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ChIPseqMip6_preprocessing
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ChIPseqMip6_preprocessing
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#######################################################################
####### PRE-PROCESSING ChIP-seq RPOMETEO DATA #######
#######################################################################
## By Manuel Ugidos and Carme Nuño
# Raw data are available at GEO, GSE135568
#### THIS PORTION OF THE SCRIPT IS RUN IN HPC CLUSTER WITH A .SH SCRIPT ####
#############################################################################
## Step 1: Mapping files to reference genome
######### VERSION OF THE GENOME USED:
Reference Saccharomyces cerevisiae:
version: sacCer3
source: UCSC "https://genome.ucsc.edu"
Genes: sacCer3.gtf
######### VERSIONS OF THE SOFTWARE
samtools version 0.1.18
bowtie2 version 2.1.0
macs2 version 1.4.2
bedtools version 2.25.0
bowtie2 -x sacCer3 -U <sample>_reads.fastq.gz -S <sample>.sam
## Step 2: Identification of peaks.
# It was run using macs2.
# For each time point, wild type and delta_mip6 peaks
# were identified:
macs2 callpeak -t <sample>.sam (all samples of the same time*strain factor) --nomodel --broad -f BAM -g 12100000
# All peaks from H4K12ac samples were merged using bedtools
bedtools merge -i <all files>.bed > mergedPeaks.union.bed
# Then region file was converted to a gtf annotation file using a custom script.
## Step 3: Quantification of peaks: using htseq
htseq-count -a 20 -m union <sample>.sam mergedPeaks.union.gtf
# By merging all count files a ChIPcounts.txt file was generated.
## Step 4: Coverage per base using bedtools
bedtools genomecov -d -ibam <sample>.bam -g sacCer3 <sample>.bigWig
# bigWig files were separated in chromosomes creating 17 different folders,
# in each folder the bigWig file of all the samples for a certain chromosome
# were placed.
#### THIS PORTION OF THE SCRIPT IS RUN IN R USING A .R SCRIPT ####
##################################################################
## LIBRARIES TO USE
library(NOISeq)
### Reading file with region raw counts -------------------------------------
raw_data = read.delim("ChIPcounts.txt", header = TRUE, as.is = TRUE, sep = "\t")
### QC using NOISeq ---------------------------------------------------------
tag <- sapply(colnames(raw_data), function(x) {strsplit(x, "\\.")[[1]][1]})
strain <- sapply(colnames(raw_data), function(x) {strsplit(x, "\\.")[[1]][2]})
time <- sapply(colnames(raw_data), function(x) {strsplit(x, "\\.")[[1]][4]})
batchSP <- c(rep(1:2,12)[-23],1,1,1,2,2,2,1,1,2,2,1,1,1,2,2,1,1,2,2)
lengths <- read.table("lengths.txt", header = FALSE) # file with lengths of the peaks obtained with macs2
## Create object
factores_chip <- data.frame(strain, tag, time,
batchSP, "SxT" = paste0(tag,strain,time))
mychip <- readData(data = raw_data, factors = factores_chip,
length = lengths)
## RNA composition
mycd<- dat(mychip, type = "cd", norm = F, refColumn = 1)
explo.plot(mycd, samples = 1:12)
## Length bias
mylenbias <- dat(mychip, factor = "SxT", type = "lengthbias")
explo.plot(mylenbias, samples = NULL, toplot="global")
## Batch exploration
myPCA <- dat(mychip, type = "PCA", norm = F)
explo.plot(myPCA, factor = "batchSP")
## CPM
mycounts <- dat(mychip, factor = NULL, type = "countsbio")
explo.plot(mycounts, toplot=1, samples = NULL, plottype = "barplot")
## Library size correction
mychipTMM <- tmm(assayData(mychip)$exprs,
long = lengths,
lc = 1)
write.table((mychipTMM), row.names = T, col.names = T,
sep = "\t", file = "CountMatrixChIP-seq.txt")
## replicability
pairs(log2(mychipTMM)[,c(27,32,38,39)], panel= function(x,y,...){
points(x,y, pch = 19);
abline(0,1,col="indianred")})
### Correcting K12 samples with H4 samples ----------------------------------
matchnames <- paste0(factores_chip$strain, factores_chip$time)
## H4 average counts
mediasH4 <- sapply(unique(factores_chip$SxT)[1:6], function(x) {
aux <- (mychipTMM[,which(factores_chip$SxT==x)])
apply(aux, 1, mean)
})
colnames(mediasH4) <- unique(matchnames)
## Normalized K12 samples
normK12 <- NULL
auxK12 <- sapply(colnames(mediasH4), function(x) {
aux <- (mychipTMM[,24:42][,which(matchnames[24:42]==x),drop = FALSE])
apply(aux, 2, function(y) {y/(mediasH4[,x])})
})
for ( i in seq_along(auxK12)) {
normK12 <- cbind(normK12,auxK12[[i]])
}
## Analysis of coverage per base metrices -------------------------------
# Four files were created from the sacCer3.gtf and the .bigWig files
### Creating information files of genes and TSS locations ---------------
gff <- read.csv("saccer3.gtf", skip = 5, header = F, sep = "\t")
## Select pos genes
gff <- gff[which(gff$V7=="+"),]
genes <- sapply(gff$V9, function(x) {
strsplit(strsplit(toString(x), ";")[[1]][1], " ")[[1]][2]
})
gff <- data.frame(gff, genes)
## Obtain the coordinates of each gene
{gene_regions_pos <- (vapply(unique(genes), function(x) {
aux <- gff[which(gff$genes==x),]
st <- min((aux[which(aux$V3=="gene"),4:5]))
sp <- max((aux[which(aux$V3=="gene"),4:5]))
chr <- toString(aux[which(aux$V3=="gene"),1])
c(chr, max(st-100,0), sp+100)
}, FUN.VALUE = c(chr= "", start = 0, stop = 0)))
gene_regions_pos <- t(gene_regions_pos)}
## Obtain the coordinates of the TSS
{tss_regions_pos <- (vapply(unique(genes), function(x) {
aux <- gff[which(gff$genes==x),]
st <- min((aux[which(aux$V3=="gene"),4:5]))
chr <- toString(aux[which(aux$V3=="gene"),1])
#c(chr, st-150, min(sp+150, seq_lim[chr]))
c(chr, max(st-100,0), st+100)
}, FUN.VALUE = c(chr= "", start = 0, stop = 0)))
tss_regions_pos <- t(tss_regions_pos)}
# Changing "+" to "-" in line 131, we can obtain the coordinates for genes
# in the negative strand, so we can obtain: gene_regions_anti and tss_regions_anti
# Taking into account that TSS in negative stranded genes is the sp position.
## Analysis of the metagene ------------------------------------------------
## Open bigWig files
filelist <- list.files("bigWigfiles/chr1/", pattern = "*.txt")
names <- sapply(filelist, function(x) strsplit(x,"\\.")[[1]][1])
BW <- NULL
for ( j in 1:17) {
BWaux <- NULL
for ( i in filelist) {
bw <- read.csv (paste0("bigWigfiles/chr",j,"/",i), sep = "\t", header = F)
BWaux <- cbind(BWaux, bw[,3])
}
BW <- rbind(BW, BWaux)
}
bw <- NOISeq::tmm(BW)
text <- c()
for ( i in 1:17) {
if (i == 1) {
text <- c(text,paste0("chr I: ", 1:lenchr[i]))
} else {
text <- c(text,paste0(chr[i],": ", 1:lenchr[i]))
}
}
rownames(bw) <- text
colnames(bw) <- c("H4_mip6_30", "H4_mip6_39_120", "H4_mip6_39_20",
"H4_WT_30", "H4_WT_39_120", "H4_WT_39_20",
"H4K12_mip6_30", "H4K12_mip6_39_120", "H4K12_mip6_39_20",
"H4K12_WT_30", "H4K12_WT_39_120", "H4K12_WT_39_20")
write.table(bw, row.names = T, col.names = T,
sep = "\t", file = "CoveragePerBaseChIP-seq.txt")
## Compute metagene
AVpos <- matrix(0, nrow = 12, ncol = 201)
stepaux <- c()
for ( c in unique(gene_regions_pos[,1])) {
chrindex <- which(names(lenchr)==c)
if (chrindex == 1) {
bw2 <- bw[1:(sum(lenchr[1:chrindex])),]
} else {
bw2 <- bw[(sum(lenchr[1:max(chrindex-1,1)])):(sum(lenchr[1:chrindex])),]
}
for ( j in 1:dim(z[which(z[,1]==c),,drop=FALSE])[1]) {
x <- z[which(z[,1]==c),,drop=FALSE][j,]
AVaux <- NULL
cont.init <- as.numeric(x[2])
step <- round((as.numeric(x[3])-as.numeric(x[2]))/200)
#BW <- 2**(normalizeMedianValues(log2(BW+1)))
stepaux <- c(stepaux, step)
for ( i in 0:200) {
ini <- cont.init
end <- cont.init + step
aux <- bw2[ini:end,]
aux2 <- apply(aux,2,mean)
AVaux <- cbind(AVaux, aux2)
cont.init <- cont.init + step
}
rownames(AVaux) <- names
AVpos <- AVpos + AVaux
}
}
# Changing "pos" to "anti", we get the coverage for genes in the negative strand
# The result of the genes in the negative strand must be turn around in order to be
# compare with the positive stranded genes.
AVanti2pos <- t(apply(AVanti, 1, function(x) {
r <- c()
for ( i in seq(201,1,-1)) {
r <- c(r, x[i])
}
r
}))
# Total result
AV <- AVpos + AVanti2pos
## Plot
tocolors <- rep(c("darkolivegreen", "lightsalmon3", "lightblue4",
"darkolivegreen1", "lightsalmon", "lightblue"),2)
tolty <- c(rep(5,6), rep(1,6))
plot(AV[1,], type = "n", ylim = c(200,800), col = tocolors[1],
lty = 1, lwd = 3, cex.axis = 2, cex.main = 2, ylab = "", xlab = "")
for ( i in c(1:12)) {
lines(AV[i,], col = tocolors[i], lty = tolty[i], lwd = 3)
}
# With the same code and by changing "gene_regions_pos" to "tss_regions_pos"
# the coverage across TSS region exclusively can be obtained.