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name: analysis-visualization description: "Plan and interpret DeepVariant VCF stats, runtime-by-region, show_examples, and benchmark-analysis reports safely." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

DeepVariant Analysis Visualization

Use this sub-skill when a user already has DeepVariant outputs or intermediates and needs command previews, report interpretation, pileup-image review, runtime bottleneck analysis, hap.py summary interpretation, or checkpoint-metric summaries.

Do not use this sub-skill to run variant calling, trio calling, pangenome-aware calling, stage orchestration, or model training. Route those workflows to the appropriate calling, pipeline-stage, or training sub-skills. Use this sub-skill after a run has produced a VCF, runtime TSVs/logs, examples TFRecords, hap.py outputs, or training/eval .metrics files.

Start Here

1. Identify the artifact type: final VCF, make_examples runtime TSV, examples TFRecord, hap.py summary/output VCF, runtime log, or training .metrics files.
2. Confirm whether the user wants a non-executing command preview, interpretation of an existing report, or a benchmark/review handoff.
3. Use scripts/report_command_builder.py for safe previews of vcf_stats_report, runtime_by_region_vis, and show_examples; it validates common path and flag mistakes but never executes DeepVariant, Docker, Singularity, hap.py, or native report tools.
4. Keep report generation bounded: use explicit output locations, small --num_records values for pileup review, and --max_examples_to_scan for broad TFRecord scans.
5. If missing inputs require a new DeepVariant run, hand off run planning to the calling or pipeline-stage sub-skills instead of trying to create those inputs here.

Bundled References

• references/reports-and-visualization.md covers VCF stats reports, runtime-by-region reports, show_examples, notebook-to-script translation, command shapes, outputs, and validation checks.
• references/benchmarking.md covers release metrics context, hap.py summaries, PASS-only interpretation, pangenome-aware metrics, false-positive/false-negative review, and print_f1-style checkpoint summaries.
• references/troubleshooting.md maps common report, visualization, benchmark, and notebook-translation symptoms to checks and recoveries.

Decision Rules

• Use --vcf_stats_report=true during a new wrapper run when the user wants a VCF HTML summary produced automatically; use vcf_stats_report afterward when the user already has a compatible single-sample VCF.
• Use --runtime_report=true together with --logging_dir during a wrapper run to create runtime TSVs and an HTML report; use runtime_by_region_vis afterward when the user already has runtime TSVs.
• Use --intermediate_results_dir during calling when the user later wants show_examples; otherwise examples TFRecords may be temporary or hidden inside the runtime environment.
• Use show_examples --vcf with hap.py false-positive or false-negative VCF slices, plus strict --num_records, --regions, or --max_examples_to_scan guardrails for visual inspection.
• Interpret hap.py and release metrics only after recording version, model type, sample, truth set, confident regions, reference build, assay/coverage, hardware, shard count, and optional reporting flags.

Safe Helper

Run the helper from this sub-skill directory to preview commands only:

python scripts/report_command_builder.py --help

Useful modes:

• vcf-stats: previews /opt/deepvariant/bin/vcf_stats_report --input_vcf ... --outfile_base ....
• runtime: previews /opt/deepvariant/bin/runtime_by_region_vis --input ... --output ....
• show-examples: previews /opt/deepvariant/bin/show_examples --examples ... --output ....

Safety Notes

This sub-skill provides post-run analysis guidance and command previews. It must not silently pull containers, run Docker/Singularity/Bazel binaries, execute hap.py, download benchmark datasets, generate thousands of pileup images, mutate runtime configuration, or assume a lightweight Python environment is sufficient for native DeepVariant visualization modules that require TensorFlow and compiled genomics dependencies.