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", [""]