Copy Number

Copy Number

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially CNVkit-style and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for copy-number estimation, segmentation, annotation, and visualization in sequencing-based assays.

When To Use This Skill

• use when the task is CNV calling or copy-number visualization
• use when coverage-based segment inference is needed for tumor or cohort samples
• use when the user needs gene-level CNV summaries or segment plots

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• coverage or ratio data
• target bins or intervals
• sample metadata

Expected Outputs

• CNV segments
• gene-level CNV tables
• CNV plots

Preferred Tools

• CNVkit-style workflows
• GATK CNV-style workflows
• pandas
• matplotlib

Starter Pattern

Preferred starting point: CNVkit-style
Inputs: coverage or ratio data, target bins or intervals, sample metadata
Outputs: CNV segments, gene-level CNV tables, CNV plots

Workflow

1. Confirm assay context

Clarify tumor-normal versus tumor-only design and target capture versus genome-wide coverage.

2. Generate or import coverage summaries

Build bin- or target-level signals suitable for segmentation.

3. Call segments

Infer copy-number segments and classify gains, losses, or focal events.

4. Annotate to genes and loci

Map segments to biologically relevant genes and recurrent regions.

5. Report with visualization

Produce chromosome-level plots and gene-centric summaries.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• CNV segments
• gene-level CNV tables
• CNV plots

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Record reference build, caller assumptions, and filtering rules in the final outputs.
• Separate raw calls from filtered or interpreted results.

Anti-Patterns

• treating noisy ratio shifts as confident focal events without segmentation support
• ignoring tumor purity or ploidy context when it matters
• reporting copy-number calls without genome build and binning details

Related Skills

• Variant Calling
• Long-Read Genomics
• Genome Assembly
• Comparative Genomics

Optional Supplements

• None required for the first pass.