Bio Genome Annotation Functional Annotation

Functional Annotation

Assign functional annotations (GO terms, KEGG orthologs, Pfam domains, EC numbers) to predicted protein sequences using eggNOG-mapper and InterProScan.

eggNOG-mapper

Database Setup

# Download eggNOG v5.0 database (~44 GB)
# Required for local searches; use --data_dir to specify location
download_eggnog_data.py --data_dir /path/to/eggnog_db -y

# Download DIAMOND database only (~9 GB, faster setup)
download_eggnog_data.py --data_dir /path/to/eggnog_db -y -D

# Download taxon-specific databases (optional, smaller)
download_eggnog_data.py --data_dir /path/to/eggnog_db -y -t 2 # Bacteria
download_eggnog_data.py --data_dir /path/to/eggnog_db -y -t 2759 # Eukaryota

Basic Usage

emapper.py \
    -i predicted_proteins.faa \
    --output functional_annot \
    --output_dir eggnog_out \
    --data_dir /path/to/eggnog_db \
    --cpu 16 \
    -m diamond

Key Options

With Taxonomic Scope

# Restrict to bacterial orthologs for a prokaryotic genome
emapper.py \
    -i proteins.faa \
    --output annot \
    --output_dir eggnog_out \
    --data_dir /path/to/eggnog_db \
    --cpu 16 \
    -m diamond \
    --tax_scope Bacteria \
    --go_evidence non-electronic

Output Files

eggnog_out/
├── annot.emapper.annotations    # Main annotation table
├── annot.emapper.hits           # DIAMOND/mmseqs hits
├── annot.emapper.seed_orthologs # Best orthologs
└── annot.emapper.pfam           # Pfam domain annotations

Key Output Columns

InterProScan

InterProScan searches multiple protein signature databases simultaneously.

Basic Usage

interproscan.sh \
    -i predicted_proteins.faa \
    -o interpro_results.tsv \
    -f tsv,gff3 \
    -cpu 16 \
    -goterms \
    -pa

Key Options

Select Specific Databases

# Run only Pfam, TIGRFAM, and CDD
interproscan.sh \
    -i proteins.faa \
    -o interpro_results.tsv \
    -f tsv,gff3 \
    -cpu 16 \
    -goterms -pa \
    -appl Pfam,TIGRFAM,CDD

Available Applications

Merging eggNOG and InterProScan Results

import pandas as pd

def parse_eggnog(annotations_file):
    '''Parse eggNOG-mapper annotations output.'''
    df = pd.read_csv(annotations_file, sep='\t', comment='#',
                     header=None, skiprows=5)
    col_names = [
        'query', 'seed_ortholog', 'evalue', 'score', 'eggNOG_OGs',
        'max_annot_lvl', 'COG_category', 'Description', 'Preferred_name',
        'GOs', 'EC', 'KEGG_ko', 'KEGG_Pathway', 'KEGG_Module',
        'KEGG_Reaction', 'KEGG_rclass', 'BRITE', 'KEGG_TC', 'CAZy',
        'BiGG_Reaction', 'PFAMs'
    ]
    df.columns = col_names[:len(df.columns)]
    return df

def parse_interproscan_tsv(tsv_file):
    '''Parse InterProScan TSV output.'''
    col_names = [
        'protein_id', 'md5', 'length', 'analysis', 'signature_acc',
        'signature_desc', 'start', 'stop', 'score', 'status', 'date',
        'interpro_acc', 'interpro_desc', 'go_terms', 'pathways'
    ]
    df = pd.read_csv(tsv_file, sep='\t', header=None, names=col_names)
    return df

def merge_annotations(eggnog_file, interpro_file):
    '''Merge eggNOG and InterProScan annotations per protein.'''
    eggnog_df = parse_eggnog(eggnog_file)
    interpro_df = parse_interproscan_tsv(interpro_file)

    interpro_summary = interpro_df.groupby('protein_id').agg({
        'signature_acc': lambda x: ','.join(x.dropna().unique()),
        'interpro_acc': lambda x: ','.join(x.dropna().unique()),
        'go_terms': lambda x: '|'.join(x.dropna().unique()),
    }).reset_index()
    interpro_summary.columns = ['query', 'interpro_signatures', 'interpro_ids', 'interpro_go']

    merged = eggnog_df.merge(interpro_summary, on='query', how='outer')

    merged['all_go'] = merged.apply(
        lambda row: combine_go_terms(row.get('GOs', ''), row.get('interpro_go', '')), axis=1
    )
    return merged

def combine_go_terms(eggnog_go, interpro_go):
    '''Combine GO terms from both sources, removing duplicates.'''
    terms = set()
    for go_str in [eggnog_go, interpro_go]:
        if pd.notna(go_str) and go_str != '-':
            terms.update(t.strip() for t in str(go_str).replace('|', ',').split(',') if t.strip().startswith('GO:'))
    return ','.join(sorted(terms)) if terms else '-'

Annotation Statistics

def annotation_summary(merged_df):
    '''Summarize functional annotation coverage.'''
    total = len(merged_df)
    has_go = (merged_df['all_go'] != '-').sum()
    has_kegg = merged_df['KEGG_ko'].notna().sum() if 'KEGG_ko' in merged_df else 0
    has_pfam = merged_df['PFAMs'].notna().sum() if 'PFAMs' in merged_df else 0
    has_ec = merged_df['EC'].notna().sum() if 'EC' in merged_df else 0
    has_desc = (merged_df['Description'] != '-').sum() if 'Description' in merged_df else 0

    print(f'Total proteins: {total}')
    print(f'With GO terms: {has_go} ({has_go/total:.1%})')
    print(f'With KEGG orthologs: {has_kegg} ({has_kegg/total:.1%})')
    print(f'With Pfam domains: {has_pfam} ({has_pfam/total:.1%})')
    print(f'With EC numbers: {has_ec} ({has_ec/total:.1%})')
    print(f'With description: {has_desc} ({has_desc/total:.1%})')

    # Annotation coverage target: >60% with at least one functional term
    has_any = ((merged_df['all_go'] != '-') | merged_df['PFAMs'].notna() | merged_df['KEGG_ko'].notna()).sum()
    print(f'With any annotation: {has_any} ({has_any/total:.1%})')

Troubleshooting

Low Annotation Rate

• Check protein sequence quality (no fragmented ORFs)
• Try broader taxonomic scope (--tax_scope auto)
• Run both eggNOG-mapper and InterProScan and merge results

eggNOG Database Errors

• Verify database version matches emapper version
• Re-download with download_eggnog_data.py --data_dir /path -y

InterProScan Memory Issues

• Reduce batch size with -b option
• Split input FASTA into smaller chunks

Related Skills

• prokaryotic-annotation - Bakta includes basic functional annotation
• eukaryotic-gene-prediction - Produces protein sequences for functional annotation
• pathway-analysis/go-enrichment - Enrichment analysis using GO annotations
• pathway-analysis/kegg-pathways - Pathway mapping with KEGG orthologs