CUT&Tag 数据处理和分析教程（5）

引言

本系列 将开展全新的CUT&Tag 数据处理和分析专栏。想要获取更多教程内容或者生信分析服务可以添加文末的学习交流群或客服QQ:941844452。

重复去除

CUT&Tag 技术会将接头序列插入到抗体连接的 pA-Tn5 附近的 DNA 中，而插入的具体位置会受到周围 DNA 可及性的影响。因此，那些起始和结束位置完全相同的片段是比较常见的，但这些所谓的“重复项”可能并不是由于 PCR 过程中的复制产生的。实际上，发现高质量的 CUT&Tag 数据集的表观重复率通常很低，即使是看起来像是“重复”的片段，也可能是真实的片段。因此，不建议删除这些重复项。不过，在实验样本量极少，或者怀疑存在 PCR 扩增重复的情况下，可以考虑删除重复项。以下命令展示了如何使用 Picard 来检查重复率。

##== linux command ==##
## depending on how you load picard and your server environment, the picardCMD can be different. Adjust accordingly.
picardCMD="java -jar picard.jar"
mkdir -p $projPath/alignment/removeDuplicate/picard_summary

## Sort by coordinate
$picardCMD SortSam I=$projPath/alignment/sam/${histName}_bowtie2.sam O=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam SORT_ORDER=coordinate

## mark duplicates
$picardCMD MarkDuplicates I=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam O=$projPath/alignment/removeDuplicate/${histName}_bowtie2.sorted.dupMarked.sam METRICS_FILE=$projPath/alignment/removeDuplicate/picard_summary/${histName}_picard.dupMark.txt

## remove duplicates
picardCMD MarkDuplicates I=$projPath/alignment/sam/${histName}_bowtie2.sorted.sam O=$projPath/alignment/removeDuplicate/${histName}_bowtie2.sorted.rmDup.sam REMOVE_DUPLICATES=true METRICS_FILE=$projPath/alignment/removeDuplicate/picard_summary/${histName}_picard.rmDup.txt

总结了明显的重复率，并计算出唯一的库大小而无需重复。

##=== R command ===## 
## Summarize the duplication information from the picard summary outputs.
dupResult = c()
for(hist in sampleList){
  dupRes = read.table(paste0(projPath, "/alignment/removeDuplicate/picard_summary/", hist, "_picard.rmDup.txt"), header = TRUE, fill = TRUE)
  
  histInfo = strsplit(hist, "_")[[1]]
  dupResult = data.frame(Histone = histInfo[1], Replicate = histInfo[2], MappedFragNum_hg38 = dupRes$READ_PAIRS_EXAMINED[1] %>% as.character %>% as.numeric, DuplicationRate = dupRes$PERCENT_DUPLICATION[1] %>% as.character %>% as.numeric * 100, EstimatedLibrarySize = dupRes$ESTIMATED_LIBRARY_SIZE[1] %>% as.character %>% as.numeric) %>% mutate(UniqueFragNum = MappedFragNum_hg38 * (1-DuplicationRate/100))  %>% rbind(dupResult, .)
}
dupResult$Histone = factor(dupResult$Histone, levels = histList)
alignDupSummary = left_join(alignSummary, dupResult, by = c("Histone", "Replicate", "MappedFragNum_hg38")) %>% mutate(DuplicationRate = paste0(DuplicationRate, "%"))
alignDupSummary

• 在这些示例数据集中，IgG 对照样本的重复率比较高。这是因为这些样本中的数据来源于 CUT&Tag 反应中的非特异性片段化。因此，在进行下游分析之前，从 IgG 数据集中去除重复项是比较合理的。
• 估计的文库大小是根据 Picard 计算的 PE 重复率来估算文库中独特分子数量的。
• 估计的文库大小与目标表位的丰度以及抗体的质量成正比，而 IgG 样本的估计文库大小通常会很低。
• 独特片段数是通过公式 MappedFragNum_hg38 * (1 - DuplicationRate/100) 计算得出的。

##=== R command ===## 
## generate boxplot figure for the  duplication rate
fig4A = dupResult %>% ggplot(aes(x = Histone, y = DuplicationRate, fill = Histone)) +
    geom_boxplot() +
    geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
    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 = 18) +
    ylab("Duplication Rate (*100%)") +
    xlab("") 

fig4B = dupResult %>% ggplot(aes(x = Histone, y = EstimatedLibrarySize, fill = Histone)) +
    geom_boxplot() +
    geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
    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 = 18) +
    ylab("Estimated Library Size") +
    xlab("") 

fig4C = dupResult %>% ggplot(aes(x = Histone, y = UniqueFragNum, fill = Histone)) +
    geom_boxplot() +
    geom_jitter(aes(color = Replicate), position = position_jitter(0.15)) +
    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 = 18) +
    ylab("# of Unique Fragments") +
    xlab("")

ggarrange(fig4A, fig4B, fig4C, ncol = 3, common.legend = TRUE, legend="bottom")

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