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name: germline-calling description: "Plan, validate, and adapt standard DeepVariant germline run_deepvariant workflows for WGS, WES, long-read, hybrid, MAS-Seq, and RNA-seq data." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

DeepVariant Germline Calling

Use this sub-skill when the user needs a safe plan, command, or preflight review for single-sample DeepVariant germline calling through run_deepvariant. It covers WGS, WES, PACBIO, ONT_R104, HYBRID_PACBIO_ILLUMINA, MASSEQ, and RNASEQ model types in DeepVariant 1.10.0.

Do not use this sub-skill for trio or duo family calling, pangenome GBZ workflows, stage-by-stage tuning, custom model training, or report interpretation. Route those requests to ../trio-calling/SKILL.md, ../pangenome-aware-calling/SKILL.md, ../pipeline-stages/SKILL.md, ../training-custom-models/SKILL.md, or ../analysis-visualization/SKILL.md.

Trigger Phrases

• "Run DeepVariant on a BAM/CRAM", "make a germline VCF", "DeepVariant WGS/WES command", "PacBio/ONT DeepVariant", "hybrid PacBio Illumina calling", "MAS-Seq/RNA-seq DeepVariant".
• "Add gVCF", "restrict to chr20 or a BED", "use Docker/Singularity", "GPU DeepVariant", "dry run the wrapper", "haploid chrX/chrY", "disable small model", "customized model for inference".

Workflow Outline

1. Confirm assay, sample context, reference build, read technology, and whether a standard released model or a customized model is intended.
2. Select exactly one --model_type and validate compatibility with --ref, --reads, --regions, custom-model metadata, haploid/PAR settings, and desired outputs.
3. Choose Docker or Singularity command shape and ensure every host input, output, index, region BED, PAR BED, log directory, intermediate directory, and model path is mounted under the same container paths used in flags.
4. Prefer --dry_run=true for planning or stage inspection; do not run full variant calling, image pulls, downloads, GPU setup, or host runtime changes without user confirmation.
5. Use scripts/deepvariant_command_builder.py for local static validation and command preview when the user has host paths.
6. For lower-level flags discovered through dry run or extra args, keep the top-level command here and route detailed stage customization to ../pipeline-stages/SKILL.md.

Read/Run Links

• references/workflows.md gives Docker/Singularity command recipes, per-assay templates, CRAM/bgzip reference planning, dry-run behavior, outputs, and validation order.
• references/model-types-and-flags.md explains model types, required flags, gVCF, VCF stats, runtime logging, haploid/PAR flags, small model controls, custom model metadata, and extra-args formatting.
• references/troubleshooting.md maps common failures to recovery steps for missing indexes, contig mismatches, wrong mounts, GPU confusion, wrong model type, haploid/PAR misuse, gVCF surprises, custom models, and make_examples_extra_args formatting.
• scripts/deepvariant_command_builder.py prints a Docker or Singularity run_deepvariant command without executing DeepVariant.

Safe Helper

Run the helper locally to validate obvious path/index issues and print a command preview:

python scripts/deepvariant_command_builder.py --help

The helper never runs Docker, Singularity, run_deepvariant, or genomics tools. It validates required inputs, model type, common index companions, output parents, custom model metadata, region literals against a readable .fai, and risky flag combinations; header-level BAM/CRAM and reference compatibility still require user-side checks.

Decision Rules

• Use --output_gvcf only when downstream cohort merging, joint genotyping, or reference-block output is needed; expect larger outputs and heavier postprocessing.
• Use --vcf_stats_report=true for an HTML VCF summary, and use --logging_dir plus --runtime_report=true for make-examples runtime diagnostics.
• Use --haploid_contigs only for samples/regions that should be re-genotyped as haploid; pair it with a reference-matched --par_regions_bed when PAR regions must remain diploid.
• Use --disable_small_model=true when the assay recipe calls for it, when custom model metadata is uncertain, or when every candidate should pass through the CNN.
• Use --customized_model only with compatible model.example_info.json metadata or --customized_model_json; otherwise model-specific example-generation flags can be wrong.

Safety Notes

Official production execution depends on Docker/Bazel binaries or released containers, TensorFlow/compiled modules, and user genomics data. The lightweight Python import environment is useful for inspection only and is not sufficient for production variant calling.