deepvariant-caller

DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection

509 stars

Best use case

deepvariant-caller is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection

Teams using deepvariant-caller should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/deepvariant-caller/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/bioinformatics/skills/deepvariant-caller/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/deepvariant-caller/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How deepvariant-caller Compares

Feature / Agent	deepvariant-caller	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# DeepVariant Caller Skill

## Purpose
Enable DeepVariant deep learning variant calling for high-accuracy SNV and indel detection.

## Capabilities
- GPU-accelerated variant calling
- WGS/WES/PacBio mode selection
- Model customization and retraining
- Confidence calibration
- Multi-sample variant calling
- Docker/Singularity deployment

## Usage Guidelines
- Select appropriate model for sequencing type
- Use GPU acceleration when available
- Validate accuracy against benchmark datasets
- Consider container deployment for reproducibility
- Document model version and parameters
- Compare with traditional callers for validation

## Dependencies
- DeepVariant
- Parabricks

## Process Integration
- Whole Genome Sequencing Pipeline (wgs-analysis-pipeline)
- Long-Read Sequencing Analysis (long-read-analysis)
- Analysis Pipeline Validation (pipeline-validation)

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