Agent skill · Data & Analytics

bio-variant-calling-deepvariant

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-variant-calling-deepvariant --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 9 KB
Bundled scripts: yes
Path: skills/bio-variant-calling-deepvariant/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: GATK 4.5+, bcftools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # DeepVariant Variant Calling ## Installation **Goal:** Install DeepVariant via Docker or Singularity container. **Approach:** Pull the pre-built container image matching the target platform (CPU or GPU). ### Docker (Recommended) ```bash docker pull google/deepvariant:1.6.1 # Or with GPU support docker pull google/deepvariant:1.6.1-gpu ``` ### Singularity ```bash singularity pull docker://google/deepvariant:1.6.1 ``` ## Basic Usage **Goal:** Call germline variants from aligned reads using DeepVariant's deep learning model. **Approach:** Run the all-in-one `run_deepvariant` wrapper specifying model type, reference, reads, and output paths. **"Call variants with DeepVariant"** → Convert aligned read pileups into image tensors, classify with a CNN, and output genotyped VCF. ### One-Step Run (run_deepvaria

What's inside
Steps it walks through
  1. Version Compatibility
  2. Installation
  3. Docker (Recommended)
  4. Singularity
  5. Basic Usage
  6. One-Step Run (rundeepvariant)
  7. Model Types
  8. Step-by-Step Workflow
  9. Step 1: Make Examples
  10. Step 2: Call Variants
  11. Step 3: Postprocess Variants
  12. GPU Acceleration
  13. PacBio HiFi Calling
  14. ONT Calling
Ships with 2 files
  • examples/run_deepvariant.sh
  • usage-guide.md
Commands it runs
docker pull google/deepvariant:1.6.1
Or with GPU support
docker pull google/deepvariant:1.6.1-gpu
singularity pull docker://google/deepvariant:1.6.1
docker run -v "${PWD}:/input" -v "${PWD}/output:/output" \
docker run -v "${PWD}:/data" google/deepvariant:1.6.1 \
docker run --gpus all -v "${PWD}:/data" \
Generate gVCFs for each sample
for bam in *.bam; do
done
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-variant-calling-deepvariant skill do?

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-variant-calling-deepvariant --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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