Last updated: 2019-01-18

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    File Version Author Date Message
    Rmd 2ba9005 Briana Mittleman 2019-01-18 over filter plot idea
    html e6936f3 Briana Mittleman 2019-01-18 Build site.
    Rmd 762f5ae Briana Mittleman 2019-01-18 filter plots
    html f7f514b Briana Mittleman 2019-01-18 Build site.
    Rmd d1546dd Briana Mittleman 2019-01-18 look at peaks after 5%filt
    html 92d2e15 Briana Mittleman 2019-01-17 Build site.
    Rmd 1ec3c08 Briana Mittleman 2019-01-17 fix subset bam script to a dictionary
    html ed31eba Briana Mittleman 2019-01-14 Build site.
    Rmd c9ad11e Briana Mittleman 2019-01-14 updatte filter R code
    html e088c55 Briana Mittleman 2019-01-14 Build site.
    Rmd 6bc9243 Briana Mittleman 2019-01-14 evaluate clean reads, make new file for misprime filter


In the previous analysis I looked at a mispriming approach. Now I am going to use these filtered reads to create new BAM files, BW files, coverage files, and finally a peak list. After, I will evaluate the differences in the peak lists.

Now I need to filter the sorted bed files based on these clean reads.

I can make an R script that uses filter join:

Infile1 is the sorted bed, Infile2 is cleaned bed, Filter on read name

I can sue the number_T/N as the identifer.
##filter to reads without MP

filterSortBedbyCleanedBed.R

#!/bin/rscripts

# usage: Rscirpt --vanilla  filterSortBedbyCleanedBed.R identifier

#this script takes in the sorted bed file and the clean reads, it will clean the bed file   


library(dplyr)
library(tidyr)
library(data.table)


args = commandArgs(trailingOnly=TRUE)
identifier=args[1]


sortBedName= paste("/project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-", identifier, "-combined-sort.bed", sep="")

CleanName= paste("/project2/gilad/briana/threeprimeseq/data/nuc_10up_CleanReads/TenBaseUP.", identifier, ".CleanReads.bed", sep="")

outFile= paste("/project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/YL-SP-", identifier, "-combined-sort.clean.bed", sep="")  

bedFile=fread(sortBedName, col.names = c("Chrom", "start", "end", "name", "score", "strand"))

cleanFile=fread(CleanName, col.names = c("Chrom", "start", "end", "name", "score", "strand"))

intersection=bedFile %>% semi_join(cleanFile, by="name")

fwrite(intersection, file=outFile,quote = F, col.names = F, row.names = F, sep="\t")

I need to call this in a bash script that gets just the identifier:

run_filterSortBedbyCleanedBed.sh

#!/bin/bash

#SBATCH --job-name=run_filterSortBedbyCleanedBed
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=run_filterSortBedbyCleanedBed.out
#SBATCH --error=run_filterSortBedbyCleanedBed.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

for i in $(ls /project2/gilad/briana/threeprimeseq/data/nuc_10up_CleanReads/*);do
   describer=$(echo ${i} | sed -e 's/.*TenBaseUP.//' | sed -e "s/.CleanReads.bed//")
   Rscript --vanilla  filterSortBedbyCleanedBed.R  ${describer}
done 
   

SOrt the new bed files:

sort_filterSortBedbyCleanedBed.sh

#!/bin/bash

#SBATCH --job-name=sort_filterSortBedbyCleanedBed
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=sort_filterSortBedbyCleanedBed.out
#SBATCH --error=sort_filterSortBedbyCleanedBed.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/*);do
  describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort.clean.bed//")
  bedtools sort -faidx /project2/gilad/briana/threeprimeseq/code/chromOrder.num.txt -i  /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/YL-SP-${describer}-combined-sort.clean.bed > /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-${describer}-combined-sort.clean.sorted.bed
done

Problems with Order Try on one file to save time. sort with faidx order of bam then overlap describer=“18486-N”

check that i filtered with

NB501189:272:HGWL5BGX5:1:11109:9097:13183

samtools view -c -F 4 /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-${describer}-combined-sort.bam 11405271

samtools view -c -F 4 /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${describer}-combined-sort.noMP.bam


describer="18486-N"


bedtools sort -faidx /project2/gilad/briana/threeprimeseq/code/chromOrder.num.txt -i  /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/YL-SP-${describer}-combined-sort.clean.bed  >  /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-${describer}-combined-sort.clean.sorted.bed


bedtools intersect -wa -sorted -s -abam /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-${describer}-combined-sort.bam -b /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-${describer}-combined-sort.clean.sorted.bed > /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${describer}-combined-sort.noMP.bam

Next I can use bedtools intersect to filter the bam files from these bed files. I will write the code then wrap it.

filterOnlyOKPrimeFromBam.sh

a is the bam, b is the clean bed , stranded, sorted, -wa

#!/bin/bash

#SBATCH --job-name=filterOnlyOKPrimeFromBam
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=filterOnlyOKPrimeFromBam.out
#SBATCH --error=filterOnlyOKPrimeFromBam.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


describer=$1

bedtools intersect -wa -sorted -s -abam /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-${describer}-combined-sort.bam -b /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-${describer}-combined-sort.clean.sorted.bed > /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${desrciber}-combined-sort.noMP.bam

This is slow! I want to try to use pysam to do this. I need to make a list of the ok reads from the bed file then filter on these as I read the bam file.

Add pysam to my environement

filterBamforMP.pysam2.py

#!/usr/bin/env python


"""
Usage: python filterBamforMP.pysam2.py <describer>
"""


def main(Bin, Bamin, out):
    okRead={}
    for ln in open(Bin, "r"):
        chrom, start_new , end_new , name, score, strand = ln.split()
        okRead[name] = ""
    #pysam to read in bam allignments
    bamfile = pysam.AlignmentFile(Bamin, "rb")
    finalBam =  pysam.AlignmentFile(out, "wb", template=bamfile)
    #read name is the first col in each bam file
    n=0
    for read in bamfile.fetch():
        read_name=read.query_name
        #if statement about name  
        if read_name  in okRead.keys():
            finalBam.write(read)
        if n % 1000 == 0 : print(n)
        n+=1 
    bamfile.close()
    finalBam.close()

    
if __name__ == "__main__":
    import sys, pysam
    describer = sys.argv[1]
    inBed= "/project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-" + describer + "-combined-sort.clean.sorted.bed"
    inBam="/project2/gilad/briana/threeprimeseq/data/sort/YL-SP-" + describer + "-combined-sort.bam"
    outBam="/project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-" + describer + "-combined-sort.noMP.bam"
    main(inBed, inBam, outBam)

run_filterBamforMP.pysam2.sh

#!/bin/bash

#SBATCH --job-name=dic_test_filterBamforMP.pysam2
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=run_filterBamforMP.pysam2.out
#SBATCH --error=run_filterBamforMP.pysam2.err
#SBATCH --partition=broadwl
#SBATCH --mem=32G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


describer=$1
python filterBamforMP.pysam2.py ${describer}

wrap_filterBamforMP.pysam2.sh

#!/bin/bash

#SBATCH --job-name=wrap_filterBamforMP.pysam2
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=wrap_filterBamforMP.pysam2.out
#SBATCH --error=wrap_filterBamforMP.pysam2.err
#SBATCH --partition=broadwl
#SBATCH --mem=32G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/*);do
   describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort.clean.sorted.bed//")
   sbatch run_filterBamforMP.pysam2.sh ${describer}
done

Sort and index bam files:

SortIndexBam_noMP.sh


#!/bin/bash

#SBATCH --job-name=SortIndexBam_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=SortIndexBam_noMP.out
#SBATCH --error=SortIndexBam_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load samtools
#source activate three-prime-env 
for i in $(ls /project2/gilad/briana/threeprimeseq/data/bam_NoMP/*);do
 describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort.noMP.bam//")
  samtools sort /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${describer}-combined-sort.noMP.bam >  /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/YL-SP-${describer}-combined-sort.noMP.sort.bam  
  samtools index /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/YL-SP-${describer}-combined-sort.noMP.sort.bam 
done  

Merge bams:

I will merge all of the bam files to vreate the BW and coverage files

mergeBamFiles_noMP.sh

#!/bin/bash

#SBATCH --job-name=mergeBamFiles_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=mergeBamFiles_noMP.out
#SBATCH --error=mergeBamFiles_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


samtools merge  /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllSamples.MergedBamFiles.noMP.bam /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*.bam

SortIndexMergedBam_noMP.sh

#!/bin/bash

#SBATCH --job-name=SortIndexMergedBam_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=SortIndexMergedBam_noMP.out
#SBATCH --error=SortIndexMergedBam_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

samtools sort /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllSamples.MergedBamFiles.noMP.bam > /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllSamples.MergedBamFiles.noMP.sort.bam

samtools index /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllSamples.MergedBamFiles.noMP.sort.bam

Create bigwig and coverage files from the merged bam

mergedBam2Bedgraph.sh

#!/bin/bash

#SBATCH --job-name=mergedBam2Bedgraph
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=mergedBam2Bedgraph.out
#SBATCH --error=mergedBam2Bedgraph.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


bedtools genomecov -ibam /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllSamples.MergedBamFiles.noMP.sort.bam -bg -split > /project2/gilad/briana/threeprimeseq/data/mergeBG_noMP/AllSamples.MergedBamFiles.noMP.sort.bg  

Use my bg_to_cov.py script. This script takes the infile and output file

run_bgtocov_noMP.sh

#!/bin/bash

#SBATCH --job-name=run_bgtocov_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_bgtocov_noMP.out
#SBATCH --error=run_bgtocov_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

python bg_to_cov.py "/project2/gilad/briana/threeprimeseq/data/mergeBG_noMP/AllSamples.MergedBamFiles.noMP.sort.bg" "/project2/gilad/briana/threeprimeseq/data/mergeBG_coverage_noMP/AllSamples.MergedBamFiles.noMP.sort.coverage.txt"


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/mergeBG_coverage_noMP/AllSamples.MergedBamFiles.noMP.sort.coverage.txt > /project2/gilad/briana/threeprimeseq/data/mergeBG_coverage_noMP/AllSamples.MergedBamFiles.noMP.sort.coverage.sort.txt

Call peaks

THen I will be able to call peaks

callPeaksYL_noMP.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()
        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/threeprimeseq/data/mergeBG_coverage_noMP/AllSamples.MergedBamFiles.noMP.sort.coverage.sort.txt"
    outFile = "/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_noMP_chr%s.bed"%chrom
    main(inFile, outFile, chrom)

Run this over all chroms:

run_callPeaksYL_noMP.sh

#!/bin/bash

#SBATCH --job-name=run_callPeaksYL_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_callPeaksYL_noMP.out
#SBATCH --error=run_callPeaksYL_noMP.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 callPeaksYL_noMP.py $i
done

Filter peaks

Filter peaks:

cat /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/*.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.bed

bed2saf_noMP.py

fout = open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.bed"):
    chrom, start, end, score, strand, score2 = ln.split()
    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_fc_noMP.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/APAquant_noMP.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*sort.bam -s 1

filter_peaks_noMP.py

from misc_helper import *
import numpy as np

fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.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/threeprimeseq/data/mergedPeaks_comb_noMP/APAquant_noMP.fc"),'\t'):
    tot, nuc = [], []
    for k in dic:
        if "YL-SP" not in k: continue
        T = k.split("-")[-3].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_noMP.sh

#!/bin/bash

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

module load python  


python filter_peaks_noMP.py

Name the peaks:

122488 = wc -l /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.bed

seq 1 122488 > peak.num.txt

sort -k1,1 -k2,2n Filtered_APApeaks_merged_allchrom_noMP.bed > Filtered_APApeaks_merged_allchrom_noMP.sort.bed

paste /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.bed 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/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed

#cut the chr  

sed 's/^chr//' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed

Assign genes

assign peaks to genes:

TransClosest2End_noMP.sh

#!/bin/bash

#SBATCH --job-name=TransClosest2End_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TransClosest2End_noMP.out
#SBATCH --error=TransClosest2End_noMP.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3 

source activate three-prime-env


bedtools closest -S -D b -t "first" -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed -b  /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.bed

Prepare for QTL

/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.bed

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $13 "\t" $11}' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.bed

less /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.bed | tr ":" "-" > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.bed

bed2saf_peaks2trans.noMP.py

from misc_helper import *

fout = open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.bed"):
    chrom, start, end, name, score, strand, gene = ln.split()
    name_i=int(name)
    start_i=int(start)
    end_i=int(end)
    gene_only=gene.split("-")[1]
    ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom, start_i, end_i, strand, gene_only)
    fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
fout.close()

Map to peaks with ID

ref_gene_peakTranscript_fc_TN_noMP.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*T-combined-sort.noMP.sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*N-combined-sort.noMP.sort.bam -s 2

fix_head_fc_opp_transcript_tot_noMP.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

fix_head_fc_opp_transcript_nuc_noMP.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

create_fileid_opp_transcript_total_noMP.py

fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_total_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

create_fileid_opp_transcript_nuclear_noMP.py

fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_nuclear_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

remove the extra top lines from these files:

awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_nuclear_Transcript_head.txt >  /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_nuclear_Transcript.txt




awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_total_Transcript_head.txt > /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_total_Transcript.txt

Make Phenotype

makePhenoRefSeqPeaks_Transcript_Total_noMP.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_total_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.fc","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

makePhenoRefSeqPeaks_Transcript_Nuclear_noMP.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_nuclear_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.fc","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

Script to run these:

run_makePhen_sep_Transcript_noMP.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_Transcript_Total_noMP.py  

python makePhenoRefSeqPeaks_Transcript_Nuclear_noMP.py  

Make into usage

Pull these into R to look at them and get just the counts

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
The following object is masked from 'package:purrr':

    transpose
library(reshape2)

Attaching package: 'reshape2'
The following objects are masked from 'package:data.table':

    dcast, melt
The following object is masked from 'package:tidyr':

    smiths
totalPeakUs=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.fc.gz", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))
nuclearPeakUs=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.fc.gz", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))



write.table(totalPeakUs[,7:dim(totalPeakUs)[2]], file="../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.CountsOnly",quote=FALSE, col.names = F, row.names = F)

write.table(nuclearPeakUs[,7:dim(nuclearPeakUs)[2]], file="../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.CountsOnly",quote=FALSE, col.names = F, row.names = F)

convertCount2Numeric_noMP.py

def convert(infile, outfile):
  final=open(outfile, "w")
  for ln in open(infile, "r"):
    line_list=ln.split()
    new_list=[]
    for i in line_list:
      num, dem = i.split("/")
      if dem == "0":
        perc = "0.00"
      else:
        perc = int(num)/int(dem)
        perc=round(perc,2)
        perc= str(perc)
      new_list.append(perc)
    final.write("\t".join(new_list)+ '\n')
  final.close()
  
convert("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.CountsOnly","/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.CountsOnlyNUMERIC.txt" )


convert("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.CountsOnly","/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.CountsOnlyNUMERIC.txt")

Peaks with 5% cov

Read these in to filter and make 5% plots:

ind=colnames(totalPeakUs)[7:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.CountsOnlyNUMERIC.txt", col.names = ind)

nuclearPeakUs_CountNum=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.CountsOnlyNUMERIC.txt", col.names = ind)

Numeric values with the annotations:

totalPeak=as.data.frame(cbind(totalPeakUs[,1:6], totalPeakUs_CountNum))
nuclearPeak=as.data.frame(cbind(nuclearPeakUs[,1:6], nuclearPeakUs_CountNum))

Get the mean coverage for each peak.

totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)
nuclearPeakUs_CountNum_mean=rowMeans(nuclearPeakUs_CountNum)

Append these to the inforamtion about the peak.

TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:6],totalPeakUs_CountNum_mean))
NuclearPeakUSMean=as.data.frame(cbind(nuclearPeakUs[,1:6],nuclearPeakUs_CountNum_mean))
TotalPeakUSMean_filt=TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())
totalPeaksPerGene=TotalPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())


NuclearPeakUSMean_filt=NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())
nuclearPeaksPerGene=NuclearPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaksPerGene$GenesWithNPeaks=as.integer(nuclearPeaksPerGene$GenesWithNPeaks)

peak number level:

nPeaksBoth=totalPeaksPerGene %>% full_join(nuclearPeaksPerGene, by="Npeaks")
colnames(nPeaksBoth)= c("Peaks", "Total", "Nuclear")
nPeaksBoth$Total= nPeaksBoth$Total %>% replace_na(0)

#melt nPeaksBoth
nPeaksBoth_melt=melt(nPeaksBoth, id.var="Peaks")
colnames(nPeaksBoth_melt)= c("Peaks", "Fraction", "Genes")
peakUsage5perc=ggplot(nPeaksBoth_melt, aes(x=Peaks, y=Genes, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + labs(title="Number of Genes with >5% Peak Usage \n cleaned for mispriming") + theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold"))+ scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + facet_grid(~Fraction)

peakUsage5perc

Expand here to see past versions of unnamed-chunk-43-1.png:
Version Author Date
f7f514b Briana Mittleman 2019-01-18

ggsave(peakUsage5perc, file="../output/plots/QC_plots/peakUsage5perc_noMP.png")
Saving 7 x 5 in image

Genes covered

#nuclear
nrow(NuclearPeakUSMean_filt)  
[1] 14470
#total
nrow(TotalPeakUSMean_filt) 
[1] 14474

Difference plot:

nPeaksBoth_gene=TotalPeakUSMean_filt %>% full_join(NuclearPeakUSMean_filt, by="gene")
colnames(nPeaksBoth_gene)= c("Gene", "Total", "Nuclear")
nPeaksBoth_gene$Nuclear= nPeaksBoth_gene$Nuclear %>% replace_na(0)
nPeaksBoth_gene$Total= nPeaksBoth_gene$Total %>% replace_na(0)
nPeaksBoth_gene=nPeaksBoth_gene %>% mutate(Difference=Nuclear-Total)

ggplot(nPeaksBoth_gene, aes(x=Difference)) + geom_histogram() + labs(title="Distribution of difference in number of \n Peaks >5% between Nuclear and Total \n cleaned for mispriming", y="Genes", x="Nuclear - Total")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Expand here to see past versions of unnamed-chunk-45-1.png:
Version Author Date
f7f514b Briana Mittleman 2019-01-18

summary(nPeaksBoth_gene$Difference)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-6.0000  0.0000  0.0000  0.3421  1.0000  8.0000 

Peak in each set

#nuclear  
NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% nrow()
[1] 40967
#total
TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% nrow()
[1] 36015

Filter peaks

I need to filter these peaks in the phenotype files to call QTLs.

filter the phenotype files and make a filtered set of the named peaks (prequant)

  • /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP/*

  • /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed.bed

Make sure we are not over filtering

use a bed files with the peaks from the old list that are not in the new list. These have evidence for mispriming. we want to make sure RNAseq doesnt decrease sharply at these. Do this for not clean not filtered compared to the clean and filtered.

Extra not using

filterOnlyOKPrimeFromBam.sh

a is the bam, b is the clean bed , stranded, sorted, -wa

#!/bin/bash

#SBATCH --job-name=filterOnlyOKPrimeFromBam
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=filterOnlyOKPrimeFromBam.out
#SBATCH --error=filterOnlyOKPrimeFromBam.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


describer=$1

bedtools intersect -wa -sorted -s -abam /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-${describer}-combined-sort.bam -b /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/YL-SP-${describer}-combined-sort.clean.sorted.bed > /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${desrciber}-combined-sort.noMP.bam

This is slow! I want to try to use pysam to do this. I need to make a list of the ok reads from the bed file then filter on these as I read the bam file.

Wrap this:

wrap_filterOnlyOKPrimeFromBam.sh

#!/bin/bash

#SBATCH --job-name=w_filterOnlyOKPrimeFromBam
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=w_filterOnlyOKPrimeFromBam.out
#SBATCH --error=w_filterOnlyOKPrimeFromBam.err
#SBATCH --partition=broadwl
#SBATCH --mem=8G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP_sorted/*);do
   describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort.clean.sorted.bed//")
   sbatch filterOnlyOKPrimeFromBam.sh ${describer}
done

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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     

other attached packages:
 [1] bindrcpp_0.2.2    reshape2_1.4.3    data.table_1.11.8
 [4] cowplot_0.9.3     forcats_0.3.0     stringr_1.3.1    
 [7] dplyr_0.7.6       purrr_0.2.5       readr_1.1.1      
[10] tidyr_0.8.1       tibble_1.4.2      ggplot2_3.0.0    
[13] tidyverse_1.2.1   workflowr_1.1.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.19      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.17     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.1         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    crayon_1.3.4     
[40] whisker_0.3-2     pkgconfig_2.0.2   xml2_1.2.0       
[43] lubridate_1.7.4   assertthat_0.2.0  rmarkdown_1.10   
[46] httr_1.3.1        rstudioapi_0.8    R6_2.3.0         
[49] nlme_3.1-137      git2r_0.23.0      compiler_3.5.1   



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