CNV Pipeline
Complete workflow for detecting copy number variants from exome or targeted sequencing data.
Workflow Overview
BAM files (tumor/normal or germline)
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[1. Target Preparation] --> Create/access target BED
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[2. Coverage Calculation] --> Read depth per target
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[3. Reference Creation] --> Pool of normals
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[4. CNV Calling] --------> Log2 ratios, segmentation
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[5. Visualization] ------> Scatter plots, heatmaps
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[6. Annotation] ---------> Gene-level CNVs
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CNV calls with gene annotations
Primary Path: CNVkit
Step 1: Prepare Target Regions
# If using exome capture kit BED
cnvkit.py target capture_targets.bed \
--annotate refFlat.txt \
--split \
-o targets.bed
# Access regions (off-target for WGS-like sensitivity)
cnvkit.py access genome.fa \
-o access.bed
cnvkit.py antitarget targets.bed \
--access access.bed \
-o antitargets.bed
Step 2: Calculate Coverage
# For each sample
for bam in *.bam; do
sample=$(basename $bam .bam)
# Target coverage
cnvkit.py coverage $bam targets.bed \
-o coverage/${sample}.targetcoverage.cnn
# Antitarget coverage
cnvkit.py coverage $bam antitargets.bed \
-o coverage/${sample}.antitargetcoverage.cnn
done
Step 3: Create Reference (Pool of Normals)
# From normal samples
cnvkit.py reference \
coverage/normal*.targetcoverage.cnn \
coverage/normal*.antitargetcoverage.cnn \
--fasta genome.fa \
-o reference.cnn
# Or flat reference (no normals available)
cnvkit.py reference \
--fasta genome.fa \
--targets targets.bed \
--antitargets antitargets.bed \
-o flat_reference.cnn
Step 4: Call CNVs
for bam in tumor*.bam; do
sample=$(basename $bam .bam)
# Fix and segment
cnvkit.py fix \
coverage/${sample}.targetcoverage.cnn \
coverage/${sample}.antitargetcoverage.cnn \
reference.cnn \
-o cnv/${sample}.cnr
# Segment
cnvkit.py segment cnv/${sample}.cnr \
-o cnv/${sample}.cns
# Call integer copy numbers
cnvkit.py call cnv/${sample}.cns \
-o cnv/${sample}.call.cns
done
Step 5: Visualization
# Scatter plot for single sample
cnvkit.py scatter cnv/tumor1.cnr \
-s cnv/tumor1.cns \
-o plots/tumor1_scatter.pdf
# Chromosome-specific
cnvkit.py scatter cnv/tumor1.cnr \
-s cnv/tumor1.cns \
-c chr17 \
-o plots/tumor1_chr17.pdf
# Diagram (chromosome ideogram)
cnvkit.py diagram cnv/tumor1.cnr \
-s cnv/tumor1.cns \
-o plots/tumor1_diagram.pdf
# Heatmap for multiple samples
cnvkit.py heatmap cnv/*.cns \
-o plots/cohort_heatmap.pdf
Step 6: Export and Annotation
# Export to various formats
cnvkit.py export seg cnv/*.cns -o cnv/cohort.seg
cnvkit.py export vcf cnv/tumor1.call.cns -o cnv/tumor1.vcf
# Gene-level summary
cnvkit.py genemetrics cnv/tumor1.cnr \
-s cnv/tumor1.cns \
--threshold 0.2 \
-o cnv/tumor1_genes.tsv
# Filter for significant CNVs
awk '$6 < -0.4 || $6 > 0.3' cnv/tumor1_genes.tsv > cnv/tumor1_significant_genes.tsv
Batch Processing Script
#!/bin/bash
set -e
TARGETS="targets.bed"
REFERENCE="reference.cnn"
OUTDIR="cnv_results"
mkdir -p ${OUTDIR}/{coverage,cnv,plots}
# Process all tumor samples
for bam in tumor*.bam; do
sample=$(basename $bam .bam)
echo "Processing ${sample}..."
# Coverage
cnvkit.py coverage $bam ${TARGETS} \
-o ${OUTDIR}/coverage/${sample}.targetcoverage.cnn
# Fix
cnvkit.py fix \
${OUTDIR}/coverage/${sample}.targetcoverage.cnn \
${OUTDIR}/coverage/${sample}.antitargetcoverage.cnn \
${REFERENCE} \
-o ${OUTDIR}/cnv/${sample}.cnr
# Segment
cnvkit.py segment ${OUTDIR}/cnv/${sample}.cnr \
-o ${OUTDIR}/cnv/${sample}.cns
# Call
cnvkit.py call ${OUTDIR}/cnv/${sample}.cns \
-o ${OUTDIR}/cnv/${sample}.call.cns
# Plot
cnvkit.py scatter ${OUTDIR}/cnv/${sample}.cnr \
-s ${OUTDIR}/cnv/${sample}.cns \
-o ${OUTDIR}/plots/${sample}.pdf
done
# Cohort heatmap
cnvkit.py heatmap ${OUTDIR}/cnv/*.cns -o ${OUTDIR}/plots/heatmap.pdf
Germline CNV Calling
# For germline analysis (no tumor-normal)
cnvkit.py batch sample*.bam \
--normal normal*.bam \
--targets targets.bed \
--fasta genome.fa \
--output-reference reference.cnn \
--output-dir cnv_output \
--scatter --diagram
# Or use flat reference
cnvkit.py batch sample.bam \
--method hybrid \
--targets targets.bed \
--fasta genome.fa \
--output-dir cnv_output
Parameter Recommendations
| Step |
Parameter |
Value |
| target |
--split |
Yes (for WES) |
| segment |
--method |
cbs (default) |
| call |
--ploidy |
2 (adjust if known) |
| call |
--purity |
Estimate if tumor |
| genemetrics |
--threshold |
0.2 |
Troubleshooting
| Issue |
Likely Cause |
Solution |
| Noisy signal |
Low coverage |
Increase sequencing depth |
| No CNVs |
Flat reference, normal sample |
Check reference creation |
| Many small CNVs |
Over-segmentation |
Increase segment min size |
| Batch effects |
Different capture kits |
Match samples to correct reference |
Complete Pipeline Script
#!/bin/bash
set -e
GENOME="genome.fa"
TARGETS="capture_targets.bed"
REFFLAT="refFlat.txt"
NORMAL_BAMS="normal*.bam"
TUMOR_BAMS="tumor*.bam"
OUTDIR="cnv_results"
mkdir -p ${OUTDIR}/{coverage,cnv,plots,annotation}
# Step 1: Prepare targets
cnvkit.py target ${TARGETS} --annotate ${REFFLAT} --split -o ${OUTDIR}/targets.bed
cnvkit.py access ${GENOME} -o ${OUTDIR}/access.bed
cnvkit.py antitarget ${OUTDIR}/targets.bed --access ${OUTDIR}/access.bed -o ${OUTDIR}/antitargets.bed
# Step 2: Coverage (normals)
for bam in ${NORMAL_BAMS}; do
sample=$(basename $bam .bam)
cnvkit.py coverage $bam ${OUTDIR}/targets.bed -o ${OUTDIR}/coverage/${sample}.targetcoverage.cnn
cnvkit.py coverage $bam ${OUTDIR}/antitargets.bed -o ${OUTDIR}/coverage/${sample}.antitargetcoverage.cnn
done
# Step 3: Reference
cnvkit.py reference ${OUTDIR}/coverage/normal*.cnn --fasta ${GENOME} -o ${OUTDIR}/reference.cnn
# Step 4-5: Process tumors
for bam in ${TUMOR_BAMS}; do
sample=$(basename $bam .bam)
cnvkit.py coverage $bam ${OUTDIR}/targets.bed -o ${OUTDIR}/coverage/${sample}.targetcoverage.cnn
cnvkit.py coverage $bam ${OUTDIR}/antitargets.bed -o ${OUTDIR}/coverage/${sample}.antitargetcoverage.cnn
cnvkit.py fix ${OUTDIR}/coverage/${sample}.targetcoverage.cnn \
${OUTDIR}/coverage/${sample}.antitargetcoverage.cnn \
${OUTDIR}/reference.cnn -o ${OUTDIR}/cnv/${sample}.cnr
cnvkit.py segment ${OUTDIR}/cnv/${sample}.cnr -o ${OUTDIR}/cnv/${sample}.cns
cnvkit.py call ${OUTDIR}/cnv/${sample}.cns -o ${OUTDIR}/cnv/${sample}.call.cns
cnvkit.py scatter ${OUTDIR}/cnv/${sample}.cnr -s ${OUTDIR}/cnv/${sample}.cns -o ${OUTDIR}/plots/${sample}.pdf
cnvkit.py genemetrics ${OUTDIR}/cnv/${sample}.cnr -s ${OUTDIR}/cnv/${sample}.cns -o ${OUTDIR}/annotation/${sample}_genes.tsv
done
echo "Pipeline complete. Results in ${OUTDIR}/"
Related Skills
- copy-number/cnvkit-analysis - CNVkit details
- copy-number/cnv-visualization - Plotting options
- copy-number/cnv-annotation - Gene annotations
- copy-number/gatk-cnv - GATK alternative
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