简介
CUT&Tag 技术会在靠近固定酶的染色质颗粒两侧加上接头，不过染色质颗粒内部的标签化反应也有可能发生。所以，当 CUT&Tag 针对组蛋白修饰时，得到的主要是核小体长度（大约 180 bp）或其倍数的片段。而如果目标是转录因子，就会生成核小体大小的片段，同时混杂一些较短的片段，这些短片段分别来自旁边的核小体和转录因子结合的位置。此外，核小体表面的 DNA 也会被标签化。通过绘制片段长度分布图（精确到单个碱基对），可以观察到 10 bp 的锯齿形周期变化，这是成功的 CUT&Tag 实验的一个典型标志。
评估测序片段的长度分布
##== linux command ==##
mkdir -p $projPath/alignment/sam/fragmentLen
## Extract the 9th column from the alignment sam file which is the fragment length
samtools view -F 0x04 $projPath/alignment/sam/${histName}_bowtie2.sam | awk -F'\t' 'function abs(x){return ((x < 0.0) ? -x : x)} {print abs($9)}' | sort | uniq -c | awk -v OFS="\t" '{print $2, $1/2}' >$projPath/alignment/sam/fragmentLen/${histName}_fragmentLen.txt
##=== R command ===##
## Collect the fragment size information
fragLen = c()
for(hist in sampleList){
histInfo = strsplit(hist, "_")[[1]]
fragLen = read.table(paste0(projPath, "/alignment/sam/fragmentLen/", hist, "_fragmentLen.txt"), header = FALSE) %>% mutate(fragLen = V1 %>% as.numeric, fragCount = V2 %>% as.numeric, Weight = as.numeric(V2)/sum(as.numeric(V2)), Histone = histInfo[1], Replicate = histInfo[2], sampleInfo = hist) %>% rbind(fragLen, .)
}
fragLen$sampleInfo = factor(fragLen$sampleInfo, levels = sampleList)
fragLen$Histone = factor(fragLen$Histone, levels = histList)
## Generate the fragment size density plot (violin plot)
fig5A = fragLen %>% ggplot(aes(x = sampleInfo, y = fragLen, weight = Weight, fill = Histone)) +
geom_violin(bw = 5) +
scale_y_continuous(breaks = seq(0, 800, 50)) +
scale_fill_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma", alpha = 0.8) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9) +
theme_bw(base_size = 20) +
ggpubr::rotate_x_text(angle = 20) +
ylab("Fragment Length") +
xlab("")
fig5B = fragLen %>% ggplot(aes(x = fragLen, y = fragCount, color = Histone, group = sampleInfo, linetype = Replicate)) +
geom_line(size = 1) +
scale_color_viridis(discrete = TRUE, begin = 0.1, end = 0.9, option = "magma") +
theme_bw(base_size = 20) +
xlab("Fragment Length") +
ylab("Count") +
coord_cartesian(xlim = c(0, 500))
ggarrange(fig5A, fig5B, ncol = 2)
