Cite:
- Detecting neurodegenerative changes in glaucoma using deep mean kurtosis-curve–corrected tractometry Loxlan W. Kasa, William Schierding, Eryn Kwon, Samantha Holdsworth, Helen V Danesh-Meyer medRxiv 2025.06.05.25329075; doi: https://doi.org/10.1101/2025.06.05.25329075
Snakemake workflow for anatomically guided tracking
Inputs:
- participants.tsv with target subject IDs
- For each target subject:
- Freesurfer processed data
- DWI data
- Singularity containers required:
- [mrtrix3, freesurfer and qsiprep]
Data should be in BIDs format
Loxlan Kasa @loxlan_kasa
Edit the files in the config/ folder accordingly. Adjust config.yml to configure the workflow execution and participants.tsv to specify your subjects.
Install Snakemake using conda:
conda create -c bioconda -c conda-forge -n snakemake snakemake
For installation details, see the instructions in the Snakemake documentation.
Activate the conda environment:
conda activate snakemake Test your configuration by performing a dry-run via
snakemake --use-singularity -n
Execute the workflow locally via
snakemake --use-singularity --cores $N
using $N cores or run it in a cluster environment via
snakemake --use-singularity --cluster qsub --jobs 100
or
snakemake --use-singularity --drmaa --jobs 100
If you are using HCP, you can use your slurm profile, which submits jobs and takes care of requesting the correct resources per job (including GPUs). Once it is set-up run:
snakemake --profile slurm_profile
Or, you can request for interactive job following your system requirements:
Then, run:
snakemake --use-singularity --cores 8 --resources mem=32000
See the Snakemake documentation for further details.
After successful execution, you can create a self-contained interactive HTML report with all results via:
snakemake --report report.html
This study incooperated open tools and the UKBB, listed below: