Call Peaks by Species

Last updated: 2018-08-17

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File	Version	Author	Date	Message
Rmd	392aed9	brimittleman	2018-08-17	lift code and add to index
html	471aec3	brimittleman	2018-08-17	Build site.
Rmd	356e3d8	brimittleman	2018-08-17	full protocol for HC peaks
html	50b7e98	brimittleman	2018-08-17	Build site.
Rmd	89d6424	brimittleman	2018-08-17	start peak calling human

Human Peaks

First I will call peaks in the merged human data like I did in https://brimittleman.github.io/threeprimeseq/peak.cov.pipeline.html

Merge BW

#!/bin/bash

#SBATCH --job-name=mergeBW_H
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeBW_H.out
#SBATCH --error=mergeBW_H.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END

module load Anaconda3
source activate comp_threeprime_env

ls -d -1 /project2/gilad/briana/comparitive_threeprime/human/data/bigwig/* | tail -n +2 > /project2/gilad/briana/comparitive_threeprime/human/data/list_bw/list_of_bigwig.txt

bigWigMerge -inList /project2/gilad/briana/comparitive_threeprime/human/data/list_bw/list_of_bigwig.txt /project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.bg

Convert to coverage

Copy the bg_to_cov.py to the code directory then run it with. ERROR HERE!

#!/bin/bash

#SBATCH --job-name=run_bgtocov_H
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_bgtocov_H.out
#SBATCH --error=run_bgtocov_H.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python  

python bg_to_cov.py /project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.bg /project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.coverage.txt

sort -k1,1 -k2,2n /project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.coverage.txt > /project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.coverage.sort.txt
Call Peaks

get_APA_peaks_human.py

def main(inFile, outFile, ctarget):
    fout = open(outFile,'w')
    mincount = 10
    ov = 20
    current_peak = []
    
    currentChrom = None
    prevPos = 0
    for ln in open(inFile):
        chrom, pos, count = ln.split()
        chrom= chrom[3:]
        if chrom != ctarget: continue
        count = float(count)

        if currentChrom == None:
            currentChrom = chrom
            
        if count == 0 or currentChrom != chrom or int(pos) > prevPos + 1:
            if len(current_peak) > 0:
                print (current_peak)
                M = max([x[1] for x in current_peak])
                if M > mincount:
                    all_peaks = refine_peak(current_peak, M, M*0.1,M*0.05)
                    #refined_peaks = [(x[0][0],x[-1][0], np.mean([y[1] for y in x])) for x in all_peaks]  
                    rpeaks = [(int(x[0][0])-ov,int(x[-1][0])+ov, np.mean([y[1] for y in x])) for x in all_peaks]
                    if len(rpeaks) > 1:
                        for clu in cluster_intervals(rpeaks)[0]:
                            M = max([x[2] for x in clu])
                            merging = []
                            for x in clu:
                                if x[2] > M *0.5:
                                    #print x, M
                                    merging.append(x)
                            c, s,e,mean =  chrom, min([x[0] for x in merging])+ov, max([x[1] for x in merging])-ov, np.mean([x[2] for x in merging])
                            #print c,s,e,mean
                            fout.write("chr%s\t%d\t%d\t%d\t+\t.\n"%(c,s,e,mean))
                            fout.flush()
                    elif len(rpeaks) == 1:
                        s,e,mean = rpeaks[0]
                        fout.write("chr%s\t%d\t%d\t%f\t+\t.\n"%(chrom,s+ov,e-ov,mean))
                        print("chr%s"%chrom+"\t%d\t%d\t%f\t+\t.\n"%rpeaks[0])
                    #print refined_peaks
            current_peak = [(pos,count)]
        else:
            current_peak.append((pos,count))
        currentChrom = chrom
        prevPos = int(pos)

def refine_peak(current_peak, M, thresh, noise, minpeaksize=30):
    
    cpeak = []
    opeak = []
    allcpeaks = []
    allopeaks = []

    for pos, count in current_peak:
        if count > thresh:
            cpeak.append((pos,count))
            opeak = []
            continue
        elif count > noise: 
            opeak.append((pos,count))
        else:
            if len(opeak) > minpeaksize:
                allopeaks.append(opeak) 
            opeak = []

        if len(cpeak) > minpeaksize:
            allcpeaks.append(cpeak)
            cpeak = []
        
    if len(cpeak) > minpeaksize:
        allcpeaks.append(cpeak)
    if len(opeak) > minpeaksize:
        allopeaks.append(opeak)

    allpeaks = allcpeaks
    for opeak in allopeaks:
        M = max([x[1] for x in opeak])
        allpeaks += refine_peak(opeak, M, M*0.3, noise)

    #print [(x[0],x[-1]) for x in allcpeaks], [(x[0],x[-1]) for x in allopeaks], [(x[0],x[-1]) for x in allpeaks]
    #print '---\n'
    return(allpeaks)

if __name__ == "__main__":
    import numpy as np
    from misc_helper import *
    import sys

    chrom = sys.argv[1]
    inFile = "/project2/gilad/briana/comparitive_threeprime/human/data/mergedBW/merged_human-threeprimeseq.coverage.sort.txt" # "/project2/yangili1/threeprimeseq/gencov/TotalBamFiles.split.genomecov.bed"
    outFile = "/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks/APApeaks_human_chr%s.bed"%chrom
    main(inFile, outFile, chrom)

run_getpeakYL_human.sh

#!/bin/bash

#SBATCH --job-name=run_getpeakYL_human
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_getpeakYL_human.out
#SBATCH --error=run_getpeakYL_human.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


for i in $(seq 1 22); do 
  python get_APA_peaks_human.py  $i
done

Combine the peaks

cat /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks/*.bed > /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APApeaks_merged_allchrom.bed

Bed to saf

bed2saf_h.py

from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APApeaks_merged_allchrom.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APApeaks_merged_allchrom.bed"):
    chrom, start, end, score, strand, score2 = ln.split()
    chrom=chrom[3:]
    ID = "peak_%s_%s_%s"%(chrom,start, end)
    fout.write("%s\t%s\t%s\t%s\t+\n"%(ID+"_+", chrom.replace("chr",""), start, end))
    fout.write("%s\t%s\t%s\t%s\t-\n"%(ID+"_-", chrom.replace("chr",""), start, end))
fout.close()

Peak Feature count

#!/bin/bash

#SBATCH --job-name=peak_fc_h
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=peak_fc_h.out
#SBATCH --error=peak_fc_h.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate activate comp_threeprime_env

featureCounts -a /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APApeaks_merged_allchrom.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APAquant.fc /project2/gilad/briana/comparitive_threeprime/human/data/sort/*-sort.bam -s 1

Filter peaks

filter_peaks_human.py

from misc_helper import *
import numpy as np

fout = file("/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed",'w')

#cutoffs
c = 0.9
caveread = 2

# counters
fc, fcaveread = 0, 0
N, Npass = 0, 0

for dic in stream_table(open("/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/APAquant.fc"),'\t'):
    #/project2/gilad/briana/threeprimeseq/data/sort/YL-SP-19239-T-combined-sort.bam
    #/project2/gilad/briana/comparitive_threeprime/human/data/sort/human_combined_18498_N-sort.bam
    tot, nuc = [], []
    for k in dic:
        if "human" not in k: continue
        T = k.split("_")[-1].split("-")[0]
        if T == "T":
            tot.append(int(dic[k]))
        else:
            nuc.append(int(dic[k]))
    totP = tot.count(0)/float(len(tot))
    nucP = nuc.count(0)/float(len(nuc))
    N += 1
    if totP > c and nucP > c:
        fc += 1
        continue
    if max([np.mean(tot),np.mean(nuc)]) <= caveread:
        fcaveread += 1
        continue
    
    fout.write("\t".join(["chr"+dic['Chr'], dic["Start"], dic["End"],str(max([np.mean(tot),np.mean(nuc)])),dic["Strand"],"."])+'\n')
    Npass += 1
fout.close()
    

print("%d (%.2f%%) did not pass proportion of nonzero cutoff, %d (%.2f%%) did not pass average read cutoff. Total peaks: %d (%.3f%%) of %d peaks remaining"%(fc,float(fc)/N*100, fcaveread, float(fcaveread)/N*100, Npass, 100*Npass/float(N),N))

run_filter_peaks_human.sh

#!/bin/bash

#SBATCH --job-name=filter_peak
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=filet_peak_h.out
#SBATCH --error=filter_peak_h.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python  


python filter_peaks_human.py

Name the peaks

x = wc -l /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed 

seq 1 x > peak.num.txt

paste /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed  /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/peak.num.txt | column -s $'\t' -t > temp
awk '{print $1 "\t" $2 "\t" $3 "\t" $7  "\t"  $4 "\t"  $5 "\t" $6}' temp >   /project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_named_human.bed

This will be the bed file I use for the liftover

Chimp Peaks

Merge BW

#!/bin/bash

#SBATCH --job-name=mergeBW_C
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeBW_C.out
#SBATCH --error=mergeBW_C.err
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --mail-type=END

module load Anaconda3
source activate comp_threeprime_env

ls -d -1 /project2/gilad/briana/comparitive_threeprime/chimp/data/bigwig/* | tail -n +2 > /project2/gilad/briana/comparitive_threeprime/chimp/data/list_bw/list_of_bigwig.txt

bigWigMerge -inList /project2/gilad/briana/comparitive_threeprime/chimp/data/list_bw/list_of_bigwig.txt /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.bg

Convert to coverage

#!/bin/bash

#SBATCH --job-name=run_bgtocov_C
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_bgtocov_C.out
#SBATCH --error=run_bgtocov_C.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python  

python bg_to_cov.py /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.bg /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.coverage.txt

sort -k1,1 -k2,2n /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.coverage.txt> /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.coverage.sort.txt
Call Peaks

get_APA_peaks_chimp.py

def main(inFile, outFile, ctarget):
    fout = open(outFile,'w')
    mincount = 10
    ov = 20
    current_peak = []
    
    currentChrom = None
    prevPos = 0
    for ln in open(inFile):
        chrom, pos, count = ln.split()
        chrom= chrom[3:]
        if chrom != ctarget: continue
        count = float(count)

        if currentChrom == None:
            currentChrom = chrom
            
        if count == 0 or currentChrom != chrom or int(pos) > prevPos + 1:
            if len(current_peak) > 0:
                print (current_peak)
                M = max([x[1] for x in current_peak])
                if M > mincount:
                    all_peaks = refine_peak(current_peak, M, M*0.1,M*0.05)
                    #refined_peaks = [(x[0][0],x[-1][0], np.mean([y[1] for y in x])) for x in all_peaks]  
                    rpeaks = [(int(x[0][0])-ov,int(x[-1][0])+ov, np.mean([y[1] for y in x])) for x in all_peaks]
                    if len(rpeaks) > 1:
                        for clu in cluster_intervals(rpeaks)[0]:
                            M = max([x[2] for x in clu])
                            merging = []
                            for x in clu:
                                if x[2] > M *0.5:
                                    #print x, M
                                    merging.append(x)
                            c, s,e,mean =  chrom, min([x[0] for x in merging])+ov, max([x[1] for x in merging])-ov, np.mean([x[2] for x in merging])
                            #print c,s,e,mean
                            fout.write("chr%s\t%d\t%d\t%d\t+\t.\n"%(c,s,e,mean))
                            fout.flush()
                    elif len(rpeaks) == 1:
                        s,e,mean = rpeaks[0]
                        fout.write("chr%s\t%d\t%d\t%f\t+\t.\n"%(chrom,s+ov,e-ov,mean))
                        print("chr%s"%chrom+"\t%d\t%d\t%f\t+\t.\n"%rpeaks[0])
                    #print refined_peaks
            current_peak = [(pos,count)]
        else:
            current_peak.append((pos,count))
        currentChrom = chrom
        prevPos = int(pos)

def refine_peak(current_peak, M, thresh, noise, minpeaksize=30):
    
    cpeak = []
    opeak = []
    allcpeaks = []
    allopeaks = []

    for pos, count in current_peak:
        if count > thresh:
            cpeak.append((pos,count))
            opeak = []
            continue
        elif count > noise: 
            opeak.append((pos,count))
        else:
            if len(opeak) > minpeaksize:
                allopeaks.append(opeak) 
            opeak = []

        if len(cpeak) > minpeaksize:
            allcpeaks.append(cpeak)
            cpeak = []
        
    if len(cpeak) > minpeaksize:
        allcpeaks.append(cpeak)
    if len(opeak) > minpeaksize:
        allopeaks.append(opeak)

    allpeaks = allcpeaks
    for opeak in allopeaks:
        M = max([x[1] for x in opeak])
        allpeaks += refine_peak(opeak, M, M*0.3, noise)

    #print [(x[0],x[-1]) for x in allcpeaks], [(x[0],x[-1]) for x in allopeaks], [(x[0],x[-1]) for x in allpeaks]
    #print '---\n'
    return(allpeaks)

if __name__ == "__main__":
    import numpy as np
    from misc_helper import *
    import sys

    chrom = sys.argv[1]
    inFile = "/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedBW/merged_chimp-threeprimeseq.coverage.sort.txt" # "/project2/yangili1/threeprimeseq/gencov/TotalBamFiles.split.genomecov.bed"
    outFile = "/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks/APApeaks_chimp_chr%s.bed"%chrom
    main(inFile, outFile, chrom)

run_getpeakYL_chimp.sh

#!/bin/bash

#SBATCH --job-name=run_getpeakYL_C
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_getpeakYL_C.out
#SBATCH --error=run_getpeakYL_C.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate comp_threeprime_env


for i in $(seq 1 22); do 
  python get_APA_peaks_chimp.py  $i
done

Combine the peaks

cat /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks/*.bed > /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APApeaks_merged_allchrom.bed

Bed to saf

bed2saf_c.py

from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APApeaks_merged_allchrom.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APApeaks_merged_allchrom.bed"):
    chrom, start, end, score, strand, score2 = ln.split()
    chrom=chrom[3:]
    ID = "peak_%s_%s_%s"%(chrom,start, end)
    fout.write("%s\t%s\t%s\t%s\t+\n"%(ID+"_+", chrom.replace("chr",""), start, end))
    fout.write("%s\t%s\t%s\t%s\t-\n"%(ID+"_-", chrom.replace("chr",""), start, end))
fout.close()

Peak Feature count
peak_fc_c.sh

#!/bin/bash

#SBATCH --job-name=peak_fc_c
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=peak_fc_c.out
#SBATCH --error=peak_fc_c.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate activate comp_threeprime_env

featureCounts -a /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APApeaks_merged_allchrom.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APAquant.fc /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*-sort.bam -s 1

Filter peaks

filter_peaks_chimp.py

from misc_helper import *
import numpy as np

fout = file("/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed",'w')

#cutoffs
c = 0.9
caveread = 2

# counters
fc, fcaveread = 0, 0
N, Npass = 0, 0

for dic in stream_table(open("/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/APAquant.fc"),'\t'):
    tot, nuc = [], []
    for k in dic:
        if "chimp" not in k: continue
        T = k.split("_")[-1].split("-")[0]
        if T == "T":
            tot.append(int(dic[k]))
        else:
            nuc.append(int(dic[k]))
    totP = tot.count(0)/float(len(tot))
    nucP = nuc.count(0)/float(len(nuc))
    N += 1
    if totP > c and nucP > c:
        fc += 1
        continue
    if max([np.mean(tot),np.mean(nuc)]) <= caveread:
        fcaveread += 1
        continue
    
    fout.write("\t".join(["chr"+dic['Chr'], dic["Start"], dic["End"],str(max([np.mean(tot),np.mean(nuc)])),dic["Strand"],"."])+'\n')
    Npass += 1
fout.close()
    

print("%d (%.2f%%) did not pass proportion of nonzero cutoff, %d (%.2f%%) did not pass average read cutoff. Total peaks: %d (%.3f%%) of %d peaks remaining"%(fc,float(fc)/N*100, fcaveread, float(fcaveread)/N*100, Npass, 100*Npass/float(N),N))

run_filter_peaks_chimp.sh

#!/bin/bash

#SBATCH --job-name=filter_peak_C
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=filet_peak_C.out
#SBATCH --error=filter_peak_C.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load python  


python filter_peaks_chimp.py

Name the peaks

x = wc -l /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed 

seq 1 x > peak.num.txt

paste /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.bed  /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/peak.num.txt | column -s $'\t' -t > temp
awk '{print $1 "\t" $2 "\t" $3 "\t" $7  "\t"  $4 "\t"  $5 "\t" $6}' temp >   /project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_named_chimp.bed

The final files are:

/project2/gilad/briana/comparitive_threeprime/human/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_named_human.bed
/project2/gilad/briana/comparitive_threeprime/chimp/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_named_chimp.bed

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.1.19       compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1

Call Peaks by Species

Briana Mittleman

8/16/2018

Human Peaks

Chimp Peaks

Session information