Bio Copy Number Gatk Cnv

Step 1: Preprocess Intervals

Goal: Prepare genomic intervals for read counting, handling both WES and WGS modes.

Approach: Use PreprocessIntervals to bin or merge target intervals with appropriate padding.

# For WES/targeted
gatk PreprocessIntervals \
    -R reference.fa \
    -L targets.interval_list \
    --bin-length 0 \
    --interval-merging-rule OVERLAPPING_ONLY \
    -O preprocessed.interval_list

# For WGS
gatk PreprocessIntervals \
    -R reference.fa \
    --bin-length 1000 \
    --padding 0 \
    -O wgs.interval_list

Step 2: Collect Read Counts

Goal: Count reads per interval for each sample.

Approach: Run CollectReadCounts on each BAM against the preprocessed intervals.

# For each sample
gatk CollectReadCounts \
    -R reference.fa \
    -I sample.bam \
    -L preprocessed.interval_list \
    --interval-merging-rule OVERLAPPING_ONLY \
    -O sample.counts.hdf5

Step 3: Create Panel of Normals

Goal: Build a reference panel from multiple normal samples for denoising.

Approach: Combine normal sample count HDF5 files into a single panel-of-normals using PCA-based denoising.

# Combine multiple normal samples
gatk CreateReadCountPanelOfNormals \
    -I normal1.counts.hdf5 \
    -I normal2.counts.hdf5 \
    -I normal3.counts.hdf5 \
    --minimum-interval-median-percentile 5.0 \
    -O cnv_pon.hdf5

Step 4: Denoise Read Counts

Goal: Remove systematic noise from tumor read counts using the panel of normals.

Approach: Apply DenoiseReadCounts with the PoN to produce standardized and denoised copy ratio profiles.

# Using panel of normals
gatk DenoiseReadCounts \
    -I tumor.counts.hdf5 \
    --count-panel-of-normals cnv_pon.hdf5 \
    --standardized-copy-ratios tumor.standardized.tsv \
    --denoised-copy-ratios tumor.denoised.tsv

Step 5: Collect Allelic Counts

Goal: Capture allele-specific information at known heterozygous SNP sites for LOH detection.

Approach: Run CollectAllelicCounts against common SNP sites to generate allelic count profiles.

# From known SNP sites (for LOH detection)
gatk CollectAllelicCounts \
    -R reference.fa \
    -I tumor.bam \
    -L common_snps.vcf \
    -O tumor.allelicCounts.tsv

Step 6: Model Segments

Goal: Jointly segment copy ratio and allelic data to identify CNV regions.

Approach: Run ModelSegments with denoised ratios and allelic counts from both tumor and matched normal.

# Somatic with matched normal allelic counts
gatk ModelSegments \
    --denoised-copy-ratios tumor.denoised.tsv \
    --allelic-counts tumor.allelicCounts.tsv \
    --normal-allelic-counts normal.allelicCounts.tsv \
    --output-prefix tumor \
    -O results/

# Output files: tumor.cr.seg, tumor.modelFinal.seg, tumor.hets.tsv

Step 7: Call Copy Ratio Segments

Goal: Assign amplification, deletion, or neutral calls to each segment.

Approach: Apply CallCopyRatioSegments to convert continuous log2 ratios into discrete CN states.

gatk CallCopyRatioSegments \
    -I results/tumor.cr.seg \
    -O results/tumor.called.seg

Plotting

Goal: Visualize denoised copy ratios and modeled segments with allelic information.

Approach: Use GATK PlotDenoisedCopyRatios and PlotModeledSegments to generate standardized plots.

# Plot copy ratios and segments
gatk PlotDenoisedCopyRatios \
    --standardized-copy-ratios tumor.standardized.tsv \
    --denoised-copy-ratios tumor.denoised.tsv \
    --sequence-dictionary reference.dict \
    --minimum-contig-length 46709983 \
    --output-prefix tumor \
    -O plots/

# Plot segments with allelic information
gatk PlotModeledSegments \
    --denoised-copy-ratios tumor.denoised.tsv \
    --allelic-counts results/tumor.hets.tsv \
    --segments results/tumor.modelFinal.seg \
    --sequence-dictionary reference.dict \
    --minimum-contig-length 46709983 \
    --output-prefix tumor \
    -O plots/

Germline CNV Workflow

# For germline: use cohort mode
# 1. Collect counts (same as above)

# 2. Determine contig ploidy
gatk DetermineGermlineContigPloidy \
    -I sample1.counts.hdf5 \
    -I sample2.counts.hdf5 \
    --model cohort_ploidy_model \
    --contig-ploidy-priors ploidy_priors.tsv \
    -O ploidy-calls/

# 3. Call germline CNVs
gatk GermlineCNVCaller \
    --run-mode COHORT \
    -I sample1.counts.hdf5 \
    -I sample2.counts.hdf5 \
    --contig-ploidy-calls ploidy-calls/ploidy_calls \
    --annotated-intervals annotated_intervals.tsv \
    --output-prefix cohort \
    -O germline_cnv_calls/

# 4. Post-process calls per sample
gatk PostprocessGermlineCNVCalls \
    --calls-shard-path germline_cnv_calls/cohort-calls \
    --model-shard-path germline_cnv_calls/cohort-model \
    --sample-index 0 \
    --contig-ploidy-calls ploidy-calls/ploidy_calls \
    --sequence-dictionary reference.dict \
    --output-genotyped-intervals sample1.genotyped.tsv \
    --output-denoised-copy-ratios sample1.denoised.tsv \
    -O sample1_segments.vcf

Complete Somatic Pipeline Script

#!/bin/bash
REFERENCE=reference.fa
INTERVALS=targets.interval_list
PON=cnv_pon.hdf5
SNP_SITES=common_snps.vcf
TUMOR=$1
NORMAL=$2
OUTDIR=$3

mkdir -p $OUTDIR

# Collect read counts
gatk CollectReadCounts -R $REFERENCE -I $TUMOR -L $INTERVALS \
    -O $OUTDIR/tumor.counts.hdf5
gatk CollectReadCounts -R $REFERENCE -I $NORMAL -L $INTERVALS \
    -O $OUTDIR/normal.counts.hdf5

# Denoise
gatk DenoiseReadCounts -I $OUTDIR/tumor.counts.hdf5 \
    --count-panel-of-normals $PON \
    --standardized-copy-ratios $OUTDIR/tumor.standardized.tsv \
    --denoised-copy-ratios $OUTDIR/tumor.denoised.tsv

# Allelic counts
gatk CollectAllelicCounts -R $REFERENCE -I $TUMOR -L $SNP_SITES \
    -O $OUTDIR/tumor.allelicCounts.tsv
gatk CollectAllelicCounts -R $REFERENCE -I $NORMAL -L $SNP_SITES \
    -O $OUTDIR/normal.allelicCounts.tsv

# Model and call
gatk ModelSegments \
    --denoised-copy-ratios $OUTDIR/tumor.denoised.tsv \
    --allelic-counts $OUTDIR/tumor.allelicCounts.tsv \
    --normal-allelic-counts $OUTDIR/normal.allelicCounts.tsv \
    --output-prefix tumor -O $OUTDIR/

gatk CallCopyRatioSegments -I $OUTDIR/tumor.cr.seg -O $OUTDIR/tumor.called.seg

Key Output Files

Related Skills

• copy-number/cnvkit-analysis - Alternative CNV caller
• copy-number/cnv-visualization - Plotting results
• alignment-files/bam-statistics - Input BAM QC
• variant-calling/variant-calling - SNP calling for allelic counts