# prokka 1.14.6

prokka --depends
# Checking that required tools are installed...
# OK: makeblastdb is installed (2.13.0+)
# OK: blastp is installed (2.13.0+)
# OK: hmmscan is installed (3.3.2)
# OK: prodigal is installed (2.6.3)
# OK: barrnap is installed (0.9)

# Check available genus-specific databases bundled with Prokka
ls $(conda info --base)/envs/prokka_env/db/genus/
# Archaea  Bacteria  Mitochondria  Viruses

# Install Python parsing tools
pip install biopython pandas matplotlib

Step 2: Prepare the Input Genome

Clean and rename contigs to comply with Prokka's header requirements before annotation.

from Bio import SeqIO
import re

# Load and inspect assembly
input_fasta = "genome.fasta"
records = list(SeqIO.parse(input_fasta, "fasta"))
print(f"Input assembly: {len(records)} contigs")
total_bases = sum(len(r) for r in records)
print(f"Total bases: {total_bases:,}")
print(f"Largest contig: {max(len(r) for r in records):,} bp")
print(f"N50 approx: see assembly stats tool")

# Rename contigs to short IDs compatible with Prokka (max 37 chars)
# Prokka requires: no spaces, no special characters in header
cleaned = []
for i, rec in enumerate(records, 1):
    new_id = f"contig_{i:04d}"
    new_rec = rec.__class__(rec.seq, id=new_id, description=f"len={len(rec.seq)}")
    cleaned.append(new_rec)

SeqIO.write(cleaned, "genome_clean.fasta", "fasta")
print(f"\nWrote genome_clean.fasta with {len(cleaned)} renamed contigs")
# genome_clean.fasta: contig_0001 through contig_NNNN

# Alternatively, clean headers with a simple bash one-liner
awk '/^>/{print ">contig_" ++i; next}{print}' genome.fasta > genome_clean.fasta

# Filter out short contigs (< 200 bp) to reduce annotation noise
awk '/^>/{header=$0; next} length($0) >= 200 {print header; print}' \
    genome_clean.fasta > genome_filtered.fasta

echo "Filtered assembly ready: $(grep -c '>' genome_filtered.fasta) contigs"

Step 3: Run Basic Prokka Annotation

Run Prokka with standard options for a bacterial genome, specifying genus/species for database selection.

# Basic annotation with genus/species hint (uses genus-specific protein database first)
prokka genome_clean.fasta \
    --outdir annotation/ \
    --prefix E_coli_K12 \
    --kingdom Bacteria \
    --genus Escherichia \
    --species coli \
    --strain K12 \
    --cpus 8 \
    --mincontiglen 200

# Expected runtime: 2–10 minutes for a typical 4–6 Mb bacterial genome

echo "Prokka annotation output files:"
ls annotation/
# E_coli_K12.err   E_coli_K12.faa   E_coli_K12.ffn
# E_coli_K12.fna   E_coli_K12.gbk   E_coli_K12.gff
# E_coli_K12.log   E_coli_K12.sqn   E_coli_K12.tbl
# E_coli_K12.tsv   E_coli_K12.txt

Step 4: Parse Annotation Summary (TSV)

Load the TSV output for a quick overview of annotated features and their functional assignments.

import pandas as pd

# Load the annotation TSV (tab-delimited feature table)
tsv_file = "annotation/E_coli_K12.tsv"
df = pd.read_csv(tsv_file, sep="\t")
print(f"Total features: {len(df)}")
print(f"Columns: {list(df.columns)}")
# Columns: [locus_tag, ftype, length_bp, gene, EC_number, COG, product]

# Feature type summary
print("\nFeature type counts:")
print(df["ftype"].value_counts().to_string())
# CDS     4140
# tRNA      86
# rRNA      22
# tmRNA      1

# Functional gene annotations (non-hypothetical CDS)
cds_df = df[df["ftype"] == "CDS"].copy()
hypothetical = cds_df["product"].str.contains("hypothetical", case=False, na=True)
print(f"\nCDS with known function: {(~hypothetical).sum()}")
print(f"Hypothetical proteins: {hypothetical.sum()}")

# Genes with EC numbers (enzymes)
ec_annotated = cds_df[cds_df["EC_number"].notna() & (cds_df["EC_number"] != "")]
print(f"CDS with EC numbers: {len(ec_annotated)}")
print(ec_annotated[["locus_tag", "gene", "EC_number", "product"]].head(5).to_string(index=False))

Step 5: Parse GenBank Output with BioPython

Read the GenBank file to access per-gene sequences, qualifiers, and feature coordinates.

from Bio import SeqIO
import pandas as pd

# Parse GenBank file
gbk_file = "annotation/E_coli_K12.gbk"
records = list(SeqIO.parse(gbk_file, "genbank"))
print(f"Contigs in GenBank: {len(records)}")

# Iterate over CDS features and extract details
rows = []
for rec in records:
    for feat in rec.features:
        if feat.type != "CDS":
            continue
        qualifiers = feat.qualifiers
        rows.append({
            "contig":      rec.id,
            "locus_tag":   qualifiers.get("locus_tag", ["?"])[0],
            "gene":        qualifiers.get("gene", [""])[0],
            "product":     qualifiers.get("product", ["hypothetical protein"])[0],
            "EC_number":   qualifiers.get("EC_number", [""])[0],
            "protein_id":  qualifiers.get("protein_id", [""])[0],
            "start":       int(feat.location.start),
            "end":         int(feat.location.end),
            "strand":      feat.location.strand,
            "aa_length":   len(qualifiers.get("translation", [""])[0]),
        })

features_df = pd.DataFrame(rows)
print(f"CDS features extracted: {len(features_df)}")
print(features_df.head(3).to_string(index=False))

# Retrieve protein sequence for a specific gene
gene_name = "dnaA"
gene_feat = features_df[features_df["gene"] == gene_name]
if not gene_feat.empty:
    idx = gene_feat.index[0]
    print(f"\n{gene_name}: {features_df.loc[idx, 'aa_length']} aa")

Step 6: Visualize Annotation Statistics

Generate a summary barplot of feature types and functional annotation coverage.

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

tsv_file = "annotation/E_coli_K12.tsv"
df = pd.read_csv(tsv_file, sep="\t")

cds = df[df["ftype"] == "CDS"].copy()
n_cds = len(cds)
n_known = (~cds["product"].str.contains("hypothetical", case=False, na=True)).sum()
n_hypo = n_cds - n_known
n_ec = cds["EC_number"].notna().sum()
n_rrna = (df["ftype"] == "rRNA").sum()
n_trna = (df["ftype"] == "tRNA").sum()

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

# Left panel: feature type counts
types = df["ftype"].value_counts()
colors_left = ["#2980B9", "#27AE60", "#E74C3C", "#F39C12"] + ["#95A5A6"] * len(types)
axes[0].bar(types.index, types.values, color=colors_left[:len(types)], edgecolor="white")
axes[0].set_title("Annotated Feature Counts")
axes[0].set_xlabel("Feature Type")
axes[0].set_ylabel("Count")
for i, (label, val) in enumerate(types.items()):
    axes[0].text(i, val + 5, str(val), ha="center", fontsize=9)

# Right panel: CDS functional annotation breakdown
labels = ["Known function", "Hypothetical protein", "Enzyme (EC number)"]
sizes  = [n_known, n_hypo, n_ec]
colors_right = ["#2980B9", "#BDC3C7", "#E74C3C"]
bars = axes[1].bar(labels, sizes, color=colors_right, edgecolor="white")
axes[1].set_title(f"CDS Functional Annotation\n(n={n_cds} total CDS)")
axes[1].set_ylabel("Count")
axes[1].tick_params(axis="x", rotation=15)
for bar, val in zip(bars, sizes):
    axes[1].text(bar.get_x() + bar.get_width() / 2,
                 val + 10, str(val), ha="center", fontsize=9)

plt.suptitle("Prokka Genome Annotation Summary", fontsize=12, fontweight="bold")
plt.tight_layout()
plt.savefig("prokka_annotation_summary.png", dpi=150, bbox_inches="tight")
print(f"Saved prokka_annotation_summary.png")
print(f"CDS: {n_cds}  |  Known function: {n_known}  |  Hypothetical: {n_hypo}")
print(f"rRNA: {n_rrna}  |  tRNA: {n_trna}  |  EC-annotated CDS: {n_ec}")

Step 7: Batch Annotation Across Multiple Genomes

Annotate multiple genome assemblies sequentially and collect summary statistics.

#!/bin/bash
# batch_prokka.sh — annotate all FASTA files in a directory

INPUT_DIR="genomes/"
OUTPUT_DIR="annotations/"
mkdir -p "$OUTPUT_DIR"

for FASTA in "$INPUT_DIR"/*.fasta; do
    SAMPLE=$(basename "$FASTA" .fasta)
    echo "Annotating: $SAMPLE"
    prokka "$FASTA" \
        --outdir "${OUTPUT_DIR}/${SAMPLE}" \
        --prefix "$SAMPLE" \
        --kingdom Bacteria \
        --cpus 4 \
        --mincontiglen 200 \
        --quiet
    echo "  Done: ${OUTPUT_DIR}/${SAMPLE}/${SAMPLE}.txt"
done

echo "Batch annotation complete."

# Collect summary statistics from all Prokka .txt files
from pathlib import Path
import pandas as pd

annotation_dir = Path("annotations/")
rows = []

for txt_file in sorted(annotation_dir.glob("*/*.txt")):
    sample = txt_file.stem
    stats = {}
    with open(txt_file) as f:
        for line in f:
            line = line.strip()
            if ": " in line:
                key, val = line.split(": ", 1)
                stats[key.strip()] = val.strip()
    rows.append({
        "sample":   sample,
        "contigs":  int(stats.get("Contigs", 0)),
        "bases":    int(stats.get("Bases", 0)),
        "CDS":      int(stats.get("CDS", 0)),
        "rRNA":     int(stats.get("rRNA", 0)),
        "tRNA":     int(stats.get("tRNA", 0)),
    })

summary_df = pd.DataFrame(rows)
print(f"Annotated genomes: {len(summary_df)}")
print(summary_df.to_string(index=False))
summary_df.to_csv("batch_annotation_summary.csv", index=False)
print("\nSaved: batch_annotation_summary.csv")

Step 8: Compare Annotations Between Strains

Use the protein FASTA outputs for cross-strain comparison with identity-based clustering.

from Bio import SeqIO, pairwise2
import pandas as pd

# Load protein sequences from two strains
def load_proteins(faa_file):
    """Return dict of locus_tag -> protein sequence."""
    return {rec.id: str(rec.seq)
            for rec in SeqIO.parse(faa_file, "fasta")}

strain_a = load_proteins("annotation_A/strain_A.faa")
strain_b = load_proteins("annotation_B/strain_B.faa")

print(f"Strain A proteins: {len(strain_a)}")
print(f"Strain B proteins: {len(strain_b)}")

# Compare gene counts and size distributions
import matplotlib.pyplot as plt
import numpy as np

len_a = [len(seq) for seq in strain_a.values()]
len_b = [len(seq) for seq in strain_b.values()]

fig, ax = plt.subplots(figsize=(8, 4))
bins = np.linspace(0, 1500, 50)
ax.hist(len_a, bins=bins, alpha=0.6, label=f"Strain A (n={len(len_a)})", color="#2980B9")
ax.hist(len_b, bins=bins, alpha=0.6, label=f"Strain B (n={len(len_b)})", color="#E74C3C")
ax.set_xlabel("Protein Length (aa)")
ax.set_ylabel("Count")
ax.set_title("Protein Length Distribution by Strain")
ax.legend()
plt.tight_layout()
plt.savefig("strain_protein_length_comparison.png", dpi=150, bbox_inches="tight")
print("Saved strain_protein_length_comparison.png")

# Find proteins unique to each strain by size/count difference
print(f"\nSize difference: {abs(len(strain_a) - len(strain_b))} proteins")
print("→ Use Roary or OrthoFinder for formal pan-genome analysis")

Key Parameters

Common Recipes

Recipe: Annotation with Custom Protein Database

When to use: You have closely related reference proteins (e.g., characterized isolate) to improve annotation accuracy.

# Use a custom protein database to enhance annotation of a novel strain
# Custom proteins are searched first, before internal Prokka databases
prokka genome.fasta \
    --proteins reference_proteins.faa \
    --outdir custom_annotation/ \
    --prefix novel_strain \
    --kingdom Bacteria \
    --cpus 4

echo "Custom DB annotation complete:"
grep "CDS" custom_annotation/novel_strain.txt

Recipe: Metagenome-Assembled Genome (MAG) Annotation

When to use: Annotating a MAG where Prodigal cannot train on the full genome sequence.

# --metagenome disables Prodigal model training (uses meta mode)
# --mincontiglen 500 discards short, potentially chimeric contigs
prokka MAG_bin_42.fasta \
    --metagenome \
    --kingdom Bacteria \
    --outdir mag_annotation/ \
    --prefix MAG_bin_42 \
    --mincontiglen 500 \
    --cpus 4

echo "MAG annotation complete:"
cat mag_annotation/MAG_bin_42.txt

Recipe: Extract Specific Gene Sequences

When to use: Retrieve nucleotide or protein sequences for a target gene or pathway from the annotation.

from Bio import SeqIO

# Extract all sequences for a specific gene name from protein FASTA
faa_file = "annotation/E_coli_K12.faa"
target_gene = "dnaA"

matches = []
for rec in SeqIO.parse(faa_file, "fasta"):
    # Prokka FASTA header: >locus_tag gene product
    if target_gene in rec.description:
        matches.append(rec)

print(f"Proteins matching '{target_gene}': {len(matches)}")
for rec in matches:
    print(f"  {rec.id}: {len(rec.seq)} aa — {rec.description}")

# Save matching sequences
if matches:
    SeqIO.write(matches, f"{target_gene}_proteins.faa", "fasta")
    print(f"Saved: {target_gene}_proteins.faa")

Recipe: Convert GFF to Pandas DataFrame

When to use: Work with genomic coordinates for feature overlap analysis or visualization.

import pandas as pd

def parse_gff(gff_file):
    """Parse Prokka GFF3 file into a DataFrame (skips sequence section)."""
    rows = []
    with open(gff_file) as f:
        for line in f:
            if line.startswith("##FASTA"):
                break
            if line.startswith("#") or not line.strip():
                continue
            parts = line.rstrip("\n").split("\t")
            if len(parts) < 9:
                continue
            attr_dict = {}
            for item in parts[8].split(";"):
                if "=" in item:
                    k, v = item.split("=", 1)
                    attr_dict[k] = v
            rows.append({
                "seqname":  parts[0],
                "source":   parts[1],
                "feature":  parts[2],
                "start":    int(parts[3]),
                "end":      int(parts[4]),
                "score":    parts[5],
                "strand":   parts[6],
                "frame":    parts[7],
                "locus_tag": attr_dict.get("ID", ""),
                "gene":     attr_dict.get("gene", ""),
                "product":  attr_dict.get("product", ""),
            })
    return pd.DataFrame(rows)

gff_df = parse_gff("annotation/E_coli_K12.gff")
print(f"GFF features: {len(gff_df)}")
print(gff_df[gff_df["feature"] == "CDS"].head(5)[
    ["seqname", "start", "end", "strand", "gene", "product"]
].to_string(index=False))

Expected Outputs

Troubleshooting

References

• Prokka GitHub: tseemann/prokka — source code, installation instructions, and detailed parameter reference
• Seemann T (2014) Bioinformatics 30(14):2068–2069 — original Prokka paper with benchmarks vs. RAST
• Prodigal documentation — gene prediction engine used by Prokka for CDS calling
• Barrnap GitHub: tseemann/barrnap — rRNA prediction tool bundled with Prokka
• BioPython GenBank parsing tutorial — reference for parsing Prokka GenBank output in Python