6.3.Ribo-seq

1) Workflow

2) Background

Ribo-seq是2009年Weissman课题组首次发表的研究细胞内蛋白翻译图谱的新型二代测序技术,用来描述全基因组水平蛋白质的翻译情况。主要是选择性捕捉80S核糖体及其结合的RNA片段而定位核糖体所位于的RNA的位置

具体步骤为:

  • 在细胞裂解物中富集多聚核糖体(polysome);

  • 将多聚核糖体用核酸酶(RNA nuclease)消化成单核糖体(monosome)

  • 选择性的收集和富集80S核糖体并经纯化得到80S核糖体所保护的RNA片段。

  • 在此过程中,将80S核糖体保护的RNA片段进行下一步构建文库和测序(图1)。

  • 最后,通过生物信息学的分析获得细胞当前状态下的翻译图谱 。

Ribo-seq数据测得的RNA片段长短与small RNA-seq相似,大约分布在25~35nt区间。由于Ribo-seq是特异性描述细胞的翻译组,因此其数据的测序片段大多比对到基因组的CDS区域(coding region)。此外,Ribo-seq还有一个明显区别于其他RNA-seq的特点,即Ribo-seq的序列在CDS区域往往呈现3-nt的周期性(图1)。这主要依赖于翻译过程中核糖体通常以3-nt的周期进行移动。

图1

3) Running steps (RiboWave)

(1) Pre-processing

启动ribo-seq所用的 Docker,进入工作目录

cd /home/test/rna_regulation
cd /home/test/rna_regulation/ribo-wave

(2) Create annotation

# bedtools2没有添加到环境变量中,需要临时添加
export PATH=$PATH:test@bioinfo_docker:~/software/bedtools2/bin
# 运行annotation脚本(这一步时间会很久,生成文件已经提前跑好放在了annotation_fly目录下,可以跳过直接进行下一步)
script/create_annotation.sh \
-G annotation_fly/dmel-all-r6.18.gtf \
-f annotation_fly/dmel-all-chromosome-r6.18.fasta \
-o annotation_fly \
-s script

(2.1) input files

  • annotation.gtf: the annotation gtf should contain start_codon and stop_codon information, eg: dmel-all-r6.18.gtf

  • genome.fasta: genome fasta, eg: dmel-all-chromosome-r6.18.fasta

  • annotation_dir: the directory for all the annotation output

  • scripts_dir: the directory of all the scripts in the package

(2.2) output files

annotation directory, including :

  • start_codon.bed: the bed file annotating start codon

  • final.ORFs: all identified ORFs, eg: FBtr0300105_0_31_546 where FBtr0300105 refers to the transcript, 0 refers to the reading frame relative to the start of transcript, 31 refers to the start site, 546 refers to the stop codon.

(3) P-site determination

核糖体上具有一系列与蛋白质合成有关的结合位点与催化位点,分别为A位点(aminoacyl-site,A-site),P位点(peptidyl-site,P-site)和E位点(exit-site,E-site)先后与tRNA发生结合。P位点是肽段翻译延长的主要场所,在该位点上tRNA将携带的氨基酸移交给旁边的肽段从而使得肽段序列发生延长。为了能够更加明显的观察到3-nt的周期性,在处理Ribo-seq数据时我们参考之前已发表的方法,对每一条Ribo-seq比对上的测序片段转换为其对应的P-site位点。

This step determines the P-site position for each Ribo-seq reads length by overlapping with the annotated start codons from previous step

# 运行P-site_determination.sh脚本
script/P-site_determination.sh \
-i GSE52799/SRR1039770.sort.bam \
-S annotation_fly/start_codon.bed \
-o GSE52799 \
-n SRR1039770 \
-s script;

(3.1) input files

  • Ribo_bam: secondary alignment removed to ensure one genomic position per aligned read and sorted

  • start_codon.bed: annotated start site start_codon.bed. It is generated in the create_annotation.sh step

  • out_dir: the directory of the output result, eg: GSE52799

  • study_name: the name of all the output file, default: test. eg: SRR1039770

  • scripts_dir: the directory of all the scripts in the package

查看输出

$ cd /home/test/rna_regulation/ribo-wave/GSE52799/P-site
$ ls
SRR1039770.psite1nt.txt SRR1039770.psite.pdf SRR1039770.psite.txt
#正常时输出这三个文件
$ cp *pdf /home/test/share/
# 拷贝pdf文件到容器与计算机互通的文件夹,可以用pdf阅读工具打开查看pdf

(3.2) output files

P-site directory, including :

  • name.psite1nt.txt: the Ribo-seq reads length and its corresponding P-sites position(= offset + 1). It may look this this :

    30 13
  • name.psite.pdf: the PDF displaying the histogram of aggregated reads

    我们收集了所有已被注释的起始密码子并将 这些起始密码子和Ribo-seq 比对上的序列进行重合,分别计算Ribo-seq序列的5’端偏离起始密码子的第一个碱基A的距离(offset)。根据 Ribo-seq测序片段长度的不同,我们进一步将Ribo-seq片段分成多个组分。在每个长度对应的组分里,作出Ribo-seq片段5’端偏离起始密码子A的距离(offset)的直方图。

每一行代表不同长度的 Ribo-seq 测序片段的直方图。该数据中,30nt的reads数目最多,在30nt长度的Ribo-seq片段中,我们可以明显的看到在距离为13nt的位点含有一个峰值(peak)。鉴于大部分核糖体会在翻译起始位点停滞较多的时间,因此对于30nt长的Ribo-seq片段,其P-site位点的定义应该代表直方图中绝大多数的核糖体,因此我们将P-site位点应该定义为峰值最高的第13个碱基(13nt)的位置。

(4) Generating P-site track

基于Ribo-seq序列及其确定的P-site位点,将规律推广到所有Ribo-seq的片段中,直接根据Ribo-seq序列的长度推断其对应的P-site位点。根据这种方法,我们可以将每一条转录本上所有的Ribo-seq片段转化为对应的P-site位点的信号点并获得转录组水平的Ribo-seq信号轨迹(signal track)。由于是由P-site位点定义出的信号轨迹,通常也被叫做P-site信号轨迹(P-sites track)。转录本上每一个位点的信号丰度代表了有多少Ribo-seq片段对应的P-site位点落在该位置上。

This step creats the P-site track for transcripts of interests using determined P-sites position from previous step. look at transcripts from chromosome X :

查看输出

script/create_track_Ribo.sh \
-i GSE52799/SRR1039770.sort.bam \
-G annotation_fly/X.exons.gtf \
-g annotation_fly/genome \
-P GSE52799/P-site/SRR1039770.psite1nt.txt \
-o GSE52799 \
-n SRR1039770 \
-s script

(4.1) input files

  • Ribo_bam

  • exons.gtf: a gtf file for only the exons from transcripts of interest, eg: X.exons.gtf

  • chromosome_size: the file including all the chromosomes and its genome size. Noted: genome can be obtained by using samtools faidx function with the input of fasta file. genome may look like this:

    2L 23513712
    2R 25286936
    3L 28110227
    3R 32079331
  • P-site_position: the file listing the P-site position for each read length. This file can be found in the output of previous step, eg: name.psite1nt.txt

  • out_dir: the directory of the output result, eg: GSE52799

  • study_name: the name of all the output file, default: test. eg: SRR1039770

  • scripts_dir: the directory of all the scripts in the package

查看生成文件

$ cd /home/test/rna_regulation/ribo-wave/GSE52799/bedgraph/SRR1039770
$ ls
final.psite
$ less final.psite

(4.2) output files

  • bedgraph/name directory, including :

    final.psite : P-site track at transcriptome wide. It may look like this :

    FBtr0070533 0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,1,0,0,0,0,0,0,4,8,0,0,3,0,5,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0073886 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0070604 0,0,0,0,0,0,0,0,0,0,0,0,59,6,0,1,0,0,2,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0070603 0,0,0,0,0,0,0,0,0,0,0,0,75,2,7,10,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

(5) RiboWave main function

This step can achieve multiple functions :

  • denoising [denoise]

降噪:在降噪过程运用了小波变换来去除原始信号中非3-nt周期性的信号使得保留的信号均具有翻译的3-nt周期性,即PF P-site信号轨迹。

  • providing predicted p.value for each given ORF to identify its translation status [pvalue,-P]

鉴定ORF的翻译潜能:根据该转录本上经降噪后的P-sites是否富集在该ORF所在的阅读框内,判断ORF的翻译潜能。P<0.05即具有翻译活性。

  • providing reads density (P-site/PF P-site) for each given ORF [density,-D]

reads density = ORF上reads数/ORF长度,反映翻译水平的abundance。

  • providing translation efficiency (TE) estimation for each given ORF [TE,-T]

TE = 翻译水平的abundance/转录水平的abundance,反映翻译效率。

  • providing frameshift potential (CRF score) for each given ORF [CRF,-F]

鉴定潜在核糖体移码现象。

It might take hours to perform the analysis if the input is large. It is recommended to specify the number of CPU cores through the -p option.

Run Ribowave on example:

(5.1) Denoise the P-site track

# 在/home/test/rna_regulation/ribo-wave目录下
mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
script/Ribowave \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/final.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s script \
-p 8

(5.2) Identifying translated ORF

mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
script/Ribowave \
-P \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/final.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s script \
-p 8

(5.3) Estimating abundance

mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
script/Ribowave \
-D \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/final.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s script \
-p 8

(5.4) Estimating TE

IMPORTANT : when estimating TE, user should input the sequenced depth of Ribo-seq and the FPKM value from paired RNA-seq

mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
script/Ribowave \
-T 9012445 GSE52799/mRNA/SRR1039761.RPKM \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/final.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s scripts \
-p 8

(5.5) Calculating frameshift potential

on annotated ORFs

mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
awk -F '\t' '$3=="anno"' annotation_fly/final.ORFs > annotation_fly/aORF.ORFs;
script/Ribowave \
-F \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/aORF.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s script \
-p 8

(5.6) Multiple functions

mkdir -p /home/test/rna_regulation/ribo-wave/GSE52799/Ribowave
script/Ribowave \
-PD \
-T 9012445 GSE52799/mRNA/SRR1039761.RPKM \
-a GSE52799/bedgraph/SRR1039770/final.psite \
-b annotation_fly/final.ORFs \
-o GSE52799/Ribowave \
-n SRR1039770 \
-s script \
-p 8

(5.7) input files

  • bedgraph/name:

    P-site track: output from the previous step, containing the P-site track of transcripts of interest, eg: final.psite

  • ORF_list: ORFs of interest ,eg : final.ORFs. It is generated in the step of create_annotation.sh

  • Ribo-seq sequenced depth: the sequenced depth of Ribo-seq to calculate FPKM , eg: 9012445

  • RNA FPKM: FPKM table. It may look like this :

FBtr0100871 22262
FBtr0070604 18682
FBtr0100231 14746.5
FBtr0100874 14024.5
FBtr0100864 11475.6

(5.8) output files

  • name.PF_psite: the denoised signal track(PF P-sites signal track) at transcriptome wide. It looks similar as the input final psite.

  • including chi-square P-value information. It may look like this :

    column1-4: basic information about the ORF
    column5: reads coverage within the ORF
    column6: P-value predicted by RiboWave
    column7: Values estimating the relative abundance of PF P-sites outside of the studied ORF
    column8: Reads intensity at the current start codon

    result directory, including :

  • name.95%.mx: RiboWave makes the prediction on the translation initiation sites and gives the final translated product output (p.value < 0.05) . It may look like this :

    FBtr0070007_2_93_1028
    FBtr0070008_1_128_943
    FBtr0070025_2_135_1094
  • name.density: reads density ( PF P-site ) of given ORFs. It may look like this :

    column1-4: basic information about the ORF
    column5: number of PF P-sites in transcript
    column6: number of PF P-sites in given ORF
    column7: density of PF P-sites in given ORF
  • name.TE: TE of given ORFs. It may look like this :

    column1: transcript
    column2: ORF
    column3: TE
  • name.CRF.final: ORFs that might experience reading frame translocation. It may look like this :

    column1: ORF
    column2: start of frameshift
    column3: stop of frameshift
    column4: PF P-sites' reading frames after the change point ,eg: 2_2,0_1 where 2_2 refers to continuous two PF P-sites of frame 2 followed by continuous one PF P-sites of frame 0.
    column5: Relative position of PF P-sites after the shift ,eg : 1413,1440;1789 where 1413,1440 corresponds to the exact position of 2_2 within the transcript. Discontinuity in the reading frame is separated by ;
    column6: CRF score describing the potential of frameshift

4) Database

http://lulab.life.tsinghua.edu.cn/postar/

5) Homework

  • 解释TE的含义,用教程中的示例文件算出TE,并画出TE的分布。