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 or when highest germline calling accuracy is required.
npx skills add BioTender-max/awesome-bio-agent-skills --skill deepvariant --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: DeepVariant 1.6+, GLnexus 1.4+, bcftools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - CLI: `docker run google/deepvariant:<tag> --version` to confirm build - `bcftools --version` and `bcftools --help` to confirm flags If code throws errors, introspect the installed container and adapt the example to match the actual API rather than retrying. # DeepVariant Variant Calling ## How DeepVariant Works DeepVariant reframes variant calling as an image classification problem rather than a statistical genotyping problem. For each candidate variant site, the make_examples step encodes the local read pileup as a 100x221x6-channel image tensor. The six channels encode: read base identity, base quality, mapping quality, strand orientation, read support for the variant allele, and reference base mismatch. A convolutional neural network (CNN) then classifies each pileup image into one of three genotype classes: homozygous reference, heterozygous, or homozygous alternate. This image-based approach is why DeepVariant outperforms statistical callers in difficult genomic contexts such as homopolymer ru
- Version Compatibility
- How DeepVariant Works
- Installation
- Model Selection Guide
- When to Use DeepVariant
- Basic Usage
- Step-by-Step Workflow
- Step 1: Make Examples
- Step 2: Call Variants
- Step 3: Postprocess Variants
- GPU Acceleration
- PacBio HiFi Calling
- ONT Calling
- Exome/Targeted Sequencing
docker pull google/deepvariant:1.6.1
GPU support (NVIDIA GPU + nvidia-container-toolkit required)
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" \
for bam in *.bam; do
done
docker run -v "${PWD}:/data" quay.io/mlin/glnexus:v1.4.1 \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 or when highest germline calling accuracy is required.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill 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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.