New Peaks After Misprime Approach

Last updated: 2019-01-17

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
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    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
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.

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

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

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.19      digest_0.6.17    
 [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_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1

New Peaks After Misprime Approach

Briana Mittleman

1/14/2019

Extra not using

Session information