/
peakQCplotsSTARprocessing.Rmd
1087 lines (726 loc) · 44.8 KB
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peakQCplotsSTARprocessing.Rmd
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---
title: "Peak Plots"
author: "Briana Mittleman"
date: "2/16/2019"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(workflowr)
library(tidyverse)
library(reshape2)
library(cowplot)
```
I want to remake a lot of the peak QC plots I have been making with the new mapped and proccessed data created in the [accounting for mappping bias analysis](accountMapBias.html)
* Peaks per gene
* Number of genes with 1 peak, 2 peaks, more peaks
* Distance between gene and TES
* Peaks in each category
* Peak Size
###Peak per gene:
I will do this for total and nuclear 5% seperatly then for the peaks I used in the QTL analysis.
Nuclear peaks: 42127: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt
Total peaks: 36915: /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt
```{r}
peakNames=c("chr", 'start','end','gene','strand','name', 'mean')
totalPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)
nuclearPeaks=read.table("../data/PeaksUsed_noMP_5percCov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", stringsAsFactors = F, col.names = peakNames)
```
Peaks per gene:
```{r}
totalPeaks_genes=totalPeaks %>% group_by(gene) %>% summarise(nPeaks=n()) %>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaks_genes=nuclearPeaks %>% group_by(gene) %>% summarise(nPeaks=n())%>% group_by(nPeaks) %>% summarise(GenesWithNPeaks=n())
nPeaksBoth=totalPeaks_genes %>% full_join(nuclearPeaks_genes, by="nPeaks")
colnames(nPeaksBoth)= c("Npeaks", "Total", "Nuclear")
nPeaksBoth$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
ggsave(peakUsage5perc, file="../output/plots/PeakNumberPerGenebyFrac.png")
```
Plot this with the peaks used in the fraction
```{r}
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
ggsave(allPeaksGenes, file="../output/plots/PeakNumberPerGeneUsedinQTL.png")
```
### Number of genes with 1 peak, 2 peaks, more peaks
Make this as a boxplot
```{r}
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
```
To get the genes with 0 peaks I need to pull in the gene annotation file
```{r}
morethan2= allPeaks %>% filter(PAS > 2)
colSums(morethan2)
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$genesPerCat))
lab0=paste("Genes =", "11896", sep=" ")
lab1=paste("Genes =", "4909", sep=" ")
lab2=paste("Genes =", "2949", sep=" ")
labMore=paste("Genes =", "7361", sep=" ")
propGenesbyPAS=ggplot(genesPerCat_df, aes(x="", y=genesPerCat, fill=Category)) + geom_bar(stat="identity") + labs(x="Total Genes = 27115", y="Proportion of Genes", title="Proportion of Genes by number of PAS") + annotate("text", x="", y= .7, label=lab0) + annotate("text", x="", y= .5, label=lab1) + annotate("text", x="", y= .33, label=lab2) + annotate("text", x="", y= .2, label=labMore) + scale_fill_brewer(palette="RdYlBu")
propGenesbyPAS
ggsave(propGenesbyPAS, file="../output/plots/PropOfGenesByPASnum.png",height=8, width=5)
```
Box plot
```{r}
propGenesbyPASpi=ggplot(genesPerCat_df, aes(x="", y=genesPerCat, fill=Category)) + geom_bar(stat="identity") + labs(x="Total Genes = 27115", y="Proportion of Genes", title="Proportion of Genes by number of PAS") +coord_polar("y") + scale_fill_brewer(palette="RdYlBu")
propGenesbyPASpi
ggsave(propGenesbyPASpi, file="../output/plots/PropOfGenesByPASnum_piplot.png")
```
###Distance between TES and peak
* GetDistTXNend2Peak.py
convert /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF to bed file
peaksGeneLocAnno_5percSAF2Bed.py
```{r}
distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)
txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")
distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))
mean(txnanno$length)
```
```{r}
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$length), col="red") + annotate("text", x=1000000, y=.4, label="Average transcript length \n for genes in peaks", col='red')
distTXN2PeakPlot
ggsave(distTXN2PeakPlot, file="../output/plots/DistanceBetweenPeakandTES.png")
```
###Peaks per category
* processGenLocPeakAnno2SAF_withAnno.py
* filternamePeaks5percCov_GeneLocAnno_withAnno.py
```{r}
peakswAnno=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov_withAnno.SAF", header=T) %>% separate(GeneID, into=c("Peak", "chrom", "start", "end", "strand", "gene", "loc"),sep=":") %>% select(Peak, loc) %>% group_by(loc) %>% summarise(Num=n())
```
```{r}
locationOfPeaks=ggplot(peakswAnno, aes(x=loc, y=Num)) + geom_bar(stat="identity", fill="blue") + labs(x="Gene Location", y="Number of Peaks", title="Location distribution for all PAS with 5% Usage")
locationOfPeaks
ggsave(locationOfPeaks, file="../output/plots/PeakLocationByAnnotation.png")
```
###Peak Size
Peak length:
```{r}
peaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed",col.names=c("chr", 'start','end', 'peak', 'score', 'strand')) %>% mutate(length=end-start)
```
```{r}
ggplot(peaks,aes(x=length)) + geom_histogram(bins=300) + labs(title="Peak Size", x="number of basepairs") + geom_vline(xintercept =mean(peaks$length),col="red")
```
###Deep tools:
files to remake:
Merged bam files are in /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP
Code is mergeBam2BW.sh
mergeBam2BW.sh
```{bash,eval=F}
#!/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
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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](pipeline_55Ind.html)
I want to make a bedfile with these peaks. I need to also make sure they are in the final clean peaks
makeIntronPeakBed.py
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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:
```{bash,eval=F}
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
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{bash,eval=F}
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
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```{bash,eval=F}
#!/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
```{r,eval=F}
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
```{r,eval=F}
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:
```{bash,eval=F}
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
```{bash,eval=F}
#!/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
```