Cnv Caller Agent

name: 'cnv-caller-agent' description: 'AI-enhanced copy number variation calling and analysis from sequencing data for cancer genomics, constitutional CNV detection, and chromosomal aberration characterization.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

CNV Caller Agent

The CNV Caller Agent provides comprehensive AI-enhanced copy number variation analysis from WGS, WES, and targeted sequencing for cancer genomics and constitutional CNV detection.

When to Use This Skill

• When calling somatic CNVs from tumor-normal paired sequencing.
• To detect constitutional CNVs from germline sequencing.
• For allele-specific copy number analysis.
• When characterizing focal amplifications and deletions in cancer.
• To assess tumor purity and ploidy from CNV data.

Core Capabilities

1. Somatic CNV Calling: Detect tumor-specific copy number alterations.

2. Germline CNV Detection: Identify constitutional CNVs for rare disease.

3. Allele-Specific Analysis: Determine allele-specific copy number and LOH.

4. Purity/Ploidy Estimation: Estimate tumor content and genome doubling.

5. Focal Event Detection: Identify amplifications and deletions of driver genes.

6. Segmentation Optimization: AI-enhanced breakpoint detection.

Workflow

1. Input: BAM files (tumor/normal), or targeted panel data.

2. Coverage Normalization: GC correction, mappability adjustment.

3. Segmentation: Identify regions of consistent copy number.

4. Allele-Specific: Calculate B-allele frequency for heterozygosity.

5. Purity/Ploidy: Estimate sample parameters.

6. Calling: Assign integer copy number states.

7. Output: Segmented CNV calls, purity/ploidy, driver events.

Example Usage

User: "Call somatic copy number alterations from this tumor-normal WES pair."

Agent Action:

python3 Skills/Genomics/CNV_Caller_Agent/cnv_caller.py \
    --tumor tumor.bam \
    --normal normal.bam \
    --reference GRCh38.fa \
    --method facets \
    --targets exome_targets.bed \
    --driver_genes cancer_genes.txt \
    --output cnv_results/

CNV Calling Methods

Key Output Metrics

Cancer Driver CNVs

AI/ML Enhancements

Segmentation:

• Deep learning for breakpoint detection
• Noise reduction in low-coverage data
• Improved sensitivity for focal events

Quality Prediction:

• Sample quality scoring
• Artifact detection
• Confidence estimation

Driver Prioritization:

• GISTIC-style analysis
• Functional impact scoring
• Pan-cancer frequency context

Allele-Specific Copy Number

Total CN = Major allele + Minor allele

Examples:
- Normal: 1 + 1 = 2 (diploid)
- CN gain: 2 + 1 = 3 (trisomy)
- CN-LOH: 2 + 0 = 2 (normal total, LOH)
- Homozygous deletion: 0 + 0 = 0
- High amplification: 10 + 0 = 10 (focal amp)

Prerequisites

• Python 3.10+
• CNV calling tools (FACETS, CNVkit, etc.)
• Reference genome and annotations
• Sufficient memory for WGS (16GB+)

Related Skills

• Variant_Interpretation - For CNV annotation
• HRD_Analysis_Agent - For HRD scoring from CNV
• Pan_Cancer_MultiOmics_Agent - For pan-cancer CNV context

Quality Considerations

1. Coverage depth: Higher = better resolution
2. Tumor purity: Low purity challenges calling
3. Normal match: Best with matched normal
4. Target design: Uniform coverage for panels
5. GC bias: Proper normalization critical

Author

AI Group - Biomedical AI Platform