Reads在参考基因组各染色体上的分布情况,一方面有利于了解本次测序结果中reads的覆盖情况,另一方面也能够获得转录活性高低及其分布情况。具体作图方法为:
图中,横坐标为染色体的长度信息,纵坐标为计算得到的reads密度,其中绿色为正链,红色为负链。
bedtools makewindows -b genome.gtf -w 1000 > genome.window.bed
genome.gtf
genome.window.bed
awk '{print $1"\t"$2"\t"$3"\t0\t0\t+\n"$1"\t"$2"\t"$3"\t0\t0\t-"}' genome.window.bed > genome.window.bed.region
bedtools bamtobed -i hisat2.bam > hisat2.bed
bedtools coverage -S -a genome.window.bed.region -b hisat2.bed > genome.window.bed.region.cov
data <- read.table("/ssd1/wy/workspace/RNA-seq/workspace/resources/genome.window.bed.region.cov",sep="\t") library(tidyverse) data <- data |> mutate(log=case_when(V6=="+"~log2(V7+1), V6!="+"~ -log2(V7+1))) chr <-c("chr1","chr2","chr3","chr4","chr5","chr6","chr7","chr8","chr9","chr10","chr11","chr12","chr13","chr14","chr15","chr16","chr17","chr18","chr19","chr20","chr21","chr22","chrX","chrY") #因为人类染色体还有很多非常规染色体,如果全部画出来会不好看,所有此处只画出常规染色体。也可以在文件hg19.txt里面只输入常规染色体。data2<- data[data$V1 %in% chr,] data2<- data[data$V1 %in% c(c(1:20),"X","Y","MT"),] color <- c("+"="#008B00","-"="#FF8247") # 定义正负链颜色 png(file="a.png") ggplot(data2)+ facet_grid(V1~.)+ geom_point(aes(V2/1000000,log,colour=V6),size=1)+ theme( strip.text.y=element_text(angle=0,face="bold",hjust=0), legend.position="none",panel.grid.minor=element_blank(), panel.grid.major=element_blank(), plot.title=element_text(size=20,face="bold"), axis.text.y=element_text(size=10,angle=50,face="bold"), strip.background=element_blank(), panel.background=element_rect(fill="white"), axis.line=element_line(linetype=1))+ scale_colour_manual(values=color)+ scale_y_continuous(breaks=c(-15,15))+ labs(title="Reads Density in Chromosomes")+ xlab("chromosome position(Mb)")+ ylab("reads density(log2)") dev.off()
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