Last updated: 2019-02-27

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Knit directory: threeprimeseq/analysis/

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Unstaged changes:
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    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   analysis/unexplainedeQTL_analysis.Rmd
    Modified:   code/Snakefile

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Rmd f8c76ea Briana Mittleman 2019-02-16 move peak QC plots 
library(workflowr)
This is workflowr version 1.2.0
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library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(reshape2)

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library(cowplot)

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I want to remake a lot of the peak QC plots I have been making with the new mapped and proccessed data created in the accounting for mappping bias analysis

  • Peaks per gene

  • Number of genes with 1 peak, 2 peaks, more peaks

  • Distance between gene and TES

  • Peaks in each category

  • Peak Size

Peak per gene:

I will do this for total and nuclear 5% seperatly then for the peaks I used in the QTL analysis.

Nuclear peaks: 42127: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt

Total peaks: 36915: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt

peakNames=c("chr", 'start','end','gene','strand','name', 'mean')
totalPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)
nuclearPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)

Peaks per gene:

totalPeaks_genes=totalPeaks %>% group_by(gene) %>% summarise(nPeaks=n()) %>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaks_genes=nuclearPeaks %>% group_by(gene) %>% summarise(nPeaks=n())%>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())

nPeaksBoth=totalPeaks_genes %>% full_join(nuclearPeaks_genes, by="nPeaks")

colnames(nPeaksBoth)= c("Npeaks", "Total", "Nuclear")
nPeaksBoth$Total= nPeaksBoth$Total %>% replace_na(0)

#melt nPeaksBoth
nPeaksBoth_melt=melt(nPeaksBoth, id.var="Npeaks")
colnames(nPeaksBoth_melt)= c("PAS", "Fraction", "Genes")

peakUsage5perc=ggplot(nPeaksBoth_melt, aes(x=PAS, y=Genes, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + labs(title="Number of Genes by PAS Number \n 5% Usage",x="Number of PAS in Gene") + 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

Version Author Date
879be33 Briana Mittleman 2019-02-16 
ggsave(peakUsage5perc, file="../output/plots/PeakNumberPerGenebyFrac.png")
Saving 7 x 5 in image

Plot this with the peaks used in the fraction

allPeaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", stringsAsFactors = F, col.names = c("chr", 'start','end', 'id', 'score', 'strand')) %>% separate(id, into=c("gene", "peak"), sep=":")%>% group_by(gene) %>% summarise(nPeaks=n()) %>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())

colnames(allPeaks)=c("PAS","Genes" )

allPeaksGenes=ggplot(allPeaks, aes(x=PAS, y=Genes)) + geom_bar(stat="identity",fill="blue") + labs(title="Number of Genes by PAS Count: \n PAS Used in QTL analysis",x="Number of PAS in Gene") + 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")) 

allPeaksGenes

Version Author Date
879be33 Briana Mittleman 2019-02-16 
ggsave(allPeaksGenes, file="../output/plots/PeakNumberPerGeneUsedinQTL.png")
Saving 7 x 5 in image

Number of genes with 1 peak, 2 peaks, more peaks

Make this as a boxplot

GeneAnno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", stringsAsFactors = F, header=T) %>% select(name2) %>%  unique()
colnames(GeneAnno)="gene"
genesWithpeak= read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", stringsAsFactors = F, col.names = c("chr", 'start','end', 'id', 'score', 'strand')) %>% separate(id, into=c("gene", "peak"), sep=":") %>% select(gene) %>% unique()

Geneswith0= GeneAnno %>% anti_join(genesWithpeak, by="gene") %>% nrow()
Geneswith0
[1] 11896

To get the genes with 0 peaks I need to pull in the gene annotation file

morethan2= allPeaks %>% filter(PAS > 2) 
colSums(morethan2)
  PAS Genes 
  102  7361 
Category=c("0 PAS", "1 PAS", "2 PAS", "More than 2 PAS")
genesPerCat=c(11896/27115, 4909/27115, 2949/27115, 7361/27115)

genesPerCat_df=as.data.frame(cbind(Category,genesPerCat))

genesPerCat_df$genesPerCat=as.numeric(as.character(genesPerCat_df$genesPerCat))

lab0=paste("Genes =", "11896", sep=" ")
lab1=paste("Genes =", "4909", sep=" ")
lab2=paste("Genes =", "2949", sep=" ")
labMore=paste("Genes =", "7361", sep=" ")


propGenesbyPAS=ggplot(genesPerCat_df, aes(x="", y=genesPerCat, fill=Category)) + geom_bar(stat="identity") + labs(x="Total Genes = 27115", y="Proportion of Genes", title="Proportion of Genes by number of PAS") + annotate("text", x="", y= .7, label=lab0) + annotate("text", x="", y= .5, label=lab1) + annotate("text", x="", y= .33, label=lab2) + annotate("text", x="", y= .2, label=labMore) +  scale_fill_brewer(palette="RdYlBu")

propGenesbyPAS

Version Author Date
382f5dc Briana Mittleman 2019-02-21
879be33 Briana Mittleman 2019-02-16 
ggsave(propGenesbyPAS, file="../output/plots/PropOfGenesByPASnum.png",height=8, width=5)

Box plot

propGenesbyPASpi=ggplot(genesPerCat_df, aes(x="", y=genesPerCat, fill=Category)) + geom_bar(stat="identity") + labs(x="Total Genes = 27115", y="Proportion of Genes", title="Proportion of Genes by number of PAS") +coord_polar("y")  +  scale_fill_brewer(palette="RdYlBu")

propGenesbyPASpi

Version Author Date
382f5dc Briana Mittleman 2019-02-21 
ggsave(propGenesbyPASpi, file="../output/plots/PropOfGenesByPASnum_piplot.png")
Saving 7 x 5 in image

Distance between TES and peak

  • GetDistTXNend2Peak.py

convert /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF to bed file

peaksGeneLocAnno_5percSAF2Bed.py

distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)
txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")
distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))
mean(txnanno$length)
[1] 60808.79
distTXN2PeakPlot=ggplot(distTXN2Peak, aes(x=AbsDist + 1)) + geom_density() + scale_x_log10() + labs(x="Absolute Distance between end of Transcription and center of Peak", title="Distribution of transcription to peak absolute distance") +  geom_vline(xintercept=mean(txnanno$length), col="red") + annotate("text", x=1000000, y=.4, label="Average transcript length \n for genes in peaks", col='red')

distTXN2PeakPlot

Version Author Date
382f5dc Briana Mittleman 2019-02-21 
ggsave(distTXN2PeakPlot, file="../output/plots/DistanceBetweenPeakandTES.png")
Saving 7 x 5 in image

Peaks per category

  • processGenLocPeakAnno2SAF_withAnno.py
  • filternamePeaks5percCov_GeneLocAnno_withAnno.py
peakswAnno=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov_withAnno.SAF", header=T) %>% separate(GeneID, into=c("Peak", "chrom", "start", "end", "strand", "gene", "loc"),sep=":") %>% select(Peak, loc) %>% group_by(loc) %>% summarise(Num=n())
locationOfPeaks=ggplot(peakswAnno, aes(x=loc, y=Num)) + geom_bar(stat="identity", fill="blue") + labs(x="Gene Location", y="Number of Peaks", title="Location distribution for all PAS with 5% Usage")
locationOfPeaks

Version Author Date
382f5dc Briana Mittleman 2019-02-21 
ggsave(locationOfPeaks, file="../output/plots/PeakLocationByAnnotation.png")
Saving 7 x 5 in image

Peak Size

Peak length:

peaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed",col.names=c("chr", 'start','end', 'peak', 'score', 'strand')) %>% mutate(length=end-start)
ggplot(peaks,aes(x=length)) + geom_histogram(bins=300) + labs(title="Peak Size", x="number of basepairs") + geom_vline(xintercept =mean(peaks$length),col="red")

Version Author Date
382f5dc Briana Mittleman 2019-02-21

Deep tools:

files to remake:

Merged bam files are in /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP

Code is mergeBam2BW.sh

mergeBam2BW.sh

#!/bin/bash

#SBATCH --job-name=mergeBam2B
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeBam2BW.out
#SBATCH --error=mergeBam2BW.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#total  
bamCoverage -b /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllTotalSamples.MergedBamFiles.noMP.sort.bam -o /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw  

#nuclear  
bamCoverage -b /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllNuclearSamples.MergedBamFiles.noMP.sort.bam -o /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw

I need to remake the peak files with the strand opposite (peaks are opposite strand from the reads!)

fixStrand4DTplots.py

peaksIn="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed"
intronIn="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5percCov_INTRON.bed"
PeakOut="/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed"
intronOut="/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed"


def fix_strand(Fin,Fout):
    fout=open(Fout,"w")
    for ln in open(Fin, "r"):
        chrom, start, end, name, score, strand = ln.split()
        if strand=="+":
            fout.write("%s\t%s\t%s\t%s\t%s\t-\n"%(chrom,start,end,name,score))
        else:
            fout.write("%s\t%s\t%s\t%s\t%s\t+\n"%(chrom,start,end,name,score))
    fout.close()
    
    
fix_strand(peaksIn, PeakOut)
fix_strand(intronIn, intronOut)

/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed

/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5percCov_INTRON.bed

BothFracDTPlotmyPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPlotmyPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=BothFracDTPlotmyPeaks_noMPFilt.out
#SBATCH --error=BothFracDTPlotmyPeaks_noMPFilt.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/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined Reads at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksNompfilt.png

RNADTPlotmyPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=RNADTPlotmyPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNADTPlotmyPeaks_noMPFilt.out
#SBATCH --error=RNADTPlotmyPeaks_noMPFilt.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksNompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksNompfilt.gz --refPointLabel "Called PAS" --plotTitle "Combined Reads at All Called PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksNompfilt.png

I want to make one of these that look at total, nuclear, and RNA at peaks assigned to an intron. This means I need to subset the peak file to only include these. I can do this similar to how I did the UTR subset in this analysis

I want to make a bedfile with these peaks. I need to also make sure they are in the final clean peaks

makeIntronPeakBed.py

inFile="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed"
outFile=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5percCov_INTRON.bed" , "w")
okPeaks=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", "r")


okPeak_dic={}
for ln in okPeaks:
    peak=ln.split()[3].split(":")[1]
    peak_num=peak[4:]
    okPeak_dic[peak_num]=""



for ln in open(inFile, "r"):
    chrom, start, end, peak, cov, strand, score, anno = ln.split()
    if anno==".": 
        continue  
    anno_lst=anno.split(",")
    if len(anno_lst)==1:
        gene=anno_lst[0].split(":")[1]
        if anno_lst[0].split(":")[0]=="intron":
            if peak in okPeak_dic.keys():
                peak_i=int(peak)
                start_i=int(start)
                end_i=int(end)
                type="intron"
                outFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_i, end_i, type,score, strand))
    else:
        type_dic={}
        for each in anno_lst:
            type_dic[each.split(":")[0]]=each.split(":")[1]
        if "utr3" in type_dic.keys():
            continue
        if "intron" in type_dic.keys():
             if peak in okPeak_dic.keys():
                 peak_i=int(peak)
                 start_i=int(start)
                 end_i=int(end)
                 type="intron"
                 outFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_i, end_i,type ,score, strand))
outFile.close()

BothFracDTPlotmyIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPlotmyIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=BothFracRNADTPlotmyIntronPeaks_noMPFilt.out
#SBATCH --error=BothFracRNADTPlotmyIntronPeaks_noMPFilt.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/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksIntron_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksIntron_Nompfilt.gz --refPointLabel "Called Intronic PAS" --plotTitle "Combined Reads at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_myPeaksIntronNompfilt.png

RNA seq only:

RNADTPlotmyIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=RNADTPlotmyIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNADTPlotmyIntronPeaks_noMPFilt.out
#SBATCH --error=RNADTPlotmyIntronPeaks_noMPFilt.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntron_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntron_Nompfilt.gz --refPointLabel "Called Intronic PAS" --plotTitle "Combined Reads at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntronNompfilt.png

I should try this with the nuclear RNA samples

I need to merge them and make a BW.

mergeNucRNAseq.sh

#!/bin/bash

#SBATCH --job-name=mergeNucRNAseq
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeNucRNAseq.out
#SBATCH --error=mergeNucRNAseq.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/NuclearRNA/NuclearRNA_merged.bam  /project2/gilad/briana/Total_Nuc_RNA_seq_data/170428_K00242_0214_AHK2GMBBXX-YG-SP20/data/sort/YG-SP20-Nuc-2_S5_L005_R1_001-sort.bam /project2/gilad/briana/Total_Nuc_RNA_seq_data/170428_K00242_0214_AHK2GMBBXX-YG-SP20/data/sort/YG-SP20-Nuc-1_S2_L005_R1_001-sort.bam

samtools sort /project2/gilad/briana/threeprimeseq/data/NuclearRNA/NuclearRNA_merged.bam > /project2/gilad/briana/threeprimeseq/data/NuclearRNA/NuclearRNA_merged.sort.bam

samtools index /project2/gilad/briana/threeprimeseq/data/NuclearRNA/NuclearRNA_merged.sort.bam

NucBam2BW.sh

#!/bin/bash

#SBATCH --job-name=NucBam2BW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucBam2BW.out
#SBATCH --error=NucBam2BW.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#total  
bamCoverage -b /project2/gilad/briana/threeprimeseq/data/NuclearRNA/NuclearRNA_merged.sort.bam -o /project2/gilad/briana/threeprimeseq/data/NuclearRNA/NuclearRNA_merged.sort.bw

NucRNADTPlotmyIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyIntronPeaks_noMPFilt.out
#SBATCH --error=NucRNADTPlotmyIntronPeaks_noMPFilt.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/NuclearRNA/NuclearRNA_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed  -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt.gz --refPointLabel "Called Intronic PAS" --plotTitle "Combined Reads at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntronNompfilt.png

NucRNADTPlotmyIntronPeaks_noMPFilt_150.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyIntronPeaks_noMPFilt_150
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyIntronPeaks_noMPFilt150.out
#SBATCH --error=NucRNADTPlotmyIntronPeaks_noMPFilt150.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/NuclearRNA/NuclearRNA_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed  -b 150 -a 150  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt150.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt150.gz --refPointLabel "Called Intronic PAS" --plotTitle "Combined Reads at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntronNompfilt150.png

NucRNADTPlotmyIntronPeaks_noMPFilt_300.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyIntronPeaks_noMPFilt_300
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyIntronPeaks_noMPFilt300.out
#SBATCH --error=NucRNADTPlotmyIntronPeaks_noMPFilt300.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/NuclearRNA/NuclearRNA_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed  -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt300.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Nompfilt300.gz --refPointLabel "Called Intronic PAS" --plotTitle "Combined Reads at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntronNompfilt300.png

TES and TSS (not looking right- coding end doesnt work)

fixStrand4DTplots_TESTSS.py Fix strand for these:

TSSIn="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_TSSAllGenes.bed"
TESIn="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_TESAllGenes.bed"
TSSOut="/project2/gilad/briana/threeprimeseq/data/peaks4DT/ncbiRefSeq_TSSAllGenes_fixedStrand.bed"
TESOut="/project2/gilad/briana/threeprimeseq/data/peaks4DT/ncbiRefSeq_TESAllGenes_fixedStrand.bed"


def fix_strand(Fin,Fout):
    fout=open(Fout,"w")
    for ln in open(Fin, "r"):
        chrom, start, end, name, score, strand = ln.split()
        if strand=="+":
            fout.write("%s\t%s\t%s\t%s\t%s\t-\n"%(chrom,start,end,name,score))
        else:
            fout.write("%s\t%s\t%s\t%s\t%s\t+\n"%(chrom,start,end,name,score))
    fout.close()
    
    
fix_strand(TSSIn, TSSOut)
fix_strand(TESIn, TESOut)

files to make: new TSS file from the annotation in the new gene loc annocation pipeline

getTss.py



TXN2Gene_file=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms","r")

outFile=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_TSSAllGenes.bed", "w")

for i, ln in enumerate(TXN2Gene_file):
    if i >0 :
        chrom=ln.split()[2]
        chromf=chrom[3:]
        start=int(ln.split()[6])-1 
        end=int(ln.split()[6])
        txn=ln.split()[1]
        genename=ln.split()[12]
        id=txn + ":" + genename
        strand=ln.split()[3]
        score="."
        outFile.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chromf, start, end, id, score, strand))

outFile.close()

RNADTPlotTSS_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=RNADTPlotTSS_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNADTPlotTSS_noMPFilt.out
#SBATCH --error=RNADTPlotTSS_noMPFilt.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/genome_anotation_data/ncbiRefSeq.mRNA.transcriptTSS.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TSS_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TSS_Nompfilt.gz --refPointLabel "Called TSS" --plotTitle "Combined Reads at TSS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TSS_Nompfilt.png

BothFracDTPlotTSS_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPlotTSS_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=BothFracDTPlotTSS_noMPFilt.out
#SBATCH --error=BothFracDTPlotTSS_noMPFilt.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/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw  -R /project2/gilad/briana/genome_anotation_data/ncbiRefSeq.mRNA.transcriptTSS.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_TSS_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_TSS_Nompfilt.gz --refPointLabel "Called TSS" --plotTitle "Combined Reads at TSS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_TSS_Nompfilt.png

getTES.py



TXN2Gene_file=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms","r")

outFile=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_TESAllGenes.bed", "w")

for i, ln in enumerate(TXN2Gene_file):
    if i >0 :
        chrom=ln.split()[2]
        chromf=chrom[3:]
        start=int(ln.split()[7])-1 
        end=int(ln.split()[7])
        txn=ln.split()[1]
        genename=ln.split()[12]
        id=txn + ":" + genename
        strand=ln.split()[3]
        score="."
        outFile.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chromf, start, end, id, score, strand))

outFile.close()

BothFracRNADTPlotTES_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracRNADTPlotTES_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=BothFracRNADTPlotTES_noMPFilt.out
#SBATCH --error=BothFracRNADTPlotTESnoMPFilt.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/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/ncbiRefSeq_TESAllGenes_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFracRNA_TES_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFracRNA_TES_Nompfilt.gz --refPointLabel "Called TES" --plotTitle "Combined Reads at TES" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFracRNA_TES_Nompfilt.png

RNADTPlotTES_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=RNADTPlotTES_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNADTPlotTES_noMPFilt.out
#SBATCH --error=RNADTPlotTESnoMPFilt.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/ncbiRefSeq_TESAllGenes_fixedStrand.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TES_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TES_Nompfilt.gz --refPointLabel "Called TES" --plotTitle "Combined Reads at TES" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_TES_Nompfilt.png

/project2/gilad/briana/genome_anotation_data/ncbiRefSeq.mRNA.transcriptTSS.bed

ATAC seq

ATAC seq bam files are in /project2/yangili1/LCL/ATAC/ I will merge the bam files.

mergeBamFiles_ATAC.sh

#!/bin/bash

#SBATCH --job-name=mergeBamFiles_ATAC
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=mergeBamFiles_ATAC.out
#SBATCH --error=mergeBamFiles_ATAC.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/ATAC_mergeBam/ATAC_merged.bam /project2/yangili1/LCL/ATAC/*.sort.bam 

samtools sort /project2/gilad/briana/threeprimeseq/data/ATAC_mergeBam/ATAC_merged.bam > /project2/gilad/briana/threeprimeseq/data/ATAC_mergeBam/ATAC_merged.sort.bam

samtools index /project2/gilad/briana/threeprimeseq/data/ATAC_mergeBam/ATAC_merged.sort.bam

bam2BW_ATAC.sh

#!/bin/bash

#SBATCH --job-name=bam2BW_ATAC
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=bam2BW_ATAC.out
#SBATCH --error=bam2BW_ATAC.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#total  
bamCoverage -b /project2/gilad/briana/threeprimeseq/data/ATAC_mergeBam/ATAC_merged.sort.bam -o /project2/gilad/briana/threeprimeseq/data/ATAC_mergeBW/ATAC_merged.sort.bw

ATACDTPlotmyIntronPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=ATACDTPlotmyIntronPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ATACDTPlotmyIntronPeaks_noMPFilt.out
#SBATCH --error=ATACDTPlotmyIntronPeaks_noMPFilt.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/ATAC_mergeBW/ATAC_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed  -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksIntron_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksIntron_Nompfilt.gz --refPointLabel "Called Intronic PAS" --plotTitle "ATAC-seq at Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksIntronNompfilt.png

3’ UTR peaks

Do it with the 3’ UTR peaks

Find the scripts for the UTR only peaks

processGenLocPeakAnno2SAF_3UTRonly.py

I will subset Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_3UTR.SAF for those in the 5percent coverage.

The peak needs to be in /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF

filter_UTRpeak_5perc.py

fiveperc=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF","r")
utr=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_3UTR.SAF","r")
outBed=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_3UTR.bed","w")


#make ok dic  

okPeak={}
for i, ln in enumerate(fiveperc):
    if i >0:
        peak=ln.split()[0]
        okPeak[peak]=""

#filter and write bed  

for i,ln in enumerate(utr):
    if i > 0:
        peak = ln.split()[0]
        if peak in okPeak.keys():
            num=peak.split(":")[0]
            gene=peak.split(":")[-1]
            chrom=ln.split()[1]
            start=ln.split()[2]
            end=ln.split()[3]
            strand=ln.split()[4]
            id=num + ":" + gene
            score="."
            outBed.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end,id,score,strand ))
            
outBed.close()

This is 20 thousand peaks.

Fix strand:

fix3UTRPeakStrand.py

In="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_3UTR.bed"
Out="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_FixedStrand_3UTR.bed"

def fix_strand(Fin,Fout):
    fout=open(Fout,"w")
    for ln in open(Fin, "r"):
        chrom, start, end, name, score, strand = ln.split()
        if strand=="+":
            fout.write("%s\t%s\t%s\t%s\t%s\t-\n"%(chrom,start,end,name,score))
        else:
            fout.write("%s\t%s\t%s\t%s\t%s\t+\n"%(chrom,start,end,name,score))
    fout.close()
    
    
fix_strand(In, Out)

Make the deep tools plots:

  • Nuclear RNA
  • ATAC

ATACDTPlotUTRPeaks_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=ATACDTPlotUTRPeaks_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ATACDTPlotUTRPeaks_noMPFilt.out
#SBATCH --error=ATACDTPlotUTRPeaks_noMPFilt.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/ATAC_mergeBW/ATAC_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_FixedStrand_3UTR.bed -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksUTR_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksUTR_Nompfilt.gz --refPointLabel "Called 3' UTR PAS" --plotTitle "ATAC-seq at 3' UTR PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/ATAC_myPeaksUTR_Nompfilt.png

NucRNADTPlotmyUTR_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyUTR_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyUTR_noMPFilt.out
#SBATCH --error=NucRNADTPlotmyUTR_noMPFilt.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/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc_3UTR/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed_5perc_FixedStrand_3UTR.bed -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksUTR_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksUTR_Nompfilt.gz --refPointLabel "Called 3' UTR PAS" --plotTitle "Combined Reads at 3' UTR PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksUTRNompfilt.png

Filter internal to higher usage:

I need to find the usage for each of the peaks and take only the top 200 intronic by average usage. I can do this in R.

I need the file with the average usage.

I can do this based on the nuclear peaks

filterTop200UsageNuclearIntronPeaks.R

library(tidyverse)

#get nuclear mean usage  
nuclearPeakUs=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))
ind=colnames(nuclearPeakUs)[7:dim(nuclearPeakUs)[2]]
nuclearPeakUs_CountNum=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnlyNumeric.txt", col.names = ind)

nuclearPeak=as.data.frame(cbind(nuclearPeakUs[,1:6], nuclearPeakUs_CountNum))
nuclearPeakUs_CountNum_mean=rowMeans(nuclearPeakUs_CountNum)
NuclearPeakUSMean=as.data.frame(cbind(nuclearPeakUs[,1:6],nuclearPeakUs_CountNum_mean))

#load in intron peaks  
intron=read.table("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed", stringsAsFactors = F, col.names = c("ch", "start", "end", "loc", "score", "strand")) %>% mutate(chr=paste("chr", ch, sep="")) 
NuclearPeakUSMean$start=as.integer(NuclearPeakUSMean$start)
NuclearPeakUSMean$end=as.integer(NuclearPeakUSMean$end)
#join                  
                  
intron_usage= intron %>% inner_join(NuclearPeakUSMean, by=c("chr","start", "end"))  %>% select(ch, start, end, loc, score, strand.x, nuclearPeakUs_CountNum_mean) %>% arrange(desc(nuclearPeakUs_CountNum_mean)) %>% select(-nuclearPeakUs_CountNum_mean)  %>%slice(1:200)

write.table(intron_usage, file="/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.bed", quote=F, row.names = F, col.names = F)

DO this for total as well

filterTop200UsageNuclearIntronPeaks_total.R

library(tidyverse)

#get nuclear mean usage  
totalPeakUs=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))
ind=colnames(totalPeakUs)[7:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnlyNumeric.txt", col.names = ind)

totalPeak=as.data.frame(cbind(totalPeakUs[,1:6], totalPeakUs_CountNum))
totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)
TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:6],totalPeakUs_CountNum_mean))

#load in intron peaks  
intron=read.table("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON.bed", stringsAsFactors = F, col.names = c("ch", "start", "end", "loc", "score", "strand")) %>% mutate(chr=paste("chr", ch, sep="")) 
TotalPeakUSMean$start=as.integer(TotalPeakUSMean$start)
TotalPeakUSMean$end=as.integer(TotalPeakUSMean$end)
#join                  
                  
intron_usage= intron %>% inner_join(TotalPeakUSMean, by=c("chr","start", "end"))  %>% select(ch, start, end, loc, score, strand.x, totalPeakUs_CountNum_mean) %>% arrange(desc(totalPeakUs_CountNum_mean)) %>%  select(-totalPeakUs_CountNum_mean)%>%  slice(1:200)

write.table(intron_usage, file="/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage_Total.bed", quote=F, row.names = F, col.names = F, sep="\t")

Sort these files to use in DT:


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage_Total.bed > /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage_Total.sort.bed

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.bed > /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.Nuclear.sort.bed

NucRNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.out
#SBATCH --error=NucRNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.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/NuclearRNA/NuclearRNA_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.Nuclear.sort.bed -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Nuc_Nompfilt150.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Nuc_Nompfilt150.gz --refPointLabel "Called Intronic PAS" --plotTitle "Nuclear RNAseq at Nuclear Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Nuc_Nompfilt150.png

NucRNADTPlotmyIntronPeaks_Top200Tot_noMPFilt_150.sh

#!/bin/bash

#SBATCH --job-name=NucRNADTPlotmyIntronPeaks_noMPFilt_150
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucRNADTPlotmyIntronPeaks_noMPFilt150.out
#SBATCH --error=NucRNADTPlotmyIntronPeaks_noMPFilt150.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/NuclearRNA/NuclearRNA_merged.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.Nuclear.sort.bed -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Tot_Nompfilt150.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Tot_Nompfilt150.gz --refPointLabel "Called Intronic PAS" --plotTitle "Nuclear RNAseq at Total Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/NucRNA_myPeaksIntron_Top200Tot_Nompfilt150.png

Look at these in the top nuclear in RNA seq:
RNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.sh

#!/bin/bash

#SBATCH --job-name=RNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.out
#SBATCH --error=RNADTPlotmyIntronPeaks_Top200Nuc_noMPFilt_150.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw -R /project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand_INTRON_top200Usage.Nuclear.sort.bed -b 300 -a 300  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntron_Top200Nuc_Nompfilt150.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntron_Top200Nuc_Nompfilt150.gz --refPointLabel "Called Intronic PAS" --plotTitle "RNAseq at Nuclear Intronic PAS" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/RNA_myPeaksIntron_Top200Nuc_Nompfilt150.png

Repetitive elements:

Could this be do to repetitive elements

Process: /project2/gilad/briana/genome_anotation_data/RepeatMask.dms

I just need to cut the chr to make the chroms the same as mine

sed 's/^chr//'  /project2/gilad/briana/genome_anotation_data/RepeatMask.dms   > /project2/gilad/briana/genome_anotation_data/RepeatMask.bed

BothFracDTPlotRepeats_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPlotRepeats_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=BothFracDTPlotRepeats_noMPFilt.out
#SBATCH --error=BothFracDTPlotRepeats_noMPFilt.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/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/genome_anotation_data/RepeatMask.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.gz --refPointLabel "Repetitive Regions" --plotTitle "Combined Reads at Repetitive Regions" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.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] bindrcpp_0.2.2  cowplot_0.9.3   reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] workflowr_1.2.0

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        pillar_1.3.0       glue_1.3.0        
[10] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[13] readxl_1.1.0       bindr_0.1.1        plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[19] rvest_0.3.2        evaluate_0.13      labeling_0.3      
[22] knitr_1.20         broom_0.5.0        Rcpp_0.12.19      
[25] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[28] fs_1.2.6           hms_0.4.2          digest_0.6.17     
[31] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[34] cli_1.0.1          tools_3.5.1        magrittr_1.5      
[37] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.11     httr_1.3.1        
[46] rstudioapi_0.9.0   R6_2.3.0           nlme_3.1-137      
[49] git2r_0.24.0       compiler_3.5.1