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name: deepvariant description: "Use DeepVariant and DeepTrio for genomics variant-calling command planning, input validation, pipeline-stage troubleshooting, custom training, and report analysis." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

DeepVariant Repo Skill

Use this repo skill when a user asks for help with DeepVariant, DeepTrio, pangenome-aware DeepVariant, DeepVariant pipeline stages, custom DeepVariant training, or DeepVariant output analysis. It distills DeepVariant 1.10.0 repository evidence into self-contained routing, references, and safe command-planning helpers.

This skill is primarily for planning, validating, adapting, and troubleshooting workflows. Do not assume a lightweight Python package import is enough to run production variant calling: official execution normally uses released Docker/Singularity images or Bazel-built binaries with TensorFlow, compiled genomics modules, and user-provided genomics data.

Route Requests

User intent	Read next	Why
Single-sample germline calling with run_deepvariant, WGS/WES/PacBio/ONT/hybrid/MAS-Seq/RNA-seq, gVCF, haploid/PAR, Docker/Singularity, custom model inference	sub-skills/germline-calling/SKILL.md	Owns standard DeepVariant command construction and preflight checks.
Trio or duo family calling with child/parent reads, DeepTrio outputs, gVCFs, or GLnexus merge handoff	sub-skills/trio-calling/SKILL.md	Owns DeepTrio family-specific flags, outputs, and failure modes.
Pangenome-aware DeepVariant with GBZ inputs, pangenome reference/sample names, shared memory, or pangenome WGS/WES	sub-skills/pangenome-aware-calling/SKILL.md	Owns run_pangenome_aware_deepvariant and GBZ-specific planning.
Separate make_examples, call_variants, postprocess_variants, sharded TFRecords, gVCF internals, *_extra_args, or stage failures	sub-skills/pipeline-stages/SKILL.md	Owns lower-level stage contracts and sharded path semantics.
Custom training, labeled examples, dv_config, fine-tuning, checkpoint export, model.example_info.json, or --customized_model readiness	sub-skills/training-custom-models/SKILL.md	Owns training data, config, checkpoint, and custom-model handoff.
VCF stats HTML, runtime-by-region report, show_examples, hap.py summaries, benchmark interpretation, or visual inspection	sub-skills/analysis-visualization/SKILL.md	Owns post-run report and visualization workflows.

Shared References

• references/install-and-runtime.md explains Docker/Singularity, source-build, GPU/CPU, mount, image, and lightweight Python inspection constraints.
• references/model-and-data-compatibility.md explains model-type selection, reference/read/index contracts, contig/region compatibility, gVCF, sample naming, and custom-model metadata.
• references/troubleshooting.md covers cross-cutting install, container, data, model, optional dependency, and workflow-routing failures.
• references/repo-provenance.md records the DeepVariant source snapshot used to generate this skill and when to refresh it.
• references/repo-routing-metadata.json is structured metadata used by repo-skills-router during managed import.

Shared Helpers

Run these only as lightweight local validators or note generators. They never execute Docker, Singularity, Bazel, DeepVariant binaries, TensorFlow, hap.py, GLnexus, network downloads, or large genomics IO.

python scripts/deepvariant_input_check.py --help
python scripts/docker_command_notes.py --help

• scripts/deepvariant_input_check.py checks common FASTA/read/index/region/model/output path mistakes for DeepVariant-style workflows.
• scripts/docker_command_notes.py prints workflow-specific container image, mount, GPU, and command-review reminders.

Start Here

1. Identify the workflow family before giving commands: single-sample, family/trio, pangenome-aware, stage-level, training/custom model, or analysis/reporting.
2. Collect the minimum data contract: reference FASTA and .fai, sorted indexed BAM/CRAM, sample names where required, model type, regions or BED if restricted, output VCF/gVCF paths, runtime engine, and whether Docker/Singularity/GPU is approved.
3. Use the nearest sub-skill helper to preview commands or the root input checker to catch missing companions before recommending execution.
4. Treat full runs, image pulls, GPU setup, native tests, benchmark downloads, Beam/Dataflow jobs, GLnexus merge runs, and source builds as conditional actions that need explicit user approval and real data access.
5. Keep final user commands self-contained: all mounted host paths must match the container-visible paths used in flags, and every output parent directory must be mounted and writable.

Important Boundaries

• Do not use this skill for read alignment, reference genome construction, raw sequencing QC, or general genomics preprocessing except where those inputs must be validated for DeepVariant.
• Do not treat DeepSomatic as covered beyond a related repository note; use a DeepSomatic-specific skill or docs when the user asks for somatic calling.
• Do not silently install TensorFlow, CUDA toolkits, Docker, Singularity, GLnexus, hap.py, Apache Beam, or cloud dependencies.
• Do not present skipped native tests or dry-run command previews as proof that a production variant-calling run passed.
• Do not rely on the original repository checkout for future use; all actionable runtime guidance and helpers are bundled in this generated skill.