FridrichMethod 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
Somatic CNV Calling: Detect tumor-specific copy number alterations.
Germline CNV Detection: Identify constitutional CNVs for rare disease.
Allele-Specific Analysis: Determine allele-specific copy number and LOH.
Purity/Ploidy Estimation: Estimate tumor content and genome doubling.
Focal Event Detection: Identify amplifications and deletions of driver genes.
Segmentation Optimization: AI-enhanced breakpoint detection.
Workflow
Input: BAM files (tumor/normal), or targeted panel data.
Coverage Normalization: GC correction, mappability adjustment.
Segmentation: Identify regions of consistent copy number.
Allele-Specific: Calculate B-allele frequency for heterozygosity.
Purity/Ploidy: Estimate sample parameters.
Calling: Assign integer copy number states.
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
| Tool | Application | Key Features |
|---|---|---|
| FACETS | Tumor WES | Purity/ploidy, allele-specific |
| ASCAT | Tumor WGS/arrays | Allele-specific, multi-clone |
| CNVkit | WES/targeted | Hybrid reference approach |
| GATK CNV | WES/WGS | GATK ecosystem integration |
| Purple | WGS | GRIDSS integration, comprehensive |
| CONICS | scRNA-seq | Single-cell CNV inference |
Key Output Metrics
| Metric | Description | Interpretation |
|---|---|---|
| Purity | Tumor fraction | Sample quality |
| Ploidy | Average copy number | Genome doubling |
| LOH | Loss of heterozygosity | Regions of allele loss |
| SCNA burden | Total altered fraction | Genomic instability |
| Focal events | Amplifications/deletions | Driver candidates |
Cancer Driver CNVs
| Gene | Alteration | Cancer Type |
|---|---|---|
| ERBB2 (HER2) | Amplification | Breast, gastric |
| MYC | Amplification | Many cancers |
| EGFR | Amplification | Lung, GBM |
| CDK4/MDM2 | Amplification | Sarcoma, GBM |
| CDKN2A | Deletion | Many cancers |
| RB1 | Deletion | Many cancers |
| PTEN | Deletion | Prostate, GBM |
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
- Coverage depth: Higher = better resolution
- Tumor purity: Low purity challenges calling
- Normal match: Best with matched normal
- Target design: Uniform coverage for panels
- GC bias: Proper normalization critical
Author
AI Group - Biomedical AI Platform