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bio-copy-number-gatk-cnv

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

FreedomIntelligence/OpenClaw-Medical-Skills

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含まれるファイル（3）

SKILL.md8.1 KB
examples/run_gatk_cnv.sh1.2 KB
usage-guide.md5.5 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Version Compatibility

Reference examples tested with: GATK 4.5+

Before using code patterns, verify installed versions match. If versions differ:

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.

GATK CNV Workflow

"Call CNVs using GATK best practices" → Collect read counts, build a panel of normals, denoise tumor coverage, model segments with allelic counts, and call copy ratio states.

CLI: gatk CollectReadCounts → gatk DenoiseReadCounts → gatk ModelSegments → gatk CallCopyRatioSegments

Somatic CNV Workflow Overview

1. PreprocessIntervals → intervals.interval_list
2. CollectReadCounts → sample.counts.hdf5
3. CreateReadCountPanelOfNormals → pon.hdf5
4. DenoiseReadCounts → sample.denoised.tsv
5. CollectAllelicCounts → sample.allelicCounts.tsv
6. ModelSegments → sample.modelFinal.seg
7. CallCopyRatioSegments → sample.called.seg

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

File	Description
.counts.hdf5	Raw read counts per interval
.denoised.tsv	Denoised log2 copy ratios
.modelFinal.seg	Segmented copy ratios with confidence
.called.seg	Final called segments with CN state
.hets.tsv	Heterozygous SNP allelic counts

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

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