4.3.RBP-RNA Interactions

1) Background

CLIP-seq (crosslinking-immunprecipitation and high-throughput sequencing),是一项全基因组水平揭示RNA分子与RNA结合蛋白相互作用的革命性技术。其主要原理是基于RNA分子与RNA结合蛋白在紫外照射下发生耦联,以RNA结合蛋白的特异性抗体将RNA-蛋白质复合体沉淀之后,回收其中的RNA片段,经添加接头、RT-PCR等步骤,对这些分子进行高通量测序,再经生物信息学的分析和处理、总结,挖掘出其特定规律,从而深入揭示RNA结合蛋白与RNA分子的调控作用及其对生命的意义。

POSTAR2数据库中收集了大量的CLIP-seq,并用统一的生物信息学流程鉴定出RBP的结合位点,可以从该数据库中下载得到不同物种里,大量RBP在不同的细胞系、组织或者发育阶段的结合位点。

2) workflow

3) RBP enrichment

(1) get CLIP-seq data

文件和软件获取方式 (我们在 docker 中已提供了下载好的示例文件)

  • 数据来源: POSTAR2

  • 为保证CLIP-seq方法的统一和取出较小的文件方便大家练习,我们从human RBP binding sites中提取第八列为eCLIP得到的文件,得到一个较小的文件: human_RBP_eCLIP_hg38.txt

  • 文件示例(human_RBP_eCLIP_hg38.txt):

chr1 187044 187087 human_RBP_eCLIP_ENCODE_1 0 - AARS eCLIP K562 ENCODE 3.02982873699729
chr1 267418 267466 human_RBP_eCLIP_ENCODE_2 0 - AARS eCLIP K562 ENCODE 5.50800008227284
chr1 630751 630792 human_RBP_eCLIP_ENCODE_3 0 - AARS eCLIP K562 ENCODE 3.5454755693998
  • explanation for each column:

column1:chromosome
column2:peak start
column3:peak end
column4:name
column5:0
column6:strand
column7:RBP name
column8:CLIP-seq technology, peak calling method
column9:cell line or tissue
column10:data accession
column11:score(Piranha score: Peak heights from the CLIP-seq data; PARalyzer score: T-to-C transition ratios ranging from 0 to 1, ratios greater than 0.5 indicate protein-binding while less than 0.5 indicate without protein-binding; CIMS score: Mismatch peak heights from the CLIP-seq data; CITS score: Truncated peak heights from the CLIP-seq data; eCLIP score: -log10 P-value, the threshold is 3.)

(2) prepare interested bed and background bed

(2.1) interested bed

up.bed

chr1 1167104 1167134 ENSG00000207730.3__1167104__1167134__1 . +
chr1 1167119 1167149 ENSG00000207730.3__1167119__1167149__2 . +
chr1 1167134 1167164 ENSG00000207730.3__1167134__1167164__3 . +

(2.2) prepare background bed from interested bed

the same as method in sequence_motif (1) downstream/upstream 1000bp (2) shuffle the input sequence (3) shuffle the genome sequence

(3) overlap between CLIP-seq peaks and interested/background bed

use intersectBed (bedtools) command to get the overlaps of CLIP-seq peaks

cd /home/test/rbp
intersectBed -wa -wb -s -a up.bed -b human_RBP_eCLIP_hg38.txt >up.eCLIP.hg38.txt
intersectBed -wa -wb -s -a up_background.bed -b human_RBP_eCLIP_hg38.txt >up_background.eCLIP.hg38.txt

(4) compare RBP enrichment

(4.1) percentage of region with CLIP-seq peaks

cut -f 4 up.bed |sort -u|wc -l
1685
cut -f 4 up.eCLIP.hg38.txt |sort -u|wc -l
663
cut -f 4 up_background.bed |sort -u|wc -l
1685
cut -f 4 up_background.eCLIP.hg38.txt |sort -u|wc -l
544

(4.2) RBP enrichment in each region

cat <(cut -f 4,13 up.eCLIP.hg38.txt |sort -u|cut -f 1|sort|uniq -c|sed 's/^[ \t]*//g'|sed 's/ /\t/g'|awk 'BEGIN{FS="\t";OFS="\t"}{print $1,$2,"up"}') <(cut -f 4,13 up_background.eCLIP.hg38.txt |sort -u|cut -f 1|sort|uniq -c|sed 's/^[ \t]*//g'|sed 's/ /\t/g'|awk 'BEGIN{FS="\t";OFS="\t"}{print $1,$2,"up_bg"}')|sed '1i density\tname\tclass' >RBP_enrichment.txt

screen short of "RBP_enrichment.txt"

density name class
1 ENSG00000004897.11__47141883__47141913__96 up
2 ENSG00000104723.20__15540418__15540448__14 up
2 ENSG00000115758.12__10441554__10441584__100 up

boxplot of RBP enrichment

R
library(ggplot2)
library(reshape2)
data = read.table('/home/test/rbp/RBP_enrichment.txt',head=T);
pdf('/home/test/rbp/rbp_enrichment.pdf',width=3,height=6);
attach(data)
boxplot(density~class,data=data,outline=F,col=c("red","gray"))
dev.off()
q()
# Save workspace image? [y/n/c]: n
# Enter

3) Homework

  • 根据提供的上调gene的ID,自己准备作为比较的背景gene,并详细写清楚得到背景gene的步骤;比较这些上调gene和背景gene的3‘UTR区域RBP的富集程度;最后提交每一步的代码和相应的图。相应的文件在本章节的homework文件夹下。