bio-consensus-sequences
SkillDev toolsLets your agent create consensus DNA sequences by applying variant data to a reference genome.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the bio-consensus-sequences skill
About this capability
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/bio-consensus-sequences/SKILL.md and read by ahel’s review.
Version Compatibility
Reference examples tested with: BioPython 1.83+, bcftools 1.19+, bedtools 2.31+, minimap2 2.26+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto 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.
Consensus Sequences
"Generate a consensus sequence from my VCF" → Apply called variants to a reference FASTA, producing a sample-specific genome with optional haplotype selection and low-coverage masking.
- CLI:
bcftools consensus -f reference.fa input.vcf.gz - Python:
cyvcf2+Bio.SeqIOfor simple SNP-only cases
Basic Usage
Generate Consensus
bcftools consensus -f reference.fa input.vcf.gz > consensus.fa
Specify Sample
bcftools consensus -f reference.fa -s sample1 input.vcf.gz > sample1.fa
Output to File
bcftools consensus -f reference.fa -o consensus.fa input.vcf.gz
Haplotype Selection
First Haplotype Only
bcftools consensus -f reference.fa -H 1 input.vcf.gz > haplotype1.fa
Second Haplotype Only
bcftools consensus -f reference.fa -H 2 input.vcf.gz > haplotype2.fa
Haplotype Options
| 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) |
IUPAC Codes for Heterozygous Sites
bcftools consensus -f reference.fa -I input.vcf.gz > consensus_iupac.fa
Heterozygous sites encoded with IUPAC ambiguity codes:
- A/G → R
- C/T → Y
- A/C → M
- G/T → K
- A/T → W
- C/G → S
Missing Data Handling
Mark Missing as N
bcftools consensus -f reference.fa -M N input.vcf.gz > consensus.fa
Mark Low Coverage as N
Using a mask BED file:
# 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
Mask Options
| Option | Description |
|---|---|
-m FILE | Mask regions in BED file with N |
-M CHAR | Character for masked regions (default N) |
Region Selection
Specific Region
bcftools consensus -f reference.fa -r chr1:1000-2000 input.vcf.gz > region.fa
Multiple Regions
Use with BED file to extract multiple regions.
Chain Files
Generate Chain File
bcftools consensus -f reference.fa -c chain.txt input.vcf.gz > consensus.fa
Chain files map coordinates between reference and consensus:
- Useful for liftover of annotations
- Required when indels change sequence length
Chain File Format
chain score ref_name ref_size ref_strand ref_start ref_end query_name query_size query_strand query_start query_end id
Sample-Specific Consensus
For Each Sample
for sample in $(bcftools query -l input.vcf.gz); do
bcftools consensus -f reference.fa -s "$sample" input.vcf.gz > "${sample}.fa"
done
Both Haplotypes
sample="sample1"
bcftools consensus -f reference.fa -s "$sample" -H 1 input.vcf.gz > "${sample}_hap1.fa"
bcftools consensus -f reference.fa -s "$sample" -H 2 input.vcf.gz > "${sample}_hap2.fa"
Filtering Before Consensus
PASS Variants Only
bcftools view -f PASS input.vcf.gz | \
bcftools consensus -f reference.fa > consensus.fa
High-Quality Variants Only
bcftools filter -i 'QUAL>=30 && INFO/DP>=10' input.vcf.gz | \
bcftools consensus -f reference.fa > consensus.fa
SNPs Only
bcftools view -v snps input.vcf.gz | \
bcftools consensus -f reference.fa > consensus_snps.fa
Sequence Naming
Default Naming
Output uses reference sequence names.
Custom Prefix
bcftools consensus -f reference.fa -p "sample1_" input.vcf.gz > consensus.fa
Sequences named: sample1_chr1, sample1_chr2, etc.
Common Workflows
Goal: Generate consensus sequences for downstream analyses like phylogenetics, viral surveillance, or gene-level comparison.
Approach: Filter variants to high-quality calls, apply per-sample consensus generation, mask low-coverage regions with N, then combine for multi-sample workflows.
Phylogenetic Analysis Preparation
# 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
Viral Genome Assembly
# 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
Gene-Specific Consensus
# Extract gene region
bcftools consensus -f reference.fa -r chr1:1000000-1010000 \
-s sample1 variants.vcf.gz > gene.fa
Masked Low-Coverage Regions
# 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
Verify Consensus
Check Differences
# 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
Count Changes
# Number of differences
bcftools view -H input.vcf.gz | wc -l
Handling Overlapping Variants
bcftools consensus handles overlapping variants automatically:
- Applies variants in order
- Warns about conflicts
Check for warnings:
bcftools consensus -f reference.fa input.vcf.gz 2>&1 | grep -i warn
cyvcf2 Consensus (Simple Cases)
Manual Consensus Generation
from 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.
Quick Reference
| 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 |
Common Errors
| 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 |
Related Skills
- variant-calling - Generate VCF for consensus
- filtering-best-practices - Filter variants before consensus
- variant-normalization - Normalize indels first
- alignment-files/reference-operations - Reference manipulation
Signals
- GitHub stars
- 3k
- Forks
- 407
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
bio-consensus-sequences- Source
- github.com/freedomintelligence/openclaw-medical-skills