Hi-C and HiChIP

###For HiC:

##install Juicer as introduced as https://github.com/aidenlab/juicer based on your cluster type. 

#Run:

juicer.sh -g hg19 -s MboI

#Call Loops

juicebox hiccups -m 512 -k KR -r 5000,10000,25000 -f .2,.2,.2 -p 4,2,1 -i 7,5,3 -t 0.05,1.25,1.25,1.5 -d 20000,20000,50000 samp.hic output_loop

###For HiChIP:

##Install HiCPro as https://github.com/nservant/HiC-Pro 

#Run:

HiC-Pro -i trim_folder -o output_folder -c config-hicpro-hg19.txt -p

#Call loops

##Install FitHiChIP at https://github.com/ay-lab/FitHiChIP

PeakInferHiChIP.sh -H output_folder -R hs

FitHiChIP_Singularity.sh -C configfile_P2P_BiasCorrection_CoverageBias

#Convert to HiC format

hicpro2juicebox.sh output_folder/data/sample.allValidPairs hicpro hg19

#Run APA

juicebox apa samp.hic common.loop.bedpe

#Plot APA

hic_tool.py -m plotapa -f l_HiCHIP.APA.plot.lst

#Run HiCRep

##Install HiCRep as https://github.com/TaoYang-dev/hicrep 

##Install .hic support R package strawr remotes::install_github("aidenlab/straw/R")

#get score

scc.out <- c()

for (chr in chrslist){

mat1 <- hic2mat("samp1.hic", chromosome1 = chr, chromosome2 = chr, resol = 100000, method = "NONE")

mat2 <- hic2mat("samp2.hic", chromosome1 = chr, chromosome2 = chr, resol = 100000, method = "NONE")

scc.out = c(scc.out,get.scc(mat1, mat2, resol = 100000, h = 5, lbr = 0, ubr = 5000000))

}

median.scc.score <- median(scc.out)