Bio Genome Annotation Annotation Transfer

Version Compatibility

Reference examples tested with: BioPython 1.83+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Annotation Transfer

"Transfer annotations from a reference to my new assembly" → Map gene models from a well-annotated reference genome onto a new assembly using coordinate liftover or protein-to-genome alignment.

• CLI: liftoff -g reference.gff -o target.gff ref.fa target.fa (same species), miniprot ref.mpi target.fa (cross-species)

Transfer gene annotations from a reference genome to a new assembly (same species with Liftoff) or across species (with MiniProt protein-to-genome alignment). Faster and more consistent than de novo prediction when a high-quality reference annotation exists.

Liftoff (Same-Species Transfer)

Liftoff maps annotations from a reference genome to a target assembly using Minimap2 alignments. Ideal for transferring annotations between different assemblies of the same species.

Basic Usage

# Transfer annotations from reference to target
liftoff \
    -g reference_annotation.gff3 \
    -o lifted_annotation.gff3 \
    -u unmapped_features.txt \
    -dir liftoff_intermediates \
    -p 16 \
    target_assembly.fasta \
    reference_genome.fasta

Key Options

Strict Parameters for High-Quality Transfer

# Stricter thresholds for closely related assemblies
# sc 0.95: 95% of reference feature must align
# s 0.90: 90% sequence identity required
liftoff \
    -g reference.gff3 \
    -o lifted.gff3 \
    -u unmapped.txt \
    -dir liftoff_tmp \
    -sc 0.95 \
    -s 0.90 \
    -exclude_partial \
    -p 16 \
    target.fasta \
    reference.fasta

With Chromosome Name Mapping

# Create chromosome mapping file (tab-separated)
# ref_chr1    target_scaffold_1
# ref_chr2    target_scaffold_2
liftoff \
    -g reference.gff3 \
    -o lifted.gff3 \
    -chroms chrom_map.txt \
    -p 16 \
    target.fasta \
    reference.fasta

Output

The output GFF3 contains transferred annotations with additional attributes:

LiftOn (Newer Successor)

LiftOn improves on Liftoff by combining Liftoff liftover with MiniProt protein alignment to correct gene models that do not transfer cleanly.

# LiftOn combines Liftoff + MiniProt
lifton \
    -g reference.gff3 \
    -o lifton_annotation.gff3 \
    -ref reference.fasta \
    -p 16 \
    target.fasta

MiniProt (Cross-Species Protein Alignment)

MiniProt aligns protein sequences to a genome with splicing awareness. Ideal for cross-species annotation transfer using proteins from related species.

Basic Usage

# Index target genome
miniprot -t 16 -d target.mpi target_assembly.fasta

# Align proteins to genome
miniprot -t 16 --gff target.mpi reference_proteins.faa > miniprot_alignments.gff

Key Options

Cross-Species Transfer

# Use proteins from closely related species
# -G: Adjust max intron size based on target species
# Vertebrates: -G 500000; Insects: -G 50000; Fungi: -G 5000
miniprot -t 16 --gff -G 500000 target.mpi related_species_proteins.faa > cross_species.gff

Convert MiniProt GFF to Gene Models

import gffutils

def miniprot_gff_to_gene_models(miniprot_gff, output_gff):
    '''Convert MiniProt alignment GFF to standard gene models.

    MiniProt outputs mRNA features with CDS children.
    This adds gene-level parent features for compatibility.
    '''
    db = gffutils.create_db(miniprot_gff, ':memory:', merge_strategy='merge')

    gene_id = 0
    with open(output_gff, 'w') as out:
        out.write('##gff-version 3\n')
        for mrna in db.features_of_type('mRNA'):
            gene_id += 1
            gene_line = f'{mrna.seqid}\tMiniProt\tgene\t{mrna.start}\t{mrna.end}\t{mrna.score}\t{mrna.strand}\t.\tID=mpgene_{gene_id}\n'
            mrna_line = f'{mrna.seqid}\tMiniProt\tmRNA\t{mrna.start}\t{mrna.end}\t{mrna.score}\t{mrna.strand}\t.\tID={mrna.id};Parent=mpgene_{gene_id}\n'
            out.write(gene_line)
            out.write(mrna_line)
            for child in db.children(mrna):
                child_line = f'{child.seqid}\tMiniProt\t{child.featuretype}\t{child.start}\t{child.end}\t{child.score}\t{child.strand}\t{child.frame}\tParent={mrna.id}\n'
                out.write(child_line)

    return output_gff

Distinguish from Orthology-Based Transfer

MiniProt performs protein-to-genome alignment, which maps protein sequences to genomic coordinates with intron prediction. This is different from orthology-based transfer (see comparative-genomics/ortholog-inference), which identifies evolutionary relationships between gene families without genome alignment.

Quality Assessment

Goal: Evaluate annotation transfer quality by comparing gene/transcript counts and validating that transferred CDSs have intact open reading frames.

Approach: Count genes and transcripts in both reference and transferred GFF files to compute a transfer rate, then extract each transferred CDS sequence from the target assembly and check for valid start codon, single stop codon, and correct frame.

import gffutils
import pandas as pd

def compare_annotations(reference_gff, transferred_gff):
    '''Compare reference and transferred annotations for QC.'''
    ref_db = gffutils.create_db(reference_gff, ':memory:', merge_strategy='merge')
    tgt_db = gffutils.create_db(transferred_gff, ':memory:', merge_strategy='merge')

    ref_genes = list(ref_db.features_of_type('gene'))
    tgt_genes = list(tgt_db.features_of_type('gene'))

    ref_mrnas = list(ref_db.features_of_type(['mRNA', 'transcript']))
    tgt_mrnas = list(tgt_db.features_of_type(['mRNA', 'transcript']))

    stats = {
        'ref_genes': len(ref_genes),
        'transferred_genes': len(tgt_genes),
        'transfer_rate': len(tgt_genes) / len(ref_genes) if ref_genes else 0,
        'ref_transcripts': len(ref_mrnas),
        'transferred_transcripts': len(tgt_mrnas),
    }

    print('=== Annotation Transfer QC ===')
    print(f'Reference genes: {stats["ref_genes"]}')
    print(f'Transferred genes: {stats["transferred_genes"]}')
    print(f'Transfer rate: {stats["transfer_rate"]:.1%}')
    print(f'Reference transcripts: {stats["ref_transcripts"]}')
    print(f'Transferred transcripts: {stats["transferred_transcripts"]}')

    # Transfer rate > 95% is excellent for same-species liftover
    # Transfer rate > 80% is typical for closely related species
    # Transfer rate < 70% suggests distant species or assembly issues
    if stats['transfer_rate'] > 0.95:
        print('Quality: Excellent (>95% transfer rate)')
    elif stats['transfer_rate'] > 0.80:
        print('Quality: Good (>80% transfer rate)')
    else:
        print('Quality: Low transfer rate - check assembly quality or species distance')

    return stats

def check_transferred_orfs(transferred_gff, target_fasta):
    '''Check how many transferred CDSs have valid open reading frames.'''
    from Bio import SeqIO

    genome = SeqIO.to_dict(SeqIO.parse(target_fasta, 'fasta'))
    db = gffutils.create_db(transferred_gff, ':memory:', merge_strategy='merge')

    valid, invalid, total = 0, 0, 0
    for cds in db.features_of_type('CDS'):
        total += 1
        seq = genome[cds.seqid].seq[cds.start - 1:cds.end]
        if cds.strand == '-':
            seq = seq.reverse_complement()

        protein = seq.translate()
        if protein.startswith('M') and protein.endswith('*') and protein.count('*') == 1:
            valid += 1
        else:
            invalid += 1

    print(f'\n=== ORF Validation ===')
    print(f'Total CDSs: {total}')
    print(f'Valid ORFs: {valid} ({valid/total:.1%})')
    print(f'Invalid ORFs: {invalid} ({invalid/total:.1%})')

    return valid, invalid, total

Troubleshooting

Many Unmapped Features with Liftoff

• Check assembly contiguity (fragmented assemblies lose features at contig boundaries)
• Relax thresholds: -sc 0.5 -s 0.5
• Verify chromosome naming consistency

MiniProt Misses Short Genes

• Reduce minimum alignment coverage: -C 0.3
• Check that protein sequences include short ORFs

Invalid ORFs After Transfer

• Assembly may have variants causing frameshifts
• Try LiftOn which combines Liftoff + MiniProt for correction
• Consider re-predicting genes de novo in problem regions

Related Skills

• eukaryotic-gene-prediction - De novo prediction alternative
• comparative-genomics/ortholog-inference - Orthology-based functional transfer
• comparative-genomics/synteny-analysis - Synteny context for annotation transfer
• genome-intervals/gtf-gff-handling - Parse and manipulate transferred annotations