CNV Caller & Plotter

Detect copy number variations (CNVs) from whole genome sequencing (WGS) data and generate genome-wide visualization plots for cancer genomics, rare disease analysis, and population genetics studies. Provides CNV calling, segmentation analysis, and publication-ready visualization.

Key Capabilities:

• CNV Detection from WGS: Identify copy number gains and losses from aligned sequencing data
• Genomic Segmentation: Divide genome into bins/windows for copy number estimation
• Flexible Input Support: Process BAM, VCF, and other standard genomics formats
• Publication-Quality Plots: Generate genome-wide CNV profiles in PNG, PDF, or SVG formats
• Standard Output Formats: Export CNV calls in BED format for downstream analysis

When to Use

✅ Use this skill when:

• Analyzing cancer genomes to identify somatic copy number alterations (SCNAs)
• Studying rare diseases with suspected copy number variation etiology
• Performing population genetics studies comparing CNV frequencies across groups
• Generating genome-wide CNV visualizations for publications or reports
• Creating BED format CNV calls for integration with other analysis pipelines
• Performing comparative CNV analysis between tumor and normal samples
• Validating CNV calls from SNP arrays with sequencing data

❌ Do NOT use when:

• Working with targeted sequencing panels (exome/targeted capture) → Use specialized tools like CNVkit or ExomeDepth
• Detecting structural variations involving translocations or inversions → Use structural-variant-caller
• Analyzing single-cell RNA-seq data → Use single-cell specific CNV tools (e.g., inferCNV)
• Detecting small indels (<50bp) → Use variant-caller for small variant detection
• Requiring clinical-grade CNV detection for diagnostic purposes → Use validated clinical pipelines with proper QC
• Working with low-coverage data (<10x) → Results may be unreliable; consider SNP array-based methods

Related Skills:

• 上游 (Upstream): fastqc-report-interpreter, alignment-quality-checker, variant-caller
• 下游 (Downstream): circos-plot-generator, go-kegg-enrichment, heatmap-beautifier

Integration with Other Skills

Upstream Skills:

• fastqc-report-interpreter: Assess sequencing quality before CNV calling; low quality data may produce unreliable CNVs
• alignment-quality-checker: Verify BAM file quality and coverage uniformity; uneven coverage causes CNV artifacts
• variant-caller: Generate SNV/indel calls for combined CNV-SNV analysis in cancer samples

Downstream Skills:

• circos-plot-generator: Create circular genome plots integrating CNVs with other genomic features
• go-kegg-enrichment: Perform pathway enrichment on genes within CNV regions
• heatmap-beautifier: Visualize CNV profiles across multiple samples