Chromatin Immunoprecipitation Sequencing    ◾    231

peaks2<- read.table(“chip2_peaks.narrowPeak”,header=FALSE)

colnames(peaks2) <- colnames

colnames(peaks2)

peaks3<- read.table(“chip3_peaks.narrowPeak”,header=FALSE)

colnames(peaks3) <- colnames

#head(peaks1)

peaks1Ranges<- GRanges(seqnames=peaks1$chrom,

ranges=IRanges(peaks1$start,peaks1$end),

peaks1$name,

peaks1$score,

strand=NULL,

peaks1$signal,

peaks1$pvalue,

peaks1$qvalue,

peaks1$peak)

covplot(peaks1Ranges, weightCol=”peaks1$peak”)

peaks2Ranges<- GRanges(seqnames=peaks2$chrom,

ranges=IRanges(peaks2$start,peaks2$end),

peaks2$name,

peaks2$score,

strand=NULL,

peaks2$signal,

peaks2$pvalue,

peaks2$qvalue,

peaks2$peak)

covplot(peaks2Ranges, weightCol=”peaks2$peak”)

peaks3Ranges<- GRanges(seqnames=peaks3$chrom,

ranges=IRanges(peaks3$start,peaks3$end),

peaks3$name,

peaks3$score,

strand=NULL,

peaks3$signal,

peaks3$pvalue,

peaks3$qvalue,

peaks3$peak)

covplot(peaks3Ranges, weightCol=”peaks3$peak”)

Three ChIP-Seq peak coverage plots will be created but we will display only a single plot

to save space. Figure 6.8 shows the coverage plot for the first sample (chip1); it shows the

distribution of the peaks of each human chromosome.

Rather than the entire genome, “covplot()” can also display the coverage in a single

chromosome, a group of chromosome, a specific region of a chromosome, or it can be