Created: 2024-09-04

Last edited: 2025-04-16

Authors

Colby Tubbs, Mary Lauren Benton, Evonne McArthur, John A. Capra and Douglas M. Ruderfer @ the Ruderfer Lab

Paper

Tubbs, C., Benton, M. L., McArthur, E., Capra, J. A. & Ruderfer, D. M. Identifying deleterious noncoding variation through gain and loss of CTCF binding activity. The American Journal of Human Genetics.

Publicly available data

All datasets used in this study are publicly available.

• Genetic data from gnomAD v3.1.2 was downloaded for all chromosomes as a Hail matrix table. | Link

• CTCF’s binding activity Z-scores assessed in the cCRE framework were downloaded from the ENCODE-SCREEN web portal. | Link

• CTCF’s canonical PWM (MA0139.2) was downloaded from the JASPAR 2024 web portal. | Link

• Conservation scores were downloaded from the UCSC genome browser. GERP++ scores in build hg19, Phastcons100 and PhyloP100 scores in build hg38 are found at:

  • GERP++ in build hg19 (Link)

  • Phastcons100 in build hg38. | Link

  • Phlop100 in build hg38. | Link

  • LINSIGHT scores in build hg19 from Huang et al. 2017. | Link

Repository code

This respository is structured as a Snakemake workflow. To demonstrate how the study dataset was constructed, we've included all workflow input and outputs. All code used to generate these files are contained in the workflow directory and all intermediate outputs for this sample dataset are in the results directory.

For details on the generation of all figures included in the manuscript, see workflow/notebooks/plots.ipynb. For code to generate maps scores see workflow/notebooks/maps.ipynb. We provide two summary files generated from the study; for all CBS annotated by their intersecting rDHS, CTCF motif and activity score see results/Manuscript/files/activity-scores.all-CBS.tsv.gz, for all CBS variants annotated by their activity and dPWM statistics see results/Manuscript/files/variants-scores.all-CBS.tsv.gz.

Executing the pipeline

To execute the pipeline without HPC resources, execute the command:

snakemake --rerun-triggers mtime --use-conda --conda-prefix ~/snakemake_condas/ --use-conda --conda-prefix ~/snakemake_condas/ -c12 --rerun-incomplete

To execute the pipeline with HPC resources, execute the command: snakemake --rerun-triggers mtime --use-conda --conda-prefix ~/snakemake_condas/ --jobs 25 --cluster-config "config/cluster.yaml" --cluster "sbatch --mem={resources.mem_mb} --time={resources.runtime} --cpus-per-task {threads} --output={cluster.output} --error={cluster.error} --account={cluster.account}"