Code for analysis of ChIP-seq data from five transcription regulators
Code for assessing whether peaks are in the proximal or distal region Example usage:
python promoter_enhancer_split.py arid1b_peaks_hg38.txt human ARID1B
Code for assessing overlap between replicates Example usage:
python compare_replicates.py -a arid1b_peaks_hg38.txt -b atac_peaks_hg38.txt -mo 0.01
Code for assessing and visualizing the overlap between ChIP peak sets Example usage:
python analysis_code_NeuroDevEpi.py -m 0.4 -t hg38_GW23 -g ARID1B,BCL11A,FOXP1,TBR1,TCF7L2 -p arid1b_peaks_hg38.txt,bcl11a_peaks_hg38.txt,foxp1_peaks_hg38.txt,tbr1_peaks_hg38.txt,tcf7l2_peaks_hg38.txt -s 3.9
Function for the assessment of overlap between multiple files of ChIP-seq data (deployed in Jupyter Lab) Example usage:
spearman_peaks(prom_liver_brain_list, 10000, 'prom_liver_brain_cor_10000.txt')
Where:
- 'prom_liver_brain_list' is a python list of files of ChIP-seq peaks sorted by log10P
- '10000' is the number of peaks to use across the peak files
- 'prom_liver_brain_cor_10000.txt' is the output file name