Variant Calling

Variant Calling

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially GATK-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 small-variant and structural-variant discovery, filtering, annotation, and interpretation from sequencing data.

When To Use This Skill

• use when the user asks for germline, somatic, or structural variant calling
• use when BAM or CRAM files and a reference genome are available
• use when VCF generation, filtering, annotation, or interpretation is needed

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

• aligned reads
• reference genome
• optional truth set or panel resources

Expected Outputs

• VCF files
• filtered variant tables
• annotation summaries

Preferred Tools

• GATK-style workflows
• DeepVariant-style workflows
• bcftools
• pandas

Starter Pattern

Preferred starting point: GATK-style
Inputs: aligned reads, reference genome, optional truth set or panel resources
Outputs: VCF files, filtered variant tables, annotation summaries

Workflow

1. Define the variant task

Separate germline, somatic, and structural variant paths early because assumptions differ.

2. Check alignment quality

Review coverage, duplicate rates, contamination indicators, and reference compatibility before calling.

3. Call and filter variants

Use caller-appropriate best practices and keep raw versus filtered outputs distinct.

4. Annotate and prioritize

Attach gene, consequence, frequency, and clinical context before interpretation.

5. Export reproducible artifacts

Save VCFs, filter criteria, annotation tables, and QC 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:
• VCF files
• filtered variant tables
• annotation summaries

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

• mixing germline and somatic assumptions
• interpreting unfiltered calls as final findings
• forgetting to record the reference build and caller version

Related Skills

• Copy Number
• Long-Read Genomics
• Genome Assembly
• Comparative Genomics

Optional Supplements

• pysam
• tiledbvcf