Claude-skill-registry 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.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/dorado-bench-v2" ~/.claude/skills/majiayu000-claude-skill-registry-dorado-bench-v2 && rm -rf "$T"

manifest: 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:

ont_experiments.py run basecalling exp-abc123 --model sup --output calls.bam --json stats.json

Or standalone:

python3 dorado_basecall.py /path/to/pod5 --model sup --cluster armis2 --output calls.bam

Cluster Configurations

ARMIS2 (sigbio-a40)

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)

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

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

ont_experiments.py run basecalling exp-abc123 \
  --model sup \
  --modifications 5mCG_5hmCG \
  --output calls_5mc.bam

Resources adjusted: memory +50%, batch size -30%