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Copy pathPARCLIP_MD14_structure_intronsLINESINE_V2.Rmd
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PARCLIP_MD14_structure_intronsLINESINE_V2.Rmd
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---
title: "Untitled"
output: html_document
---
# Analysis of Intron + Exon CLIP peaks
## Set Analysis mode
CLIPtype:
Introns - Select peaks that are classified as "Protein Coding : Exons"
Exons - Select peaks that are classified as "Protein Coding : Introns"
Introns_LINESINETLR - Select peaks that are classified as Protein Coding : Introns and Intron peaks that are classified as "Repeat Elements" but overelap with LINE SINE or TLR
RunMode:
Genomic - Include Intronic and Exonic Sequences when finding 100nt region Upsteram and Downstream of CLIP peak
Transcriptomic - Only include Exonic Sequences when finding 100nt region Upsteram and Downstream of CLIP peak. If Upstream/Downstream region crosses Intronic region, the intronic sequence is skpped and the next exon in included.
TranscLoc: select subset of Protein Coding CLIP
```{r ,echo=F, include=FALSE}
list=ls()
remove(list=list[list%in%c('Peaksdata4')==F])
##### Set Analysis mode
CLIPtype='Introns_LINESINETLR' #Introns or #Exons #Introns_LINESINETLR
RunMode='Genomic' #Genomic or Transcriptomic
TrasncLoc='all' # all, 3UTR, 5UTR, exons_CDS, introns
scratch=paste0('./structure/chunkwindow/',CLIPtype,'/',TrasncLoc,'/scr')
scratch_UP=paste0('./structure/chunkwindow/',CLIPtype,'/',TrasncLoc,'/upstream/scr')
scratch_DN=paste0('./structure/chunkwindow/',CLIPtype,'/',TrasncLoc,'/downstream/scr')
outdir_UP=paste0('./structure/chunkwindow/',CLIPtype,'/',TrasncLoc,'/upstream')
outdir_DN=paste0('./structure/chunkwindow/',CLIPtype,'/',TrasncLoc,'/downstream')
qgrs='/Users/homanpj/Documents/Tools/qgrs-cpp/qgrs'
fastaRegexFinder= '/Users/homanpj/Documents/Tools/fastaRegexFinder/fastaRegexFinder.py'
RNAfold= '/Users/homanpj/Documents/Tools/ViennaRNA-2.4.14/bin/RNAfold'
inRandom='./structure/Random/Random_Biowulf/V2'
# inRandom='./structure/Random/Random_Biowulf/Exon_includeintron'
LINESINEout=paste0('./structure/LINESINE/',CLIPtype,'/',TrasncLoc)
window=100
nrepeat=1
meanBPP=.2
maxBPP=.9
```
```{r include=F ,echo=F,warning=F,message=FALSE}
# Peaksdata4=write.table(Peaksdata4, file=paste0('./structure/chunkwindow/Peaksdata4'), quote=F, sep="\t", row.names=F, col.names=T)
Peaksdata4=read.delim('./structure/chunkwindow/Peaksdata4',header = T,sep = "\t")
colnames(Peaksdata4)=c("ID", "strand", "Average MAPQ", "Counts_Unique", "Counts_Multimappers_All", "Counts_Multimappers_Scaled", "Counts_Multimappers_BestMapping",
"Multimaped Reads %",
"Control:Counts_Unique",
"Control:Counts_Multimappers_All", "Control:Counts_Multimappers_Scaled",
"Control:Counts_Multimappers_BestMapping",
"Same Strand: Host_gene_name",
"Same Strand: Host_gene_ensembl_id",
"Same Strand: RNA_type",
"Same Strand: Repeat_Type",
"Same Strand: Intron Exon",
"Same Strand: RNA_type_Classification",
"Same.Strand: ncRNA_Classification",
"Opposite Strand: Host_gene_name",
"Opposite Strand: Host_gene_ensembl_id",
"Opposite Strand: RNA_type",
"Opposite Strand: Repeat_Type",
"Opposite Strand: Intron Exon",
"Opposite Strand: RNA_type_Classification",
"Opposite Strand: ncRNA_Classification",
"Both Strand: RNA_type_Classification",
"MM_number", "MM_Alt_Peaktype", "MM_Same_Peaktype", "MM_biotype", "MM_Prec_Alt_Peaktype", "MM_Alt_Alignments", "iCount_peak" )
fasta='../../../../Resources/ref/mm10/Gencode_VM23/fromGencode/GRCm38.primary_assembly.genome.fa'
intronbed="/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromUCSC/KnownGene/KnownGene_GRCm38_exons.bed"
library("ComplexHeatmap") ## version should be >=2.5
# packageVersion("ComplexHeatmap")
library(VariantAnnotation,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(GenomicRanges,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(trackViewer)
library("pheatmap")
library(vcfR)
library(seqinr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(ggplot2,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library("viridis",quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library('GenomicFeatures',quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library('rtracklayer',quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
#library(GeneStructureTools)
library(matrixStats,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(plyr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(tidyr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(fitdistrplus,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(stringr,quietly = T,verbose = F,warn.conflicts = F,logical.return = F)
library(data.table)
library(reshape)
library(knitr)
library(stringi)
library(biomaRt)
library(plotly)
library(tidyr)
library(GenomicRanges)
library(RColorBrewer)
library('gplots')
library(ggpubr)
library(circlize)
library('regioneR')
library(GeneStructureTools)
source("PARCLIP_Annotation_MD10.R")
blank_theme <- theme_minimal()+
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.border = element_blank(),
panel.grid=element_blank(),
axis.ticks = element_blank(),
plot.title=element_text(size=14, face="bold")
)
incd_rRNA=T
```
```{r , include=FALSE}
#################################################
# Get Peaks data
#################################################
########################################
# All Coding Transcripts
########################################
# Peaksdata4_chunkEx=Peaksdata4[Peaksdata4$`Both Strand: RNA_type_Classification`%in%'protein_coding: Intron'|
# Peaksdata4$`Both Strand: RNA_type_Classification`%in%'protein_coding: exon'|
# Peaksdata4$`Both Strand: RNA_type_Classification`%in%'Repeat Element'&Peaksdata4$`Same Strand: Repeat_Type`%in%c('LINE', 'SINE','LTR'),]
### Select peaks that are classified as Intron or Exon
Peaksdata4_chunkEx1=Peaksdata4[(Peaksdata4$`Both Strand: RNA_type_Classification`)%in%c('protein_coding: Intron','protein_coding: exon') ,]
### Select peaks that are classified as Repeat Element and OL with LINE SINE LTR only
Peaksdata4_chunkEx2a=Peaksdata4[Peaksdata4$`Both Strand: RNA_type_Classification`%in%c('Repeat Element')&
grepl(c('LINE|SINE|LTR'),Peaksdata4$`Same Strand: Repeat_Type`),]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[-grep(c('repeat'),Peaksdata4_chunkEx2a$`Same Strand: Repeat_Type`),]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[-grep(c('complexity'),Peaksdata4_chunkEx2a$`Same Strand: Repeat_Type`),]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[-grep(c('Satellite'),Peaksdata4_chunkEx2a$`Same Strand: Repeat_Type`),]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[-grep(c('DNA'),Peaksdata4_chunkEx2a$`Same Strand: Repeat_Type`),]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[grepl(c('Unknown'),Peaksdata4_chunkEx2a$`Opposite Strand: Repeat_Type`)==F,]
Peaksdata4_chunkEx2a=Peaksdata4_chunkEx2a[grepl(c('Other'),Peaksdata4_chunkEx2a$`Opposite Strand: Repeat_Type`)==F,]
### Combine Regions
Peaksdata4_chunkEx=rbind(Peaksdata4_chunkEx1,Peaksdata4_chunkEx2a)
Peaksdata4_chunkEx=Peaksdata4_chunkEx[duplicated(Peaksdata4_chunkEx$ID)==F,]
### PEAK MUST OVERLAP WITH INTRON OR EXON
Peaksdata4_chunkEx=Peaksdata4_chunkEx[is.na(Peaksdata4_chunkEx$`Same Strand: Intron Exon`)==F,]
########################################
### Select peaks that are classified as Antisense Feature
Peaksdata4_chunkEx2b=Peaksdata4[Peaksdata4$`Both Strand: RNA_type_Classification`%in%c('Antisense Feature')&
grepl(c('LINE|SINE|LTR'),Peaksdata4$`Opposite Strand: Repeat_Type`),]
### Select peaks that are classified as Antisense Feature and remove repeeat type except LINE SINE LTR
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[-grep(c('repeat'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`),]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[-grep(c('complexity'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`),]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[-grep(c('Satellite'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`),]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[grepl(c('DNA'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`)==F,]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[grepl(c('Unknown'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`)==F,]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[grepl(c('Other'),Peaksdata4_chunkEx2b$`Opposite Strand: Repeat_Type`)==F,]
Peaksdata4_chunkEx2b=Peaksdata4_chunkEx2b[Peaksdata4_chunkEx2b$ID%in%Peaksdata4_chunkEx$ID==F,]
Peaksdata4_chunkEx=rbind(Peaksdata4_chunkEx,Peaksdata4_chunkEx2b)
Peaksdata4_chunkEx=Peaksdata4_chunkEx[duplicated(Peaksdata4_chunkEx$ID)==F,]
### PEAK MUST OVERLAP WITH INTRON OR EXON on either strand
# Peaksdata4_chunkEx=Peaksdata4_chunkEx[(is.na(Peaksdata4_chunkEx$`Same Strand: Host_gene_ensembl_id`)==F)|(is.na(Peaksdata4_chunkEx$`Opposite Strand: Host_gene_ensembl_id`)==F),]
Peaksdata4_chunkEx=Peaksdata4_chunkEx[(is.na(Peaksdata4_chunkEx$`Same Strand: Intron Exon`)==F)|(is.na(Peaksdata4_chunkEx$`Opposite Strand: Intron Exon`)==F),]
Peaksdata4_chunkEx=separate(Peaksdata4_chunkEx,col = ID,into = c('chr','start'),sep=':',remove = F)
Peaksdata4_chunkEx=separate(Peaksdata4_chunkEx,start,into = c('start','end'),sep='-')
Peaksdata4_chunkEx$start=as.numeric(Peaksdata4_chunkEx$start)
Peaksdata4_chunkEx$end=as.numeric(Peaksdata4_chunkEx$end)
########################################################################################################################################################################
chunkname3_exon=as.data.frame(matrix(nrow=1,ncol=nrow(Peaksdata4_chunkEx)+2))
chunkout3_exon=as.data.frame(matrix(nrow=window,ncol=nrow(Peaksdata4_chunkEx)+2))
chunkout3_exon[,1]=seq(1,window,1)
chunkout3_max_exon=as.data.frame(matrix(nrow=window,ncol=nrow(Peaksdata4_chunkEx)+2))
chunkout3_max_exon[,1]=seq(1,window,1)
seqout3_exon=as.data.frame(matrix(nrow=window,ncol=nrow(Peaksdata4_chunkEx)+2))
seqout3_exon[,1]=seq(1,window,1)
seqout3_max_exon=as.data.frame(matrix(nrow=window,ncol=nrow(Peaksdata4_chunkEx)+2))
seqout3_max_exon[,1]=seq(1,window,1)
#################################################
# Function to process RNAfold output
#################################################
RNAfold_output=function(fls){
chunkRout3=as.data.frame(matrix(nrow=window,ncol=4))
chunkRname3=as.data.frame(matrix(nrow=1,ncol=3))
chunkRout3[,1]=seq(1,window,1)
chunkRout3_max=as.data.frame(matrix(nrow=window,ncol=4))
# for (x in 1:length(fls)) {
# if (x%in%seq(from=0,to=1000,by=25)){print(x/length(fls))}
# fls=as.list(fls)
# fls=fls[117]
fls=unlist(fls)
chunkR=read.delim(paste0(fls[1]),comment.char = "%",header = F,sep = "\t")
chunkR=chunkR[grep("ubox",chunkR$V1),]
chunkR=as.data.frame(chunkR[-1],stringsAsFactors=F)
chunkR$`chunkR[-1]`=as.character(chunkR$`chunkR[-1]`);colnames(chunkR)="V1"
chunkR=separate(chunkR,'V1',sep = " ",into = c('b1','b2','pvalue','ubox'))
chunkR$pvalue=as.numeric(chunkR$pvalue)
chunkR$b1=as.numeric(chunkR$b1)
chunkR$b2=as.numeric(chunkR$b2)
### count each base
for (y in 1:(window-9)) {
ch=chunkR[chunkR$b1%in%seq(y,y+9,1)|chunkR$b2%in%seq(y,y+9,1),]
ch2=ch
#
if (nrow(ch2)>0) {
ch2$br1=rowMins(as.matrix(ch[,c('b1','b2')]))
ch2$br2=rowMaxs(as.matrix(ch[,c('b1','b2')]))
ch$b1=ch2$br1
ch$b2=ch2$br2
ch=ch[duplicated(ch)==F,]
chunkRout3[y,1]=y
chunkRout3[y,2]=paste0(y,'-',y+9)
chunkRout3[y,3]=mean(ch$pvalue) ##average all basepairinng accross chunck
#
chunkRout3_max[y,1]=y
chunkRout3_max[y,2]=paste0(y,'-',y+9)
chunkRout3[y,4]=max(ch$pvalue) ##max all basepairinng accross chunck
}
if (nrow(ch2)==0) {
chunkRout3[y,1]=y
chunkRout3[y,2]=paste0(y,'-',y+9)
chunkRout3[y,3]=0
chunkRout3_max[y,1]=y
chunkRout3_max[y,2]=paste0(y,'-',y+9)
chunkRout3[y,4]=0
}
chunkRname3[1,3]=gsub("_dp.ps","",fls[1])
}
chunkRname3=t(chunkRname3)
colnames(chunkRout3)[3:ncol(chunkRout3)]=chunkRname3[3:nrow(chunkRname3),1]
chunkRout3=chunkRout3[is.na(chunkRout3[,3])==F,]
chunkRout3[is.nan(as.matrix(chunkRout3))==T]=0
return(chunkRout3)
}
#################################################
# Get Genome files
#################################################
canonical=fread("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromUCSC/KnownCanonical/KnownCanonical_GencodeM23_GRCm38.txt", header=T, sep="\t",stringsAsFactors = F,data.table=F)
canonical$transcript=removeVersion(canonical$transcript)
canonical$protein=removeVersion(canonical$protein)
mm10all=fread("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromGencode/gencode.vM23.basic.annotation.gtf.txt", header=T, sep="\t",stringsAsFactors = F,data.table=F)
mm10all$transcript_id=removeVersion(mm10all$transcript_id)
mm10all$gene_id=removeVersion(mm10all$gene_id)
mm10_trans=mm10all[mm10all$feature%in%c("transcript"),]
mm10_exon=mm10all[mm10all$feature%in%c("exon"),]
mm10_exon$start=as.numeric(as.character(mm10_exon$start))
mm10_exon$end=as.numeric(as.character(mm10_exon$end))
mm10_exon=mm10_exon[mm10_exon$transcript_id%in%mm10_trans$transcript_id,]
introns=fread("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromUCSC/KnownGene/KnownGene_GRCm38_introns.bed", header=F, sep="\t",stringsAsFactors = F,data.table=F)
introns=introns[grepl("_",introns$V1)==F,]
colnames(introns)=c('seqname','start','end','attribute','V5','strand')
introns=separate(introns,attribute,into=c('transcript_id','feature','exon_number','level','chr2','intronnumber','dir'),remove = T,sep = "_")
introns$transcript_id=removeVersion(introns$transcript_id)
# introns_canon=introns[introns$transcript_id%in%canonical$transcript_id,]
introns$start=introns$start+1
introns$exon_number=as.numeric(introns$exon_number)+1
introns=introns[introns$transcript_id%in%mm10_exon$transcript_id,]
introns=merge(introns,mm10_trans[,c('transcript_id','gene_id')],by='transcript_id',all.x=T)
###################################
iso=read.delim('../mESC_102218/4-Analysis/Wolin.mECS_102218/inst/extdata/RSEM.isoforms.FPKM.all_samples.txt', header=T, sep="\t",stringsAsFactors = F)
iso$WT=rowMeans(iso[,c('WTQ3_FPKM','WTQ4_FPKM')])
iso$gene_id=removeVersion(iso$gene_id)
iso$transcript_id=removeVersion(iso$transcript_id)
iso=merge(iso,mm10_trans[,c('transcript_id','start','end')],by='transcript_id',all.x=T)
colnames(iso)[colnames(iso)%in%c('start','end')]=c('start_iso','end_iso')
gene=read.delim('../mESC_102218/4-Analysis/Wolin.mECS_102218/inst/extdata/RSEM.genes.FPKM.all_samples.txt', header=T, sep="\t",stringsAsFactors = F)
gene$WT=rowMeans(gene[,c('WTQ3_FPKM','WTQ4_FPKM')])
gene$gene_id=removeVersion(gene$gene_id)
if (CLIPtype=='Introns'){mm10_USE=introns}
if (CLIPtype=='Introns_LINESINETLR'){mm10_USE=introns}
if (CLIPtype=='Exons'){mm10_USE=mm10_exon}
#################################################
# Select apropriate isoforms for each
#################################################
isoout=as.data.frame(matrix(nrow = nrow(Peaksdata4_chunkEx),ncol=16));colnames(isoout)=c('ID','strand','gene_id','CL','maxiso','maxiso_count','maxiso_start','maxiso_end','maxiso_OL','maxiso2','maxiso2_count','maxiso2_start','maxiso2_end','maxiso2_OL','diff',"IScanonical")
for (e in 1:nrow(Peaksdata4_chunkEx)) {
p=Peaksdata4_chunkEx[e,]
if (p$strand=='+') {isoout[e,"CL"]=p$start }
if (p$strand=='-') {isoout[e,"CL"]=p$end }
g=p$`Same Strand: Host_gene_ensembl_id`
if (is.na(g)) {g=p$`Opposite Strand: Host_gene_ensembl_id` }
if (length(strsplit(g,",")[[1]])>1) { ##split if multiple genes
g=(strsplit(g,",")[[1]])
gexp=gene[gene$gene_id%in%g,]
gexp=gexp[order(gexp$WT,decreasing = T),]
if (nrow(gexp)>1) {
# if ((gexp[1,'WT']/gexp[2,'WT'])<1.5) {xxxxGene}
g=gexp[1,'gene_id']
}}
isog=iso[iso$gene_id%in%g,]
isog=isog[order(isog$WT,decreasing = T),]
isogMax=isog[1,'WT']
isogMax2=isog[2,'WT']
if (is.na(isogMax2)==F&isogMax!=0){
if (((isogMax-isogMax2)/isogMax)<.2) {
isoout[e,'maxiso']=isog[1,'transcript_id']
isoout[e,'maxiso_count']=isog[1,'WT']
isoout[e,'maxiso_start']=isog[1,'start_iso']
isoout[e,'maxiso_end']=isog[1,'end_iso']
isoout[e,'maxiso2']=isog[2,'transcript_id']
isoout[e,'maxiso2_count']=isog[2,'WT']
isoout[e,'maxiso2_start']=isog[2,'start_iso']
isoout[e,'maxiso2_end']=isog[2,'end_iso']
isoout[e,'diff']=((isogMax-isogMax2)/isogMax)
}}
isoout[e,'ID']=p$ID
isoout[e,'strand']=p$strand
isoout[e,'gene_id']=isog[1,'gene_id']
isoout[e,'maxiso']=isog[1,'transcript_id']
isoout[e,'maxiso_count']=isog[1,'WT']
isoout[e,'maxiso_start']=isog[1,'start_iso']
isoout[e,'maxiso_end']=isog[1,'end_iso']
isoout[e,'IScanonical']=0
i=isoout[e,c("maxiso","maxiso2"),drop=T]
i=i[i%in%canonical$transcript]
if (length(i)>0) {isoout[e,'IScanonical']=i}
}
isoout[,'maxiso_OL']=(isoout[,'CL']>isoout[,'maxiso_start'])&(isoout[,'CL']<isoout[,'maxiso_end'])
isoout[,'maxiso2_OL']=(isoout[,'CL']>isoout[,'maxiso2_start'])&(isoout[,'CL']<isoout[,'maxiso2_end'])
isoout=merge(isoout,canonical[,c('transcript','protein','chromStart','chromEnd')],by.x='gene_id',by.y='protein',all.x=T)
isoout$CanOL=(isoout[,'CL']>isoout[,'chromStart'])&(isoout[,'CL']<isoout[,'chromEnd'])
isoout[isoout$maxiso_OL==T|isoout$maxiso2_OL==T|isoout$CanOL==T,]
isoout$USEtranscript=0
## if no close sexond use max
criteria=which(is.na(isoout$maxiso2)&(isoout$maxiso_OL==T))
isoout[criteria,'USEtranscript']=isoout[criteria,'maxiso']
## if close sexond and one of top two is Canonicol use Cannonical
criteria=which((is.na(isoout$maxiso2)==F)&(isoout$maxiso2_OL==T))
isoout[criteria,'USEtranscript']=isoout[criteria,'IScanonical']
## if close sexond and one of top two is NOT Canonicol use Cannonical
criteria=which((isoout$USEtranscript)%in%'0'&(isoout$CanOL==T))
isoout[criteria,c('USEtranscript')]=isoout[criteria,c('transcript')]
## if still no CanOL chec to see if max still OL
criteria=which((isoout$USEtranscript=="0")&(isoout$maxiso_OL==T))
isoout[criteria,'USEtranscript']=isoout[criteria,'maxiso']
criteria=which((isoout$USEtranscript=="0")&(isoout$maxiso2_OL==T))
isoout[criteria,'USEtranscript']=isoout[criteria,'maxiso2']
mm10_transGR=GRanges(seqnames=mm10_trans$seqname,
ranges=IRanges(start=mm10_trans$start,end=mm10_trans$end),
strand=mm10_trans$strand,
width2=(mm10_trans$end-mm10_trans$start),
transcript_id=mm10_trans$transcript_id)
isoutcorrect=isoout[isoout$USEtranscript=="0",]
isoutcorrect=separate(isoutcorrect,ID,into=c('CLIPchr','CLIPstart'),sep = ":",remove=F)
isoutcorrect=separate(isoutcorrect,CLIPstart,into=c('CLIPstart','CLIPend'),sep = "-",remove=T)
isoutcorrect$CLIPstart=as.numeric(isoutcorrect$CLIPstart)
isoutcorrect$CLIPend=as.numeric(isoutcorrect$CLIPend)
for (x in 1:nrow(isoutcorrect)) {
a=isoutcorrect[x,]
a_gR=GRanges(seqnames=a$CLIPchr,
ranges=IRanges(start=as.numeric(a$CLIPstart),end=as.numeric(a$CLIPend)),
strand=a$strand )
gr = a_gR
gr2 = mm10_transGR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
TransOut_GR=as.data.frame(gr2[xo$subjectHits])
aGR=as.data.frame(gr[xo$queryHits])
TransOut_GR=TransOut_GR[order(TransOut_GR$width,decreasing = T),]
isoutcorrect[x,'USEtranscript']=TransOut_GR[1,'transcript_id']
}
isoout=rbind(isoout[(isoout$ID%in%isoutcorrect$ID)==F,],isoutcorrect[,colnames(isoutcorrect)%in%colnames(isoout)])
```
```{r , include=FALSE}
## Overlap with Transcript Component
Nlocations=nrow(Peaksdata4_chunkEx)
UseCores=8
isoout=separate(isoout,ID,into = c('chr','start'),sep = ":",remove = F)
isoout=separate(isoout,start,into = c('start','end'),sep = "-",remove = T)
CrossLink_GR=GRanges(seqnames=isoout$chr, ranges=IRanges(start=isoout$CL,
end=isoout$CL),
strand=isoout$strand,
ID=isoout$ID,
USEtranscript=isoout$USEtranscript)
################################################
#### create Genomic files
################################################
mm10clip_trans=mm10all[mm10all$feature%in%'transcript',]
mm10clip_UTR=mm10all[mm10all$feature%in%'UTR',]
mm10clip_exon=mm10all[mm10all$feature%in%c("exon"),]
###############
mm10clip_3UTRall=fread("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromUCSC/KnownGene/KnownGene_GRCm38_GencodeV23_3UTR.bed", header=F, sep="\t",stringsAsFactors = F,data.table=F)
mm10clip_3UTRall=mm10clip_3UTRall[grepl("_",mm10clip_3UTRall$V1)==F,]
colnames(mm10clip_3UTRall)=c('chr','start','end','attribute','V5','strand')
mm10clip_3UTRall=separate(mm10clip_3UTRall,attribute,into=c('transcript_id','feature','exon_number','level','chr2','start+1','dir'),remove = T,sep = "_")
mm10clip_3UTRall$transcript_id=removeVersion(mm10clip_3UTRall$transcript_id)
mm10clip_3UTRall$start=mm10clip_3UTRall$start+1
mm10clip_3UTRall$exon_number=as.numeric(mm10clip_3UTRall$exon_number)+1
mm10clip_3UTRall$width=(mm10clip_3UTRall$end-mm10clip_3UTRall$start)+1
mm10clip_3UTR=mm10clip_3UTRall[mm10clip_3UTRall$transcript_id%in%mm10clip_trans$transcript_id,]
############################################################################################
mm10clip_intron=fread("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/Gencode_VM23/fromUCSC/KnownGene/KnownGene_GRCm38_introns.bed", header=F, sep="\t",stringsAsFactors = F,data.table=F)
mm10clip_intron=mm10clip_intron[grepl("_",mm10clip_intron$V1)==F,]
colnames(mm10clip_intron)=c('chr','start','end','attribute','V5','strand')
mm10clip_intron=separate(mm10clip_intron,attribute,into=c('transcript_id','feature','exon_number','level','chr2','intronnumber','dir'),remove = T,sep = "_")
mm10clip_intron$transcript_id=removeVersion(mm10clip_intron$transcript_id)
mm10clip_intron$start=mm10clip_intron$start+1
mm10clip_intron$exon_number=as.numeric(mm10clip_intron$exon_number)+1
mm10clip_intron=merge(mm10clip_intron,mm10_trans[,c('transcript_id','gene_id')],by='transcript_id',all.x=T)
################################################
mm10clip_trans=mm10clip_trans[mm10clip_trans$transcript_id%in%isoout$USEtranscript,]
mm10clip_UTR=mm10clip_UTR[mm10clip_UTR$transcript_id%in%isoout$USEtranscript,]
mm10clip_3UTR=mm10clip_3UTRall[mm10clip_3UTRall$transcript_id%in%isoout$USEtranscript,]
mm10clip_exon=mm10clip_exon[mm10clip_exon$transcript_id%in%isoout$USEtranscript,]
mm10clip_intron=mm10clip_intron[mm10clip_intron$transcript_id%in%isoout$USEtranscript,]
################################################
intronsclip_GR=GRanges(seqnames=mm10clip_intron$chr,
ranges=IRanges(start=mm10clip_intron$start,end=mm10clip_intron$end),
strand=mm10clip_intron$strand,
transcript_id=mm10clip_intron$transcript_id,
exon_number=mm10clip_intron$exon_number
)
############################################################################################
exonsclip_GR=GRanges(seqnames=mm10clip_exon$seqname,
ranges=IRanges(start=mm10clip_exon$start,end=mm10clip_exon$end),
strand=mm10clip_exon$strand,
transcript_id=mm10clip_exon$transcript_id,
gene_id=mm10clip_exon$gene_id,
exon_number=mm10clip_exon$exon_number
)
############################
TranscUTRclip_GR=GRanges(seqnames=mm10clip_UTR$seqname,
ranges=IRanges(start=mm10clip_UTR$start,end=mm10clip_UTR$end),
strand=mm10clip_UTR$strand,
transcript_id=mm10clip_UTR$transcript_id,
gene_id=mm10clip_UTR$gene_id,
exon_number=mm10clip_UTR$exon_number
)
############################
Transc3UTRclip_GR=GRanges(seqnames=mm10clip_3UTR$chr,
ranges=IRanges(start=mm10clip_3UTR$start,end=mm10clip_3UTR$end),
strand=mm10clip_3UTR$strand,
transcript_id=mm10clip_3UTR$transcript_id,
# gene_id=mm10clip_3UTR$gene_id,
exon_number=mm10clip_3UTR$exon_number
)
############################
Transcclip_GR=GRanges(seqnames=mm10clip_trans$seqname,
ranges=IRanges(start=mm10clip_trans$start,end=mm10clip_trans$end),
strand=mm10clip_trans$strand,
transcript_id=mm10clip_trans$transcript_id,
exon_number=mm10clip_trans$exon_number,
gene_id=mm10clip_trans$gene_id
)
################################################
#### create Genomic overlaps
################################################
### Get Transc that overlap with CLIP peaks
gr = CrossLink_GR
gr2 = Transcclip_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
Transc_CrossLink_GR=gr2[xo$subjectHits]
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Crosslink_Transc_GR=gr[xo$queryHits]
Crosslink_Transc_GR_DF=as.data.frame(Crosslink_Transc_GR)
Crosslink_Transc_GR_DF$Transc='Transc'
isoout$Transc=NA
isoout[which(isoout$ID%in%Crosslink_Transc_GR_DF$ID),'Transc']="Transc"
### Get Exon that overlap with CLIP peaks
gr = CrossLink_GR
gr2 = exonsclip_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
exons_CrossLink_GR=gr2[xo$subjectHits]
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Crosslink_exonsclip_GR=gr[xo$queryHits]
exons_CrossLink_GR_DF=as.data.frame(exons_CrossLink_GR)
colnames(exons_CrossLink_GR_DF)=paste(colnames(exons_CrossLink_GR_DF),'_exons')
Crosslink_exonsclip_GR_DF=cbind(as.data.frame(Crosslink_exonsclip_GR),exons_CrossLink_GR_DF)
Crosslink_exonsclip_GR_DF$exons='exons'
isoout$exons=NA
isoout[which(isoout$ID%in%Crosslink_exonsclip_GR_DF$ID),'exons']="exons"
### Get UTR that overlap with CLIP peaks
gr = CrossLink_GR
gr2 = TranscUTRclip_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
UTR_CrossLink_GR=gr2[xo$subjectHits]
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Crosslink_UTR_GR=gr[xo$queryHits]
UTR_CrossLink_GR_DF=as.data.frame(UTR_CrossLink_GR)
colnames(UTR_CrossLink_GR_DF)=paste0(colnames(UTR_CrossLink_GR_DF),'_UTR')
Crosslink_UTR_GR_DF=cbind(as.data.frame(Crosslink_UTR_GR),UTR_CrossLink_GR_DF)
Crosslink_UTR_GR_DF$UTR='UTR'
Crosslink_UTR_GR_DF$`5UTR`=NA
Crosslink_UTR_GR_DF[Crosslink_UTR_GR_DF$exon_number_UTR==1,'5UTR']="5UTR"
Crosslink_UTR_GR_DF_5UTR=Crosslink_UTR_GR_DF[is.na(Crosslink_UTR_GR_DF$`5UTR`)==F,]
### Get 3UTR that overlap with CLIP peaks
gr = CrossLink_GR
gr2 = Transc3UTRclip_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
UTR3CrossLink_GR=gr2[xo$subjectHits]
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Crosslink_3UTR_GR=gr[xo$queryHits]
UTR3CrossLink_GR_DF=as.data.frame(UTR3CrossLink_GR)
colnames(UTR3CrossLink_GR_DF)=paste0(colnames(UTR3CrossLink_GR_DF),'_3UTR')
Crosslink_UTR_GR_DF_3UTR=cbind(as.data.frame(Crosslink_3UTR_GR),UTR3CrossLink_GR_DF)
Crosslink_UTR_GR_DF_3UTR$`3UTR`='3UTR'
### Get introns that overlap with CLIP peaks
gr = CrossLink_GR
gr2 = intronsclip_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=F))
introns_CrossLink_GR=gr2[xo$subjectHits]
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Crosslink_intronsclip_GR=gr[xo$queryHits]
introns_CrossLink_GR_DF=as.data.frame(introns_CrossLink_GR)
colnames(introns_CrossLink_GR_DF)=paste0(colnames(introns_CrossLink_GR_DF),'_introns')
Crosslink_intronsclip_GR_DF=cbind(as.data.frame(Crosslink_intronsclip_GR),introns_CrossLink_GR_DF)
if (nrow(Crosslink_intronsclip_GR_DF)>0) {
Crosslink_intronsclip_GR_DF$introns='introns'
}
################################################
#### create Genomic files
################################################
############################################################################################
isoout$UTR=NA
isoout[which(isoout$ID%in%Crosslink_UTR_GR_DF$ID),'UTR']="UTR"
isoout$`3UTR`=NA
isoout[which(isoout$ID%in%Crosslink_UTR_GR_DF_3UTR$ID),'3UTR']="3UTR"
isoout$`5UTR`=NA
isoout[which(isoout$ID%in%Crosslink_UTR_GR_DF_5UTR$ID),'5UTR']="5UTR"
isoout$`introns`=NA
isoout[which(isoout$ID%in%Crosslink_intronsclip_GR_DF$ID),'introns']="introns"
isoout[is.na(isoout$exons)==F,'TranscRegion']='exons'
isoout[is.na(isoout$`introns`)==F,'TranscRegion']='introns'
isoout[is.na(isoout$`5UTR`)==F,'TranscRegion']='5UTR'
isoout[is.na(isoout$`3UTR`)==F,'TranscRegion']='3UTR'
isoout$TranscRegion=gsub("exons","exons_CDS",isoout$TranscRegion)
```
```{r , include=FALSE}
outchunck_rowaverage=matrix(nrow=nrepeat,ncol = (window-9) )
outchunck_rowaverage_max=matrix(nrow=nrepeat,ncol = (window-9) )
# outchunck_rowaverage=matrix(nrow=nrepeat,ncol = (window) )
outchunck_frac=matrix(nrow=nrepeat,ncol = 2 )
outchunck_frac_max=matrix(nrow=nrepeat,ncol = 2 )
outchunck_rowaverage_dn=matrix(nrow=nrepeat,ncol = (window-9) )
outchunck_rowaverage_maxdn=matrix(nrow=nrepeat,ncol = (window-9) )
# outchunck_rowaverage_dn=matrix(nrow=nrepeat,ncol = (window) )
outchunck_frac_dn=matrix(nrow=nrepeat,ncol = 2 )
outchunck_frac_maxdn=matrix(nrow=nrepeat,ncol = 2 )
n_UP=matrix(nrow=Nlocations,ncol=nrepeat)
ID_UP=matrix(nrow=Nlocations,ncol=nrepeat)
seq_UP=matrix(nrow=Nlocations,ncol=nrepeat)
SingleExon_UP=matrix(nrow=Nlocations,ncol=nrepeat)
Exon_UP=matrix(nrow=Nlocations,ncol=nrepeat)
transcript_UP=matrix(nrow=Nlocations,ncol=nrepeat)
LastExon_UP=matrix(nrow=Nlocations,ncol=nrepeat)
Gscore_UP=matrix(nrow=Nlocations,ncol=nrepeat)
GscoreDist_UP=matrix(nrow=Nlocations,ncol=nrepeat)
FinderSeq_UP=matrix(nrow=Nlocations,ncol=nrepeat)
qgrsSeq_UP=matrix(nrow=Nlocations,ncol=nrepeat)
maxBPPmean_UP=matrix(nrow=Nlocations,ncol=nrepeat)
maxBPPmax_UP=matrix(nrow=Nlocations,ncol=nrepeat)
MFE_UP=matrix(nrow=Nlocations,ncol=nrepeat)
n_DN=matrix(nrow=Nlocations,ncol=nrepeat)
ID_DN=matrix(nrow=Nlocations,ncol=nrepeat)
seq_DN=matrix(nrow=Nlocations,ncol=nrepeat)
SingleExon_DN=matrix(nrow=Nlocations,ncol=nrepeat)
Exon_DN=matrix(nrow=Nlocations,ncol=nrepeat)
transcript_DN=matrix(nrow=Nlocations,ncol=nrepeat)
LastExon_DN=matrix(nrow=Nlocations,ncol=nrepeat)
Gscore_DN=matrix(nrow=Nlocations,ncol=nrepeat)
GscoreDist_DN=matrix(nrow=Nlocations,ncol=nrepeat)
FinderSeq_DN=matrix(nrow=Nlocations,ncol=nrepeat)
qgrsSeq_DN=matrix(nrow=Nlocations,ncol=nrepeat)
maxBPPmean_DN=matrix(nrow=Nlocations,ncol=nrepeat)
maxBPPmax_DN=matrix(nrow=Nlocations,ncol=nrepeat)
MFE_DN=matrix(nrow=Nlocations,ncol=nrepeat)
########################################################################################################################################################################################################################
checkout2=as.data.frame(matrix(nrow = Nlocations,ncol=17));colnames(checkout2)=c('n','ID','CLIP_ID','crosslink','SingleExon','numberExCrossed','crosslinkexon','s1exon','e1exon','lastexon','TranscChoise','numberTranscript','transcript_id','exon#','sequence','strand','DeltaG')
checkout2_dn=as.data.frame(matrix(nrow = Nlocations,ncol=17));colnames(checkout2_dn)=c('n','ID','CLIP_ID','crosslink','SingleExon','numberExCrossed','crosslinkexon','s1dnexon','e1dnexon','lastexon','TranscChoise','numberTranscript','transcript_id','exon#','sequence','strand','DeltaG')
faout=matrix(nrow = (Nlocations*2),ncol=1)
faout_dn=matrix(nrow = (Nlocations*2),ncol=1)
checkout2_summary=matrix(nrow=nrepeat,ncol=11)
colnames(checkout2_summary)=c('SingleExon','numberExCrossed','lastexon','lastexonCrossexExon','firstexon','firstexonCrossexExon','total Locations','Gquad','Avg_GquadScore','MeanBPP','MaxBPP')
checkout2_dn_summary=matrix(nrow=nrepeat,ncol=11)
colnames(checkout2_dn_summary)=c('SingleExon','numberExCrossed','lastexon','lastexonCrossexExon','firstexon','firstexonCrossexExon',"total Locations",'Gquad','Avg_GquadScore','MeanBPP','MaxBPP')
isoout=separate(isoout,col = "ID",into = c('chr','start'),sep = ":",remove = F)
isoout=separate(isoout,col = "start",into = c('start','end'),sep = "-",remove = F)
isoout$start=as.numeric(isoout$start)
isoout$end=as.numeric(isoout$end)
isoout_transcAll=isoout ##column USEtranscript
Peaksdata4_chunkInExall=Peaksdata4_chunkEx
#### Intronic peaks only
if (CLIPtype=='Introns') {
Peaksdata4_chunkInall=Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon','Antisense Feature')==F,]
Peaksdata4_chunkEx=Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: Intron'),]
}
if (CLIPtype=='Introns_LINESINETLR') {
### will include peaks that do not OL with intron on Same strand but does on opposite strand
Peaksdata4_chunkInall1=Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon')==F,]
### Includes any peak that only overlaps with introns on same strand
Peaksdata4_chunkInall= Peaksdata4_chunkEx[grepl('intron',Peaksdata4_chunkEx$`Same Strand: Intron Exon`),]
### takes away CLIP peaks classified as opposite strand features and still could be interesting
Peaksdata4_chunkInall3= Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon','Antisense Feature')==F,]
Peaksdata4_chunkEx=Peaksdata4_chunkInall
}
#### exonic peaks only
if (CLIPtype=='Exons'&TrasncLoc=='all') {
Peaksdata4_chunkExall=Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon'),]
Peaksdata4_chunkEx=Peaksdata4_chunkEx[Peaksdata4_chunkEx$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon'),]
}
if (CLIPtype=='Exons'&TrasncLoc=='3UTR') {
Peaksdata4_chunkEx=Peaksdata4_chunkEx[(Peaksdata4_chunkEx$ID%in%isoout_transcAll[isoout_transcAll$TranscRegion%in%'3UTR','ID']),]
}
Peaksdata4_chunkEx=separate(Peaksdata4_chunkEx,ID,into = c('chr','start'),sep=':',remove = F)
Peaksdata4_chunkEx=separate(Peaksdata4_chunkEx,start,into = c('start','end'),sep='-')
Peaksdata4_chunkEx$start=as.numeric(Peaksdata4_chunkEx$start)
Peaksdata4_chunkEx$end=as.numeric(Peaksdata4_chunkEx$end)
############################################################################################################
## Checked Intron,exon peaks to find more Exon peak these are listed below
ExonAdd=c('chr1:71620722-71620766', 'chr10:115341383-115341422', 'chr10:122921578-122921615', 'chr10:53610110-53610170', 'chr11:100504854-100504876', 'chr11:115916957-115917009', 'chr15:80251044-80251086', 'chr16:17626516-17626585', 'chr17:45592176-45592225', 'chr17:83463473-83463526', 'chr2:129112779-129112816', 'chr3:108078674-108078713', 'chr8:124048273-124048334', 'chr9:106285553-106285575')
# ExonAdd=Peaksdata4[Peaksdata4$ID%in%ExonAdd,]
# ExonAdd=separate(ExonAdd,ID,into = c('chr','start'),sep=':',remove = F)
# ExonAdd=separate(ExonAdd,start,into = c('start','end'),sep='-')
# ExonAdd$start=as.numeric(ExonAdd$start)
# ExonAdd$end=as.numeric(ExonAdd$end)
#
# Peaksdata4_chunkEx=as.data.frame(rbind(Peaksdata4_chunkEx,ExonAdd))
## These couldbe exons depending on transcript
# ExonMaybe=c('chr12:69372320-69372375', 'chr2:71820222-71820258', 'chr4:138239647-138239709', 'chr5:110430755-110430813', 'chr7:133014567-133014602', 'chrX:38419895-38419931', 'chrX:48277218-48277276')
############################################################################################################
isoout=isoout[isoout$ID%in%Peaksdata4_chunkEx$ID,]
if (CLIPtype=='Introns_LINESINETLR') {CLIPtype='Introns'}
```
## Overlap with LINE/SINE
Select Any CLIP peaks that overlap with intronic regions. If Peak also Overlaps with a Repeat Element it is removed, except for peaks that overlap with LINE, SINE or LTR.
```{r ,echo=F,warning=F,eval=T,include=F}
################################################################################################################################################################
###### AllIntrons
checkout2_LS=Peaksdata4_chunkInExall[Peaksdata4_chunkInExall$`Both Strand: RNA_type_Classification`%in%c('protein_coding: exon')==F,]
# unique(checkout2_LS$`Both Strand: RNA_type_Classification`)
checkout2_LS$start=as.numeric(checkout2_LS$start)
checkout2_LS$end=as.numeric(checkout2_LS$end)
checkout2_LS[checkout2_LS$strand%in%"+",'crosslink']=checkout2_LS[checkout2_LS$strand%in%"+",'start']
checkout2_LS[checkout2_LS$strand%in%"-",'crosslink']=checkout2_LS[checkout2_LS$strand%in%"-",'end']
checkout2_LS=merge(checkout2_LS,isoout_transcAll[,c('ID','USEtranscript')],by='ID',all.x=T)
colnames(checkout2_LS)[colnames(checkout2_LS)%in%'USEtranscript']='transcript_id'
CrossLink_GR=
GRanges(seqnames=checkout2_LS$chr,
ranges=IRanges(start=checkout2_LS$crosslink,
end=checkout2_LS$crosslink),
strand=checkout2_LS$strand,
ID=checkout2_LS$ID,
transcript_id=checkout2_LS$transcript_id)
###################################################################
mm10_transall=mm10all[mm10all$feature%in%'transcript',]
mm10_transUSE=mm10_transall[mm10_transall$transcript_id%in%canonical$transcript,]
mm10_transUSE=mm10_transUSE[mm10_transUSE$transcript_type%in%'protein_coding',]
###########################
### Make sure that Transcripts from CLIP peaks are selected.
###########################
Uptrancripts=checkout2_LS$transcript_id
Uptrancripts_mm10_trans=mm10_transall[mm10_transall$transcript_id%in%Uptrancripts,]
mm10_transUSE=rbind(mm10_transUSE[mm10_transUSE$gene_id%in%unique(Uptrancripts_mm10_trans$gene_id)==F,],
Uptrancripts_mm10_trans)
trans_GR=GRanges(seqnames=mm10_transUSE$seqname,
ranges=IRanges(start=mm10_transUSE$start,end=mm10_transUSE$end),
strand=mm10_transUSE$strand,
transcript_id=mm10_transUSE$transcript_id,
gene_id=mm10_transUSE$gene_id
)
mm10_transUSE=mm10_transUSE[,c('transcript_id','seqname','start','end','strand')]
# ########################################################################################
# #### adjust CLIP coordinates
# #############
#
checkout2_LS=merge(checkout2_LS,mm10_transUSE,by='transcript_id',all.x=T,suffixes=c('_CLIP','_mm10'))
#
# checkout2_LS$CL_adj=NA
#
# checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'CL_adj']=
# checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'crosslink']-
# checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'start']
#
# checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'CL_adj']=
# checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'end']-
# checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'crosslink']
# checkout2_LS$TranscWidth=checkout2_LS$end-checkout2_LS$start
# checkout2_LS[checkout2_LS$CL_adj+1>=checkout2_LS$TranscWidth,]
# CrossLinkADJ=checkout2_LS[,c('transcript_id','chr','crosslink',"strand_CLIP",'ID','gene_id','gene_name','start','end','CL_adj')]
checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'CL_up']=checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'crosslink']-(window-1)
checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'CL_up']=checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'crosslink']+(window-1)
checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'CL_dn']=checkout2_LS[checkout2_LS$strand_CLIP%in%"+",'crosslink']+(window-1)
checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'CL_dn']=checkout2_LS[checkout2_LS$strand_CLIP%in%"-",'crosslink']-(window-1)
CL_GR = GRanges(seqnames=checkout2_LS$chr,
ranges=IRanges(start=checkout2_LS$crosslink,
end=checkout2_LS$crosslink),
strand=checkout2_LS$strand_CLIP,
ID=checkout2_LS$ID,
transcript_id=checkout2_LS$transcript_id
)
CLup_GR = GRanges(seqnames=checkout2_LS$chr,
ranges=IRanges(start=rowMins(as.matrix(checkout2_LS[,c('crosslink','CL_up')])),
end=rowMaxs(as.matrix(checkout2_LS[,c('crosslink','CL_up')]))),
strand=checkout2_LS$strand_CLIP,
ID=checkout2_LS$ID,
transcript_id=checkout2_LS$transcript_id
)
CLdn_GR = GRanges(seqnames=checkout2_LS$chr,
ranges=IRanges(start=rowMins(as.matrix(checkout2_LS[,c('crosslink','CL_dn')])),
end=rowMaxs(as.matrix(checkout2_LS[,c('crosslink','CL_dn')]))),
strand=checkout2_LS$strand_CLIP,
ID=checkout2_LS$ID,
transcript_id=checkout2_LS$transcript_id
)
# View(CrossLinkADJ[CrossLinkADJ$transcript_id%in%CrossLinkADJ[duplicated(CrossLinkADJ$transcript_id),'transcript_id'],])
###################################################################
rmsk_GRCm38=fread(paste0("/Users/homanpj/OneDrive - National Institutes of Health/Resources/ref/mm10/repeatmasker/rmsk_GRCm38.txt"), header=T, sep="\t",stringsAsFactors = F,data.table=F)
rmsk_GRCm38_LISI=rmsk_GRCm38[rmsk_GRCm38$repClass%in%c('LINE','SINE'),]
rmsk_GRCm38_LISI=rmsk_GRCm38_LISI[rmsk_GRCm38_LISI$genoName%in%c('chrUn_GL456239', 'chrUn_GL456359', 'chrUn_GL456360', 'chrUn_GL456366', 'chrUn_GL456367', 'chrUn_GL456368', 'chrUn_GL456370', 'chrUn_GL456372', 'chrUn_GL456378', 'chrUn_GL456379', 'chrUn_GL456381', 'chrUn_GL456382', 'chrUn_GL456385', 'chrUn_GL456387', 'chrUn_GL456389', 'chrUn_GL456393', 'chrUn_GL456394', 'chrUn_JH584304', 'chr1_GL456210_random', 'chr1_GL456211_random', 'chr1_GL456212_random', 'chr1_GL456213_random', 'chr1_GL456221_random', 'chr4_GL456216_random', 'chr4_GL456350_random', 'chr4_JH584292_random', 'chr4_JH584293_random', 'chr4_JH584294_random', 'chr5_GL456354_random', 'chr5_JH584296_random', 'chr5_JH584297_random', 'chr5_JH584298_random', 'chr5_JH584299_random', 'chr7_GL456219_random', 'chrX_GL456233_random', 'chrY_JH584300_random', 'chrY_JH584301_random', 'chrY_JH584302_random', 'chrY_JH584303_random')==F,]
rmsk_GRCm38_LISI$ID=paste0(rmsk_GRCm38_LISI$genoName,":",rmsk_GRCm38_LISI$genoStart,'-',rmsk_GRCm38_LISI$genoEnd)
LISI_GR = GRanges(seqnames=rmsk_GRCm38_LISI$genoName,
ranges=IRanges(start=rmsk_GRCm38_LISI$genoStart,
end=rmsk_GRCm38_LISI$genoEnd),
strand=rmsk_GRCm38_LISI$strand,
repClass=rmsk_GRCm38_LISI$repClass,
repName=rmsk_GRCm38_LISI$repName,
ID=rmsk_GRCm38_LISI$ID
)
### Get LINE/SINES that overlap with Transcripts
gr = trans_GR
gr2 = LISI_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=T))
LISI_Trans_GR=as.data.frame(gr2[xo$subjectHits])
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
Trans_LISI_GR=as.data.frame(gr[xo$queryHits])
colnames(LISI_Trans_GR)=paste0(colnames(LISI_Trans_GR),"_LINESINE")
colnames(Trans_LISI_GR)=paste0(colnames(Trans_LISI_GR),"_Trans")
LISI_Trans=cbind(as.data.frame(LISI_Trans_GR),as.data.frame(Trans_LISI_GR))
########################
### Upstream
########################
gr = CLup_GR
gr2 = LISI_GR
xo=as.data.frame(GenomicRanges::findOverlaps(gr,gr2,type = "any",ignore.strand=T))
LISI_CLup_GR=as.data.frame(gr2[xo$subjectHits])
# qh=as.data.frame(gr[xo$queryHits],row.names = NULL)
# colnames(qh)=paste0(colnames(qh),"_mm10")
# sh=as.data.frame(gr2[xo$subjectHits],row.names = NULL)
CLup_LISI_GR=as.data.frame(gr[xo$queryHits])
colnames(LISI_CLup_GR)=paste0(colnames(LISI_CLup_GR),"_LISI")
CLup_LISI_GR=as.data.frame(cbind(CLup_LISI_GR,LISI_CLup_GR))
dup=unique(CLup_LISI_GR[duplicated(CLup_LISI_GR$ID),"ID"])
CLup_LISI_GR_dup=CLup_LISI_GR[CLup_LISI_GR$ID%in%dup==T,]
CLup_LISI_GR_uniqe=CLup_LISI_GR[CLup_LISI_GR$ID%in%dup==F,]
CLup_LISI_GR_dupout=as.data.frame(matrix(nrow = length(dup),ncol=ncol(CLup_LISI_GR_dup)))
colnames(CLup_LISI_GR_dupout)=colnames(CLup_LISI_GR_dup)
for (d in 1:length(dup)) {
dm= CLup_LISI_GR_dup[CLup_LISI_GR_dup$ID%in%dup[d],]
dmm=(dm$strand==dm$strand_LISI)
if (length(unique(dmm))==1) {dm=dm[1,]}
if (length(unique(dmm))>1) {dm=dm[dmm%in%T,]
if (nrow(dm)>1) {dm=dm[1,]}}
CLup_LISI_GR_dupout[d,]=as.matrix(dm)
}