Agent skill
dorado-bench-v2
Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation.
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Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/dorado-bench-v2
SKILL.md
Dorado-Bench v2 - ONT Basecalling
Basecalling toolkit for UM HPC clusters with provenance tracking.
Integration
Run through ont-experiments for provenance tracking:
bash
ont_experiments.py run basecalling exp-abc123 --model sup --output calls.bam --json stats.json
Or standalone:
bash
python3 dorado_basecall.py /path/to/pod5 --model sup --cluster armis2 --output calls.bam
Cluster Configurations
ARMIS2 (sigbio-a40)
yaml
partition: sigbio-a40
account: bleu1
gres: gpu:a40:1
dorado: /nfs/turbo/umms-bleu-secure/programs/dorado-1.1.1-linux-x64/bin/dorado
models: /nfs/turbo/umms-bleu-secure/programs/dorado_models
Great Lakes (gpu_mig40)
yaml
partition: gpu_mig40
account: bleu99
gres: gpu:nvidia_a100_80gb_pcie_3g.40gb:1
Model Tiers
| Tier | Accuracy | ARMIS2 Resources |
|---|---|---|
| fast | ~95% | batch=4096, mem=50G, 24h |
| hac | ~98% | batch=2048, mem=75G, 72h |
| sup | ~99% | batch=1024, mem=100G, 144h |
Options
| Option | Description |
|---|---|
--model TIER |
fast, hac, sup (default: hac) |
--version VER |
Model version (default: v5.0.0) |
--cluster |
armis2 or greatlakes |
--output FILE |
Output BAM file |
--json FILE |
Output JSON statistics |
--slurm FILE |
Generate SLURM script |
--emit-moves |
Include move table |
--modifications MOD |
Enable 5mCG_5hmCG methylation |
SLURM Generation
bash
python3 dorado_basecall.py /path/to/pod5 \
--model sup@v5.0.0 \
--cluster armis2 \
--slurm job.sbatch
sbatch job.sbatch
Event Tracking
When run through ont-experiments, captures:
- Model name and full path
- Model tier/version/chemistry
- Batch size and device
- BAM statistics (reads, qscore, N50)
- SLURM job ID, nodes, GPUs
Methylation Calling
bash
ont_experiments.py run basecalling exp-abc123 \
--model sup \
--modifications 5mCG_5hmCG \
--output calls_5mc.bam
Resources adjusted: memory +50%, batch size -30%
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