Prokaryotic Genome Annotation

Annotate prokaryotic genomes with Bakta (preferred) or Prokka (legacy). Bakta provides more comprehensive functional annotation through up-to-date databases and NCBI-compatible output formatting.

Bakta

Database Setup

# Download the full database (~30 GB, recommended for comprehensive annotation)
bakta_db download --output /path/to/bakta_db --type full

# Lightweight database (~1.5 GB, faster but less comprehensive)
bakta_db download --output /path/to/bakta_db --type light

# Update existing database
bakta_db update --db /path/to/bakta_db

Basic Annotation

bakta \
    --db /path/to/bakta_db \
    --output bakta_out \
    --prefix my_genome \
    --locus-tag MYORG \
    --threads 8 \
    assembly.fasta

Key Options

With Organism Metadata

bakta \
    --db /path/to/bakta_db \
    --output bakta_out \
    --prefix ecoli_k12 \
    --locus-tag ECK12 \
    --genus Escherichia --species coli --strain K-12 \
    --gram - \
    --complete \
    --threads 16 \
    assembly.fasta

Output Files

bakta_out/
├── my_genome.gff3       # GFF3 annotation (primary output)
├── my_genome.gbff       # GenBank format
├── my_genome.ffn        # Nucleotide CDS sequences
├── my_genome.faa        # Protein sequences
├── my_genome.fna        # Annotated genome sequence
├── my_genome.embl       # EMBL format
├── my_genome.tsv        # Tab-separated feature table
├── my_genome.json       # Machine-readable JSON
└── my_genome.txt        # Summary statistics

Prokka (Legacy Alternative)

Prokka is lighter weight and faster but uses older databases. Prefer Bakta for new projects.

prokka \
    --outdir prokka_out \
    --prefix my_genome \
    --locustag MYORG \
    --genus Escherichia --species coli \
    --cpus 8 \
    --rfam \
    assembly.fasta

Prokka vs Bakta

Parsing Annotations with Python

import gffutils

def load_annotation(gff_file):
    '''Load GFF3 into a queryable database.'''
    db = gffutils.create_db(gff_file, ':memory:', merge_strategy='merge')
    return db

def extract_cds_features(db):
    '''Extract all CDS features with product annotations.'''
    features = []
    for cds in db.features_of_type('CDS'):
        features.append({
            'id': cds.id,
            'seqid': cds.seqid,
            'start': cds.start,
            'end': cds.end,
            'strand': cds.strand,
            'product': cds.attributes.get('product', ['unknown'])[0],
            'locus_tag': cds.attributes.get('locus_tag', [''])[0]
        })
    return features

def compute_coding_density(db, genome_length):
    '''Compute fraction of genome encoding proteins.

    Typical prokaryotic coding density: 85-95%.
    Values below 80% may indicate pseudogenes or annotation gaps.
    Values above 95% may indicate overlapping annotations.
    '''
    coding_bp = sum(cds.end - cds.start + 1 for cds in db.features_of_type('CDS'))
    return coding_bp / genome_length

db = load_annotation('bakta_out/my_genome.gff3')
cds_features = extract_cds_features(db)
print(f'Total CDSs: {len(cds_features)}')

Annotation QC

Expected Metrics by Genome Size

QC Checks

# Count annotated features
grep -c $'\tCDS\t' bakta_out/my_genome.gff3
grep -c $'\ttRNA\t' bakta_out/my_genome.gff3
grep -c $'\trRNA\t' bakta_out/my_genome.gff3

# Check for hypothetical proteins (ideally <40% of total CDSs)
grep -c 'hypothetical protein' bakta_out/my_genome.tsv

BUSCO on Predicted Proteins

busco -i bakta_out/my_genome.faa -m proteins -l bacteria_odb10 -o busco_proteins

Troubleshooting

Low Gene Count

• Check assembly completeness with BUSCO (genome mode)
• Verify correct translation table (--translation-table 4 for Mycoplasma)
• Inspect minimum contig length filter

Many Hypothetical Proteins

• Normal for novel organisms (30-50% is common)
• Try running InterProScan on the .faa file for additional annotations
• Consider eggNOG-mapper for orthology-based functional assignment

NCBI Submission

• Use --compliant flag for NCBI-ready output
• Ensure locus tags follow NCBI format (3-12 uppercase alphanumeric)
• Review .tsv output for annotation warnings

Related Skills

• functional-annotation - Add GO/KEGG/Pfam to predicted proteins
• ncrna-annotation - Detailed ncRNA identification with Infernal
• genome-assembly/assembly-qc - Assess assembly quality before annotation
• genome-intervals/gtf-gff-handling - Parse and manipulate GFF3 output