Ependymoma tumors HiC data analysis

The project contains analysis code for the ependymoma HiC study: https://www.nature.com/articles/s41467-023-38044-0

Required environment: Linux, Python, R

Tool details (versions, etc) are provided in launch bash commands, for questions please create an issue or contact repo owner directly.

IGV online track sessions and links to other materials are provided via wiki

HiC data analysis overview

Initial processing

Generate HiC-pro analysis configs + input reads data: hiC/prepareForHicProTumors.py

Generate JuiceBox output (JB views are used for mulitple figures e.g. Figure 1f,2a): hiC/convertToJuicebox.sh

Summarize QC: hiC/collectStats.py

Call TADs via TopDom: hiC/runTopDom.sh

TADs similarity inspeciton: hiC/tadsAnalysis.R

Unsupervised clustering of contacts (e.g. Figure 1c): hiC/clustering/human_tumor_hic_clustering.ipynb

Loops analysis

Call loops via FitHiC2: hiC/loopCalling/runFitHiC.sh

Find gene-enhancer assoications via loops: hiC/loopCalling/connectGenesEnhancersViaLoops.sh

Inspect expression of genes in loops (Figure 1e): hiC/loopCalling/checkExprLevel.R

Differential loops analysis: hiC/loopCalling/diffLoopAnalysis.R

Structural variants

Run alignment for hicBreakFinder: hiC/svAnalysis/mapping_hicBreakFinder.sh

Call SV with hicBreakFinder: hiC/svAnalysis/run_hicBreakFinder.sh

Generate JuiceBox output: hiC/svAnalysis/convertHicBreakFinderToJuiceBox.py

CTCF - methylation data analysis

Merge CTCF peaks with differntially methylated regions (Figures 4b,c): hyperMethCtcf/compareDMRToCTCF.R

Filter and annotate gene-enhancer in correlation pairs with CTCF loss in loops: hyperMethCtcf/ctcfWithinLoops.py

Filter and annotate gene-superenhancer pairs with CTCF loss in loops: hyperMethCtcf/annotateCtcfLossLoopSE.py