Dorado is a basecaller for Oxford Nanopore reads.

• dorado on GitHub

• Module Name: dorado (see the modules page for more information)
• Requires a V100/V100x or newer GPU for basecalling. Alignment is not accelerated.
• Use pod5 input format for optimal performance
• Models are found in ${DORADO_MODELS}
• Example files in ${DORADO_TEST_DATA}

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --gres=gpu:v100x:1,lscratch:200 --mem=16g --cpus-per-task=6
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load dorado/0.8.1
[user@cn3144 ~]$ cd /lscratch/$SLURM_JOB_ID
[user@cn3144 ~]$ cp -rL "${DORADO_TEST_DATA:-none}" input
[user@cn3144 ~]$ ls -lh input
-rw-r--r--. 1 user group 20G Jun  2 17:21 reads.pod5
[user@cn3144 ~]$ # emits unaligned bam by default
[user@cn3144 ~]$ dorado basecaller --device cuda:all ${DORADO_MODELS}/dna_r9.4.1_e8_sup@v3.3 input > output.bam
[user@cn3144 ~]$ ls -lh output.bam
-rw-r--r-- 1 user group 2.1G Jun  2 20:02 output.bam
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Dorado scaling

Dorado scales well to 4 v100X GPUs. For a100 GPUs 3 or fewer GPUs are ideal. Please keep in mind that jobs allocating multiple GPUs may be queued for a longer time waiting for resources.

Create a batch input file (e.g. dorado.sh). For example:

#!/bin/bash
set -e
module load dorado/0.8.1
cd /lscratch/$SLURM_JOB_ID
cp -rL "${DORADO_TEST_DATA:-none}" input
dorado basecaller --device cuda:all ${DORADO_MODELS}/dna_r9.4.1_e8_sup@v3.3 input > output.bam

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=6 --mem=16g --gres=lscratch:50,gpu:v100x:1 --partition=gpu dorado.sh