Last updated: 2019-03-25

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Knit directory: threeprimeseq/analysis/

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Unstaged changes:
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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd f3740ea Briana Mittleman 2019-03-25 add top and bottom dnase
html 3eac464 Briana Mittleman 2019-03-23 Build site.
Rmd 4939dfe Briana Mittleman 2019-03-23 look at mnase at all categories
html 1b7f088 Briana Mittleman 2019-03-21 Build site.
Rmd c02e927 Briana Mittleman 2019-03-21 add mnase merge chipseq
html a6b0fe4 Briana Mittleman 2019-03-20 Build site.
Rmd 54168fd Briana Mittleman 2019-03-20 add histone mod analysis

library(tidyverse)
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http://science.sciencemag.org/content/352/6291/aad9926.full?ijkey=fkp/DIzVNS9RY&keytype=ref&siteid=sci

This article talks about chromatin modifications for heterochromatin downstream of PAS. I will look at enrichment for repressive histone marks downstream of my called PAS.

Repressive marks H3K27me3, H3K9me3

http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwHistone/

H3k27me3 H3k36me3 H3k4me3

Deeptools plot

h3k27me3DTmypeaks.sh

#!/bin/bash

#SBATCH --job-name=h3k27me3DTmypeaks
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=h3k27me3DTmypeaks.out
#SBATCH --error=h3k27me3DTmypeaks.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k27me3StdRawRep1.bigWig -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3K27me3_myPeaksNompfilt.gz


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3K27me3_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "H3K27me3 at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3K27me3_myPeaksNompfilt.png

Put all of the marks on one plot:

I also want to just use the last base of the peak APAPAS_5percCov_fixedStrand.bed histonemarksDTmypeaks.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k27me3StdRawRep1.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k36me3StdRawRep1.bigWig  /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k4me3StdRawRep1.bigWig -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/HistMarks_myPeaksNompfilt.gz


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/HistMarks_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Histone marks at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/HistMarks_myPeaksNompfilt.png

Scales are too different to put these on the same spot:

H3k27me3DTmyPAS.sh

#!/bin/bash

#SBATCH --job-name=H3k27me3DTmypeaks.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3k27me3DTmypeaks.out
#SBATCH --error=H3k27me3DTmypeaks.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.bw  -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 5000 -a 5000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k27me3_myPeaksNompfilt.gz --outFileNameMatrix /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k27me3_myPeaksNompfilt_matrix.txt --outFileSortedRegions /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k27me3_myPeaksNompfilt_SortedRegions.txt 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k27me3_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "H3k27me3 at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k27me3_myPeaksNompfilt.png

H3k36me3DTmyPAS.sh

#!/bin/bash

#SBATCH --job-name=H3k27me3DTmypeaks.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3k36me3DTmypeaks.out
#SBATCH --error=H3k36me3DTmypeaks.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.bw   -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 5000 -a 5000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k36me3_myPeaksNompfilt.gz --outFileNameMatrix /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k36me3_myPeaksNompfilt_matrix.txt --outFileSortedRegions /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k36me3_myPeaksNompfilt_SortedRegions.txt 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k36me3_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "H3k36me3 at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k36me3_myPeaksNompfilt.png

H3k4me3DTmyPAS.sh

#!/bin/bash

#SBATCH --job-name=H3k4me3DTmypeaks.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=H3k36me3DTmypeaks.out
#SBATCH --error=H3k36me3DTmypeaks.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.bw  -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 5000 -a 5000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k4me3_myPeaksNompfilt.gz --outFileNameMatrix project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k4me3_myPeaksNompfilt_matrix.txt --outFileSortedRegions /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k4me3_myPeaksNompfilt_SortedRegions.txt 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k4me3_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "H3k4me3 at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/H3k4me3_myPeaksNompfilt.png

Download both replicates for these and merge:

mergeH3k27me3.sh


#!/bin/bash

#SBATCH --job-name=mergeH3k27me3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeH3k27me3.out
#SBATCH --error=mergeH3k27me3.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env


bigWigMerge /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k27me3StdRawRep1.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k27me3StdRawRep2.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.bedGraph


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.bedGraph /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.sort.bedGraph

bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.sort.bedGraph /project2/gilad/briana/genome_anotation_data/chrom.length.chr.txt  /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k27me3.bw


mergeH3k36me3.sh


#!/bin/bash

#SBATCH --job-name=mergeH3k36me3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeH3k36me3.out
#SBATCH --error=mergeH3k36me3.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env


bigWigMerge /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k36me3StdRawRep1.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k36me3StdRawRep2.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.bedGraph


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.bedGraph > /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.sort.bedGraph

bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.sort.bedGraph /project2/gilad/briana/genome_anotation_data/chrom.length.chr.txt  /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k36me3.bw


mergeH3k4me3.sh


#!/bin/bash

#SBATCH --job-name=mergeH3k4me3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeH3k4me3.out
#SBATCH --error=mergeH3k4me3.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env


bigWigMerge /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k4me3StdRawRep1.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeUwHistoneGm06990H3k4me3StdRawRep2.bigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.bedGraph


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.bedGraph >/project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.sort.bedGraph

bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.sort.bedGraph /project2/gilad/briana/genome_anotation_data/chrom.length.chr.txt  /project2/gilad/briana/threeprimeseq/data/ChipSeq/MergedGm06990H3k4me3.bw


MNASE:

MNASEmyPAS.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNompfilt.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "MNASE at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNompfilt.png

Try second mnase track.

MNASEmyPAS_secondfile.sh


#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/wgEncodeSydhNsomeGm12878Sig.bigWig  -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase2_myPeaksNompfilt.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase2_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "MNASE at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase2_myPeaksNompfilt.png

Convert to PAS ratehr than peak: APAPeaks_5percCov_fixedStrand_INTRON.bed

python Peak2PAS.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_INTRON.bed

Run this with intronic vs utr
MNASEmyPASIntron.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_INTRON.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksIntronNompfilt.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksIntronNompfilt.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Intronic Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksIntronNompfilt.png

Nuclear specific:

python Peak2PAS.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc.bed /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_SigUsageNuc.bed

MNASEmyPASNuclear.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_SigUsageNuc.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearNompfilt.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearNompfilt.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Nuclear specific PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearNompfilt.png

Nuclear Intronic:

APAPeaks_5percCov_fixedStrand_SigUsageNuc_Intron.bed

python Peak2PAS.py /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_SigUsageNuc_Intron.bed /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_SigUsageNuc_Intron.bed

MNASEmyPASNuclearIntronic.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_SigUsageNuc_Intron.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearIntronNompfilt.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearIntronNompfilt.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Nuclear specific PAS in Intron" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksNuclearIntronNompfilt.png

Sepatate by mean usage (strong/weak)

Total

Mean usage:

top 20% and bottom 20%. by mean usage

meanUsageTot=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", col.names = c("chr", "start", "end","gene", "strand", "name", "meanUsage")) %>% mutate(perc=ntile(meanUsage,n=100)) 


meanUsageTot_bot20=meanUsageTot %>% filter(perc <20) %>% dplyr::select(name)
meanUsageTot_top20=meanUsageTot %>% filter(perc >80)%>% dplyr::select(name)

Write out the peaks:

write.table(meanUsageTot_bot20, file="../data/PeaksUsed_noMP_5percCov/TotalPeaksBottom20Usage.txt", quote=F, row.names = F, col.names = F, sep="\t")

write.table(meanUsageTot_top20, file="../data/PeaksUsed_noMP_5percCov/TotalPeaksTop20Usage.txt", quote=F, row.names = F, col.names = F, sep="\t")

Copy to /project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/

Subset PAS file:

subsetPAStottop20perc.py

top20=open("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/TotalPeaksTop20Usage.txt", "r")


AllPas=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", "r")

Top20PAS=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_top20inTotal.bed", "w")





def subsetPAS(use, outpas, PAS):
    okPAS={}
    for ln in use:
        peak=ln.strip()
        okPAS[peak]=""
    for ln in PAS:
        peaknum=ln.split()[3].split(":")[-1]
        print
        if peaknum in okPAS.keys():
            outpas.write(ln)
    outpas.close()


subsetPAS(top20, Top20PAS, AllPas)

subsetPAStotbottom20perc.py


bottom20=open("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/TotalPeaksBottom20Usage.txt", "r")

AllPas=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", "r")


Bottom20PAS=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_bottom20inTotal.bed", "w")




def subsetPAS(use, outpas, PAS):
    okPAS={}
    for ln in use:
        peak=ln.strip()
        okPAS[peak]=""
    for ln in PAS:
        peaknum=ln.split()[3].split(":")[-1]
        print
        if peaknum in okPAS.keys():
            outpas.write(ln)
    outpas.close()


subsetPAS(bottom20, Bottom20PAS, AllPas)

Deeptools plots for these:

MNASEmyPAStop20tot.sh

#!/bin/bash

#SBATCH --job-name=MNASEmyPAStop20tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MNASEmyPAStop20tot.out
#SBATCH --error=MNASEmyPAStop20tot.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_top20inTotal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20tot.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20tot.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Top 20% Total Usage" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20tot.png

MNASEmyPASbottom20tot.sh

#!/bin/bash

#SBATCH --job-name=MNASEmyPASbottom20tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MNASEmyPASbottom20tot.out
#SBATCH --error=MNASEmyPASbottom20tot.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_bottom20inTotal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20tot.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20tot.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Bottom 20% Total Usage" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20tot.png

Nuclear

Mean usage:

top 20% and bottom 20%. by mean usage

meanUsageNuc=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", col.names = c("chr", "start", "end","gene", "strand", "name", "meanUsage")) %>% mutate(perc=ntile(meanUsage,n=100)) 


meanUsageNuc_bot20=meanUsageNuc %>% filter(perc <20) %>% dplyr::select(name)
meanUsageNuc_top20=meanUsageNuc %>% filter(perc >80)%>% dplyr::select(name)

Write out the peaks:

write.table(meanUsageNuc_bot20, file="../data/PeaksUsed_noMP_5percCov/NuclearPeaksBottom20Usage.txt", quote=F, row.names = F, col.names = F, sep="\t")

write.table(meanUsageNuc_top20, file="../data/PeaksUsed_noMP_5percCov/NuclearPeaksTop20Usage.txt", quote=F, row.names = F, col.names = F, sep="\t")

Copy to /project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/

Subset PAS file:

subsetPASnuctop20perc.py

top20=open("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/NuclearPeaksTop20Usage.txt", "r")


AllPas=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", "r")

Top20PAS=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_top20inNuclear.bed", "w")





def subsetPAS(use, outpas, PAS):
    okPAS={}
    for ln in use:
        peak=ln.strip()
        okPAS[peak]=""
    for ln in PAS:
        peaknum=ln.split()[3].split(":")[-1]
        print
        if peaknum in okPAS.keys():
            outpas.write(ln)
    outpas.close()


subsetPAS(top20, Top20PAS, AllPas)

subsetPASnucbottom20perc.py


bottom20=open("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/NuclearPeaksBottom20Usage.txt", "r")

AllPas=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", "r")


Bottom20PAS=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_bottom20inNuclear.bed", "w")




def subsetPAS(use, outpas, PAS):
    okPAS={}
    for ln in use:
        peak=ln.strip()
        okPAS[peak]=""
    for ln in PAS:
        peaknum=ln.split()[3].split(":")[-1]
        print
        if peaknum in okPAS.keys():
            outpas.write(ln)
    outpas.close()


subsetPAS(bottom20, Bottom20PAS, AllPas)

Deeptools plots for these:

MNASEmyPAStop20nuc.sh

#!/bin/bash

#SBATCH --job-name=MNASEmyPAStop20nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MNASEmyPAStop20nuc.out
#SBATCH --error=MNASEmyPAStop20nuc.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_top20inNuclear.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20nuc.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20nuc.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Top 20% Nuclear Usage" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeakstop20nuc.png

MNASEmyPASbottom20nuc.sh

#!/bin/bash

#SBATCH --job-name=MNASEmyPASbottom20nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=MNASEmyPASbottom20nuc.out
#SBATCH --error=MNASEmyPASbottom20nuc.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/ChipSeq/ENCFF000VME.bigWig  -R  /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand_bottom20inNuclear.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20nuc.gz 


plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20nuc.gz --refPointLabel "Called PAS" --plotTitle "MNASE at Bottom 20% Nuclear Usage" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/ChipSeq/mnase_myPeaksbottom20nuc.png


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] cowplot_0.9.4     data.table_1.12.0 workflowr_1.2.0  
 [4] forcats_0.4.0     stringr_1.4.0     dplyr_0.8.0.1    
 [7] purrr_0.3.1       readr_1.3.1       tidyr_0.8.3      
[10] tibble_2.0.1      ggplot2_3.1.0     tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.13    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0       haven_2.1.0     
[21] xfun_0.5         withr_2.1.2      xml2_1.2.0       httr_1.4.0      
[25] knitr_1.21       hms_0.4.2        generics_0.0.2   fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.4.0         readxl_1.3.0     rmarkdown_1.11   modelr_0.1.4    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.3  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.4-0
[45] stringi_1.3.1    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4