medaka
medaka is a tool to create consensus sequences and variant calls from Oxford Nanopore sequencing data. It uses neural networks applied to a pileup of individual sequencing reads against a draft assembly. On Roihu it is provided as a GPU-accelerated build for the GH200 GPU nodes.
License
Medaka is distributed under the Oxford Nanopore Technologies PLC Public License v1.0. The license permits use solely for research purposes, defined as internal research not intended for or directed towards commercial advantage or monetary compensation. Sponsored or grant-funded research is permitted under the license.
Available
- Roihu-GPU: 2.2.2 (module
py-medaka), via thebio-appsmodule (GPU nodes only).
medaka runs on the Roihu GPU (GH200) nodes. Log in to roihu-gpu.csc.fi and load the
modules there so you get the GPU build.
Usage
medaka is part of the bio-apps collection on Roihu. Load the bio-apps module tree and then the medaka module:
The main entry point for polishing a draft assembly is medaka_consensus. medaka
downloads the required model on first use; run it from a writable working directory (for
example in your project's /scratch).
Example batch script
medaka's neural-network inference runs on a GPU, so submit it to a GPU node:
#!/bin/bash
#SBATCH --job-name=medaka
#SBATCH --account=<project>
#SBATCH --output=output_%j.txt
#SBATCH --error=errors_%j.txt
#SBATCH --partition=gpumedium
#SBATCH --time=08:00:00
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --gres=gpu:gh200:1
module load bio-apps/v202603
module load py-medaka/2.2.2
medaka_consensus -i reads.fq.gz -d draft_assembly.fasta -o medaka_out -t $SLURM_CPUS_PER_TASK
Replace <project> with your CSC project (for example project_2001234).
See creating a batch job script for Roihu for more information about running batch jobs, and the GPU partitions for available GPU resources.