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%