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Get Started Free →Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 50% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 187% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 72% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 301% | 0% |
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Apply variants to reference FASTA using bcftools consensus.
bashbcftools consensus -f reference.fa input.vcf.gz > consensus.fa
bashbcftools consensus -f reference.fa -s sample1 input.vcf.gz > sample1.fa
bashbcftools consensus -f reference.fa -o consensus.fa input.vcf.gz
bashbcftools consensus -f reference.fa -H 1 input.vcf.gz > haplotype1.fa
bashbcftools consensus -f reference.fa -H 2 input.vcf.gz > haplotype2.fa
| Option | Description | |--------|-------------| | -H 1 | First haplotype | | -H 2 | Second haplotype | | -H A | Apply all ALT alleles | | -H R | Apply REF alleles where heterozygous | | -I | Apply IUPAC ambiguity codes (separate flag) |
bashbcftools consensus -f reference.fa -I input.vcf.gz > consensus_iupac.fa
Heterozygous sites encoded with IUPAC ambiguity codes:
bashbcftools consensus -f reference.fa -M N input.vcf.gz > consensus.fa
Using a mask BED file:
bash# Create mask from depth samtools depth input.bam | awk '$3<10 {print $1"\t"$2-1"\t"$2}' > low_coverage.bed # Apply mask bcftools consensus -f reference.fa -m low_coverage.bed input.vcf.gz > consensus.fa
| Option | Description | |--------|-------------| | -m FILE | Mask regions in BED file with N | | -M CHAR | Character for masked regions (default N) |
bashbcftools consensus -f reference.fa -r chr1:1000-2000 input.vcf.gz > region.fa
Use with BED file to extract multiple regions.
bashbcftools consensus -f reference.fa -c chain.txt input.vcf.gz > consensus.fa
Chain files map coordinates between reference and consensus:
chain score ref_name ref_size ref_strand ref_start ref_end query_name query_size query_strand query_start query_end idbashfor sample in $(bcftools query -l input.vcf.gz); do bcftools consensus -f reference.fa -s "$sample" input.vcf.gz > "${sample}.fa" done
bashsample="sample1" bcftools consensus -f reference.fa -s "$sample" -H 1 input.vcf.gz > "${sample}_hap1.fa" bcftools consensus -f reference.fa -s "$sample" -H 2 input.vcf.gz > "${sample}_hap2.fa"
bashbcftools view -f PASS input.vcf.gz | \ bcftools consensus -f reference.fa > consensus.fa
bashbcftools filter -i 'QUAL>=30 && INFO/DP>=10' input.vcf.gz | \ bcftools consensus -f reference.fa > consensus.fa
bashbcftools view -v snps input.vcf.gz | \ bcftools consensus -f reference.fa > consensus_snps.fa
Output uses reference sequence names.
bashbcftools consensus -f reference.fa -p "sample1_" input.vcf.gz > consensus.fa
Sequences named: sample1_chr1, sample1_chr2, etc.
bash# For each sample, generate consensus mkdir -p consensus for sample in $(bcftools query -l cohort.vcf.gz); do bcftools view -s "$sample" cohort.vcf.gz | \ bcftools view -c 1 | \ bcftools consensus -f reference.fa > "consensus/${sample}.fa" done # Combine for alignment cat consensus/*.fa > all_samples.fa
bash# Apply high-quality variants only bcftools filter -i 'QUAL>=30 && INFO/DP>=20' variants.vcf.gz | \ bcftools view -f PASS | \ bcftools consensus -f reference.fa -M N > consensus.fa
bash# Extract gene region bcftools consensus -f reference.fa -r chr1:1000000-1010000 \ -s sample1 variants.vcf.gz > gene.fa
bash# Create mask from coverage samtools depth -a input.bam | \ awk '$3<5 {print $1"\t"$2-1"\t"$2}' | \ bedtools merge > low_coverage.bed # Generate consensus with mask bcftools consensus -f reference.fa -m low_coverage.bed \ variants.vcf.gz > consensus.fa
bash# Align consensus to reference minimap2 -a reference.fa consensus.fa | samtools view -bS > alignment.bam # Or simple comparison diff <(grep -v "^>" reference.fa) <(grep -v "^>" consensus.fa) | head
bash# Number of differences bcftools view -H input.vcf.gz | wc -l
bcftools consensus handles overlapping variants automatically:
Check for warnings:
bashbcftools consensus -f reference.fa input.vcf.gz 2>&1 | grep -i warn
pythonfrom cyvcf2 import VCF from Bio import SeqIO # Load reference ref_dict = {rec.id: str(rec.seq) for rec in SeqIO.parse('reference.fa', 'fasta')} # Apply variants (SNPs only, simplified) vcf = VCF('input.vcf.gz') changes = {} for variant in vcf: if variant.is_snp and len(variant.ALT) == 1: chrom = variant.CHROM pos = variant.POS - 1 # 0-based if chrom not in changes: changes[chrom] = {} changes[chrom][pos] = variant.ALT[0] # Apply changes for chrom, positions in changes.items(): seq = list(ref_dict[chrom]) for pos, alt in positions.items(): seq[pos] = alt ref_dict[chrom] = ''.join(seq) # Write output with open('consensus.fa', 'w') as f: for chrom, seq in ref_dict.items(): f.write(f'>{chrom}\n{seq}\n')
Note: Use bcftools consensus for production - handles indels and edge cases properly.
| Task | Command | |------|---------| | Basic consensus | bcftools consensus -f ref.fa in.vcf.gz | | Specific sample | bcftools consensus -f ref.fa -s sample in.vcf.gz | | Haplotype 1 | bcftools consensus -f ref.fa -H 1 in.vcf.gz | | IUPAC codes | bcftools consensus -f ref.fa -I in.vcf.gz | | With mask | bcftools consensus -f ref.fa -m mask.bed in.vcf.gz | | Generate chain | bcftools consensus -f ref.fa -c chain.txt in.vcf.gz | | Specific region | bcftools consensus -f ref.fa -r chr1:1-1000 in.vcf.gz |
| Error | Cause | Solution | |-------|-------|----------| | not indexed | VCF not indexed | Run bcftools index | | sequence not found | Chromosome mismatch | Check chromosome names | | overlapping records | Variants overlap | Usually OK, check warnings | | REF does not match | Wrong reference | Use same reference as caller |
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