Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 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.

VCF Manipulation

Merge, concat, sort, and compare VCF files using bcftools.

Operations Overview

bcftools merge

Goal: Combine VCF files from different samples into a single multi-sample VCF.

Approach: Use bcftools merge to join files with different sample columns at shared genomic positions.

"Merge my per-sample VCFs into one file" → Combine variant records from multiple samples into a single multi-sample VCF.

Combine multiple VCF files with different samples at the same positions.

Basic Merge

bcftools merge sample1.vcf.gz sample2.vcf.gz -Oz -o merged.vcf.gz

Merge Multiple Files

bcftools merge *.vcf.gz -Oz -o all_samples.vcf.gz

Merge from File List

# files.txt: one VCF path per line
bcftools merge -l files.txt -Oz -o merged.vcf.gz

Handle Missing Genotypes

# Output missing genotypes as ./. (default)
bcftools merge sample1.vcf.gz sample2.vcf.gz -Oz -o merged.vcf.gz

# Output missing as reference (0/0)
bcftools merge --missing-to-ref sample1.vcf.gz sample2.vcf.gz -Oz -o merged.vcf.gz

Force Sample Names

When sample names conflict:

bcftools merge --force-samples sample1.vcf.gz sample2.vcf.gz -Oz -o merged.vcf.gz

Merge Specific Regions

bcftools merge -r chr1:1000000-2000000 sample1.vcf.gz sample2.vcf.gz -Oz -o merged.vcf.gz

bcftools concat

Goal: Concatenate VCF files that cover different genomic regions for the same samples.

Approach: Use bcftools concat to join region-split files (e.g., per-chromosome VCFs) in order.

Combine VCF files with same samples from different regions.

Concatenate Chromosomes

bcftools concat chr1.vcf.gz chr2.vcf.gz chr3.vcf.gz -Oz -o genome.vcf.gz

Concatenate All Chromosomes

bcftools concat chr*.vcf.gz -Oz -o genome.vcf.gz

From File List

# files.txt: one VCF path per line (in order)
bcftools concat -f files.txt -Oz -o concatenated.vcf.gz

Allow Overlapping Regions

bcftools concat -a chr1_part1.vcf.gz chr1_part2.vcf.gz -Oz -o chr1.vcf.gz

Remove Duplicates

bcftools concat -a -d all file1.vcf.gz file2.vcf.gz -Oz -o merged.vcf.gz

Options for -d:

• snps - Remove duplicate SNPs
• indels - Remove duplicate indels
• both - Remove duplicate SNPs and indels
• all - Remove all duplicates
• exact - Remove exact duplicates only

bcftools sort

Goal: Sort a VCF file by chromosome and position.

Approach: Use bcftools sort with optional temp directory and memory limits for large files.

Sort VCF by chromosome and position.

Basic Sort

bcftools sort input.vcf -Oz -o sorted.vcf.gz

With Temporary Directory

For large files:

bcftools sort -T /tmp input.vcf.gz -Oz -o sorted.vcf.gz

Memory Limit

bcftools sort -m 4G input.vcf.gz -Oz -o sorted.vcf.gz

bcftools isec

Goal: Identify shared and private variants between two or more VCF files.

Approach: Use bcftools isec to partition variants into private-to-each-file and shared subsets.

"Find variants called by both GATK and bcftools" → Intersect two call sets to identify concordant and discordant variants.

Intersect and compare VCF files.

Find Shared Variants

bcftools isec -p output_dir sample1.vcf.gz sample2.vcf.gz

Creates:

• 0000.vcf - Private to sample1
• 0001.vcf - Private to sample2
• 0002.vcf - Shared (sample1 records)
• 0003.vcf - Shared (sample2 records)

Output Compressed

bcftools isec -p output_dir -Oz sample1.vcf.gz sample2.vcf.gz

Intersection Only

bcftools isec -p output_dir -n=2 sample1.vcf.gz sample2.vcf.gz
# Only outputs variants present in exactly 2 files

Comparison Options

Two-File Intersection

# Variants in both files
bcftools isec -n=2 -w1 sample1.vcf.gz sample2.vcf.gz -Oz -o shared.vcf.gz

# Variants only in sample1
bcftools isec -n~10 -w1 sample1.vcf.gz sample2.vcf.gz -Oz -o only_sample1.vcf.gz

Complement Mode

# Variants in file1 not in file2
bcftools isec -C sample1.vcf.gz sample2.vcf.gz -Oz -o unique.vcf.gz

Subsetting VCF Files

Goal: Extract a subset of samples or regions from a multi-sample VCF.

Approach: Use bcftools view with -s (samples) or -r/-R (regions) flags to create targeted subsets.

Extract Samples

bcftools view -s sample1,sample2 input.vcf.gz -Oz -o subset.vcf.gz

Exclude Samples

bcftools view -s ^sample3 input.vcf.gz -Oz -o without_sample3.vcf.gz

From Sample List File

# samples.txt: one sample name per line
bcftools view -S samples.txt input.vcf.gz -Oz -o subset.vcf.gz

Extract Region

bcftools view -r chr1:1000000-2000000 input.vcf.gz -Oz -o region.vcf.gz

Extract Multiple Regions

bcftools view -R regions.bed input.vcf.gz -Oz -o targets.vcf.gz

Renaming Samples

Goal: Rename sample columns in a VCF header.

Approach: Use bcftools reheader with a mapping file of old-to-new sample names.

Single Sample

echo "old_name new_name" > rename.txt
bcftools reheader -s rename.txt input.vcf.gz -o renamed.vcf.gz

Multiple Samples

# rename.txt format: old_name new_name
cat > rename.txt << EOF
sample1 patient_001
sample2 patient_002
sample3 patient_003
EOF

bcftools reheader -s rename.txt input.vcf.gz -o renamed.vcf.gz

Splitting VCF Files

Goal: Split a multi-sample or multi-chromosome VCF into separate files.

Approach: Iterate over samples or chromosomes and extract each with bcftools view.

Split by Sample

for sample in $(bcftools query -l input.vcf.gz); do
    bcftools view -s "$sample" input.vcf.gz -Oz -o "${sample}.vcf.gz"
done

Split by Chromosome

for chr in $(bcftools view -h input.vcf.gz | grep "^##contig" | sed 's/.*ID=\([^,]*\).*/\1/'); do
    bcftools view -r "$chr" input.vcf.gz -Oz -o "${chr}.vcf.gz"
done

Split Multiallelic Sites

bcftools norm -m-any input.vcf.gz -Oz -o split.vcf.gz

Common Workflows

Goal: Execute typical multi-step VCF manipulation tasks.

Approach: Chain merge, concat, isec, and view operations for cohort assembly, caller comparison, and filtering.

Merge Cohort VCFs

# Create file list
ls *.vcf.gz > files.txt

# Merge all samples
bcftools merge -l files.txt -Oz -o cohort.vcf.gz
bcftools index cohort.vcf.gz

Combine Chromosome VCFs

# After parallel variant calling by chromosome
bcftools concat chr{1..22}.vcf.gz chrX.vcf.gz chrY.vcf.gz -Oz -o genome.vcf.gz
bcftools index genome.vcf.gz

Compare Two Callers

# Find variants called by both GATK and bcftools
bcftools isec -p comparison gatk.vcf.gz bcftools.vcf.gz

# Count results
wc -l comparison/*.vcf

Extract Passing Variants

bcftools view -f PASS input.vcf.gz -Oz -o pass_only.vcf.gz
bcftools index pass_only.vcf.gz

cyvcf2 Python Operations

Goal: Perform VCF set operations programmatically in Python.

Approach: Use cyvcf2 for position-based comparisons and record concatenation; use bcftools merge for true multi-sample merging.

Note: True VCF merging (combining samples at matching positions) is complex. Use bcftools merge for production work. cyvcf2 is better for filtering/querying.

Concatenate Records (Not True Merge)

from cyvcf2 import VCF, Writer

# WARNING: This concatenates records, not a true merge
# For actual merging of samples, use bcftools merge
vcf1 = VCF('file1.vcf.gz')
writer = Writer('combined.vcf', vcf1)

for variant in vcf1:
    writer.write_record(variant)

writer.close()
vcf1.close()

Find Shared Positions

from cyvcf2 import VCF

# Load positions from first VCF
vcf1_positions = set()
for variant in VCF('sample1.vcf.gz'):
    vcf1_positions.add((variant.CHROM, variant.POS))

# Check second VCF
shared = 0
unique = 0
for variant in VCF('sample2.vcf.gz'):
    if (variant.CHROM, variant.POS) in vcf1_positions:
        shared += 1
    else:
        unique += 1

print(f'Shared: {shared}')
print(f'Unique to sample2: {unique}')

Quick Reference

Common Errors

Related Skills

• vcf-basics - View and query VCF files
• filtering-best-practices - Filter variants before manipulation
• variant-normalization - Normalize before comparing
• vcf-statistics - Compare statistics after manipulation