bio-consensus-sequences
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
Best use case
bio-consensus-sequences is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
Teams using bio-consensus-sequences should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bio-consensus-sequences/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bio-consensus-sequences Compares
| Feature / Agent | bio-consensus-sequences | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
## 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>` 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.
# 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.SeqIO` for simple SNP-only cases
## Basic Usage
### Generate Consensus
```bash
bcftools consensus -f reference.fa input.vcf.gz > consensus.fa
```
### Specify Sample
```bash
bcftools consensus -f reference.fa -s sample1 input.vcf.gz > sample1.fa
```
### Output to File
```bash
bcftools consensus -f reference.fa -o consensus.fa input.vcf.gz
```
## Haplotype Selection
### First Haplotype Only
```bash
bcftools consensus -f reference.fa -H 1 input.vcf.gz > haplotype1.fa
```
### Second Haplotype Only
```bash
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
```bash
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
```bash
bcftools consensus -f reference.fa -M N input.vcf.gz > consensus.fa
```
### Mark Low Coverage as N
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
```
### 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
```bash
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
```bash
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
```bash
for sample in $(bcftools query -l input.vcf.gz); do
bcftools consensus -f reference.fa -s "$sample" input.vcf.gz > "${sample}.fa"
done
```
### Both Haplotypes
```bash
sample="sample1"
bcftools consensus -f reference.fa -s "$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
```bash
bcftools view -f PASS input.vcf.gz | \
bcftools consensus -f reference.fa > consensus.fa
```
### High-Quality Variants Only
```bash
bcftools filter -i 'QUAL>=30 && INFO/DP>=10' input.vcf.gz | \
bcftools consensus -f reference.fa > consensus.fa
```
### SNPs Only
```bash
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
```bash
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
```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
```
### Viral Genome Assembly
```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
```
### Gene-Specific Consensus
```bash
# Extract gene region
bcftools consensus -f reference.fa -r chr1:1000000-1010000 \
-s sample1 variants.vcf.gz > gene.fa
```
### Masked Low-Coverage Regions
```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
```
## Verify Consensus
### Check Differences
```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
```
### Count Changes
```bash
# 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:
```bash
bcftools consensus -f reference.fa input.vcf.gz 2>&1 | grep -i warn
```
## cyvcf2 Consensus (Simple Cases)
### Manual Consensus Generation
```python
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
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