原创10000+生信教程大神给你的RNA实战视频演练

2018-08-17 本文已影响138人因地制宜的生信达人

准备工作

1. 安装conda

推荐使用偷懒方法，比如安装miniconda软件，下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/ 这样就可以使用它安装绝大部分其它软件。

但是在中国大陆的小伙伴，需要更改镜像源配置

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/bioconda
conda config --set show_channel_urls yes

2. 安装软件

为了避免污染linux工作环境，推荐在conda中创建各个流程的安装环境，比如：

conda create -n rna python=2 #创建名为rna的软件安装环境
conda info --envs #查看当前conda环境
source activate rna #激活conda的rna环境

先读文献调研，得到转录组分析需要用到的软件列表；

质控

fastqc , multiqc, trimmomatic, cutadapt ,trim-galore

比对

star, hisat2, bowtie2, tophat, bwa, subread

计数

htseq, bedtools, deeptools, salmon

如果你对一个软件不了解的话，那么安装之前在https://bioconda.github.io/recipes.html，检索该软件包是否存在，或者使用conda search packagename进行检索。

但是我帮你确定好了下面的软件安装代码是可行的！！！

conda install -y sra-tools
conda install -y trimmomatic
conda install -y cutadapt multiqc 
conda install -y trim-galore
conda install -y star hisat2 bowtie2
conda install -y subread tophat htseq bedtools deeptools
conda install -y salmon
source deactivate #注销当前的rna环境

转录组流程

step1: sra2fastq

下载SRA数据

新建一个名为SRR_Acc_List.txt的文档，将SRR号码保存在文档内，一个号码占据一行。文件可以在我的GitHub下载获取：https://github.com/jmzeng1314/GEO/blob/master/airway/SRR_Acc_List.txt

prefetch下载数据

wkd=/home/jmzeng/project/airway/ #设置工作目录
source activate rna
cat SRR_Acc_List.txt | while read id; do (prefetch  ${id} &);done
ps -ef | grep prefetch | awk '{print $2}' | while read id; do kill ${id}; done #在内地下载速度很慢，所以我杀掉这些下载进程

或者直接使用我已经下载好的sra数据

mkdir $wkd/raw 
cd $wkd/raw 
ls /public/project/RNA/airway/sra/*  | while read id; do ( nohup fastq-dump --gzip --split-3 -O ./ ${id} & ); done ## 批量转换sra到fq格式。
source deactivate

得到的SRA数据如下

/public/project/RNA/airway/sra/
├── [1.6G]  SRR1039508.sra
├── [1.4G]  SRR1039509.sra
├── [1.6G]  SRR1039510.sra
├── [1.5G]  SRR1039511.sra
├── [2.0G]  SRR1039512.sra
├── [2.2G]  SRR1039513.sra
├── [3.0G]  SRR1039514.sra
├── [1.9G]  SRR1039515.sra
├── [2.1G]  SRR1039516.sra
├── [2.6G]  SRR1039517.sra
├── [2.3G]  SRR1039518.sra
├── [2.0G]  SRR1039519.sra
├── [2.1G]  SRR1039520.sra
├── [2.4G]  SRR1039521.sra
├── [2.0G]  SRR1039522.sra
└── [2.2G]  SRR1039523.sra

sra格式转fastq格式

格式转还用到的软件是fastq-dump

for i in $wkd/*sra
do
        echo $i
        nohup fastq-dump --split-3 --skip-technical --clip --gzip $i & ## 批量转换
done

得到fastq数据如下

原始数据是双端测序结果，fastq-dump配合--split-3参数，一个样本被拆分成两个fastq文件

├── [1.3G]  SRR1039508_1.fastq.gz
├── [1.3G]  SRR1039508_2.fastq.gz
├── [1.2G]  SRR1039509_1.fastq.gz
├── [1.2G]  SRR1039509_2.fastq.gz
├── [1.3G]  SRR1039510_1.fastq.gz
├── [1.3G]  SRR1039510_2.fastq.gz
├── [1.2G]  SRR1039511_1.fastq.gz
├── [1.2G]  SRR1039511_2.fastq.gz
├── [1.6G]  SRR1039512_1.fastq.gz
├── [1.6G]  SRR1039512_2.fastq.gz
├── [950M]  SRR1039513_1.fastq.gz
├── [952M]  SRR1039513_2.fastq.gz
├── [2.4G]  SRR1039514_1.fastq.gz
......
├── [1.5G]  SRR1039522_1.fastq.gz
├── [1.5G]  SRR1039522_2.fastq.gz
├── [1.8G]  SRR1039523_1.fastq.gz
└── [1.8G]  SRR1039523_2.fastq.gz

step2: check quality of sequence reads

fastqc生成质控报告，multiqc将各个样本的质控报告整合为一个。

ls *gz | xargs fastqc -t 10
multiqc ./

得到结果如下

├── [4.0K]  multiqc_data
│   ├── [2.1M]  multiqc_data.json
│   ├── [6.8K]  multiqc_fastqc.txt
│   ├── [2.2K]  multiqc_general_stats.txt
│   ├── [ 16K]  multiqc.log
│   └── [3.4K]  multiqc_sources.txt
├── [1.5M]  multiqc_report.html
├── [236K]  SRR1039508_1_fastqc.html
├── [279K]  SRR1039508_1_fastqc.zip
├── [238K]  SRR1039508_2_fastqc.html
├── [286K]  SRR1039508_2_fastqc.zip
├── [236K]  SRR1039510_1_fastqc.html
├── [278K]  SRR1039510_1_fastqc.zip
├── [241K]  SRR1039510_2_fastqc.html
├── [292K]  SRR1039510_2_fastqc.zip
......
├── [220K]  SRR1039522_fastqc.zip
├── [234K]  SRR1039523_1_fastqc.html
├── [273K]  SRR1039523_1_fastqc.zip
├── [232K]  SRR1039523_2_fastqc.html
└── [274K]  SRR1039523_2_fastqc.zip

每个id_fastqc.html都是一个质量报告，multiqc_report.html是所有样本的整合报告

step3: filter the bad quality reads and remove adaptors.

运行如下代码，得到名为config的文件，包含两列数据

mkdir $wkd/clean 
cd $wkd/clean 
ls /home/jmzeng/project/airway/raw/*_1.fastq.gz >1
ls /home/jmzeng/project/airway/raw/*_2.fastq.gz >2
paste 1 2  > config

打开文件 qc.sh ，并且写入如下内容

trim_galore，用于去除低质量和接头数据

source activate rna
bin_trim_galore=trim_galore
dir='/home/jmzeng/project/airway/clean'
cat $1 |while read id
do
        arr=(${id})
        fq1=${arr[0]}
        fq2=${arr[1]} 
nohup $bin_trim_galore -q 25 --phred33 --length 36 --stringency 3 --paired -o $dir  $fq1 $fq2 & 
done 
source deactivate

运行qc.sh

bash qc.sh config #config是传递进去的参数

结果显示如下

├── [2.9K]  SRR1039508_1.fastq.gz_trimming_report.txt
├── [1.2G]  SRR1039508_1_val_1.fq.gz
├── [3.1K]  SRR1039508_2.fastq.gz_trimming_report.txt
├── [1.2G]  SRR1039508_2_val_2.fq.gz
├── [2.9K]  SRR1039509_1.fastq.gz_trimming_report.txt
......
├── [2.9K]  SRR1039522_1.fastq.gz_trimming_report.txt
├── [1.4G]  SRR1039522_1_val_1.fq.gz
├── [3.1K]  SRR1039522_2.fastq.gz_trimming_report.txt
├── [1.4G]  SRR1039522_2_val_2.fq.gz
├── [2.9K]  SRR1039523_1.fastq.gz_trimming_report.txt
├── [1.7G]  SRR1039523_1_val_1.fq.gz
├── [3.1K]  SRR1039523_2.fastq.gz_trimming_report.txt
└── [1.7G]  SRR1039523_2_val_2.fq.gz

step4: alignment

star, hisat2, bowtie2, tophat, bwa, subread都是可以用于比到的软件

先运行一个样本，测试一下

mkdir $wkd/test 
cd $wkd/test 
source activate rna
ls $wkd/clean/*gz |while read id;do (zcat ${id}|head -1000>  $(basename ${id} ".gz"));done
id=SRR1039508
hisat2 -p 10 -x /public/reference/index/hisat/hg38/genome -1 ${id}_1_val_1.fq   -2 ${id}_2_val_2.fq  -S ${id}.hisat.sam
subjunc -T 5  -i /public/reference/index/subread/hg38 -r ${id}_1_val_1.fq -R ${id}_2_val_2.fq -o ${id}.subjunc.sam  
bowtie2 -p 10 -x /public/reference/index/bowtie/hg38  -1 ${id}_1_val_1.fq   -2 ${id}_2_val_2.fq  -S ${id}.bowtie.sam
bwa mem -t 5 -M  /public/reference/index/bwa/hg38   ${id}_1_val_1.fq   ${id}_2_val_2.fq > ${id}.bwa.sam

批量比对代码

cd $wkd/clean 
ls *gz|cut -d"_" -f 1 |sort -u |while read id;do
ls -lh ${id}_1_val_1.fq.gz   ${id}_2_val_2.fq.gz 
hisat2 -p 10 -x /public/reference/index/hisat/hg38/genome -1 ${id}_1_val_1.fq.gz   -2 ${id}_2_val_2.fq.gz  -S ${id}.hisat.sam
subjunc -T 5  -i /public/reference/index/subread/hg38 -r ${id}_1_val_1.fq.gz -R ${id}_2_val_2.fq.gz -o ${id}.subjunc.sam
bowtie2 -p 10 -x /public/reference/index/bowtie/hg38  -1 ${id}_1_val_1.fq.gz   -2 ${id}_2_val_2.fq.gz  -S ${id}.bowtie.sam
bwa mem -t 5 -M  /public/reference/index/bwa/hg38   ${id}_1_val_1.fq.gz   ${id}_2_val_2.fq.gz > ${id}.bwa.sam
done

sam文件转bam

ls *.sam|while read id ;do (samtools sort -O bam -@ 5  -o $(basename ${id} ".sam").bam   ${id});done
rm *.sam

为bam文件建立索引

ls *.bam |xargs -i samtools index {}

reads的比对情况统计

ls *.bam |xargs -i samtools flagstat -@ 10  {}  >
ls *.bam |while read id ;do ( nohup samtools flagstat -@ 1 $id >  $(basename ${id} ".bam").flagstat & );done
source deactivate

最终结果显示如下

├── [1.8G]  SRR1039508.bowite2.bam
├── [2.9M]  SRR1039508.bowite2.bam.bai
├── [ 444]  SRR1039508.bowite2.flagstat
├── [ 10G]  SRR1039508.bowite2.sam
├── [1.7G]  SRR1039509.bowite2.bam
......
├── [2.0G]  SRR1039521.bowite2.bam
├── [2.9M]  SRR1039521.bowite2.bam.bai
├── [ 444]  SRR1039521.bowite2.flagstat
├── [ 10G]  SRR1039521.bowite2.sam
├── [2.3G]  SRR1039522.bowite2.bam
├── [3.0M]  SRR1039522.bowite2.bam.bai
├── [ 444]  SRR1039522.bowite2.flagstat
├── [ 12G]  SRR1039522.bowite2.sam
├── [2.5G]  SRR1039523.bowite2.bam
├── [3.0M]  SRR1039523.bowite2.bam.bai
├── [ 444]  SRR1039523.bowite2.flagstat
└── [ 14G]  SRR1039523.bowite2.sam

step5: counts

mkdir $wkd/align 
cd $wkd/align 
source activate rna
for fn in {508..523}
do
featureCounts -T 5 -p -t exon -g gene_id  -a /public/reference/gtf/gencode/gencode.v25.annotation.gtf.gz -o counts.txt SRR1039$fn.bam
done
source deactivate

得到的文件如下

      1 # Program:featureCounts v1.6.1; Command:"featureCounts" "-T" "5" "-p" "-t" "exon" "-g" "gene_id" "-a" "/public/reference/gtf/gencode/ge
      2 Geneid  Chr     Start   End     Strand  Length  /home/llwu/RNA/airway/2.align/bowite2/SRR1039523.bowite2.bam
      3 ENSG00000223972.5       chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1    11869;12010;12179;12613;12613;12975;13221;13221;13453   12227;1
      4 ENSG00000227232.5       chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1;chr1  14404;15005;15796;16607;16858;17233;17606;17915;18268;2
      5 ENSG00000278267.1       chr1    17369   17436   -       68      9
      6 ENSG00000243485.4       chr1;chr1;chr1;chr1;chr1;chr1   29554;30267;30366;30564;30976;30976     30039;30667;30503;30667;31097;31109
      7 ENSG00000237613.2       chr1;chr1;chr1;chr1;chr1        34554;35245;35277;35721;35721   35174;35481;35481;36073;36081   -;-;-;-;-
      8 ENSG00000268020.3       chr1    52473   53312   +       840     0
      9 ENSG00000240361.1       chr1    62948   63887   +       940     0
     10 ENSG00000186092.4       chr1    69091   70008   +       918     0

step5: DEG

差异分析之前需要首先对转录组上游分析得到的文件 all.id.txt 进行一定程度的检查，代码如：

rm(list = ls())
options(stringsAsFactors = F)
a=read.table('all.id.txt',header = T)
tmp=a[1:14,1:7]
meta=a[,1:6]
exprSet=a[,7:ncol(a)]
colnames(exprSet)
a2=exprSet[,'SRR1039516.hisat.bam']

library(airway)
data(airway)
exprSet=assay(airway)
colnames(exprSet)
a1=exprSet[,'SRR1039516']
group_list=colData(airway)[,3]

a2=data.frame(id=meta[,1],a2=a2)
a1=data.frame(id=names(a1),a1=as.numeric(a1))
library(stringr)
a2$id <- str_split(a2$id,'\\.',simplify = T)[,1]
tmp=merge(a1,a2,by='id')
png('tmp.png')
plot(tmp[,2:3])
dev.off()

library(corrplot)
png('cor.png')
corrplot(cor(log2(exprSet+1)))
dev.off()

library(pheatmap)
png('heatmap.png')
m=cor(log2(exprSet+1))
pheatmap(scale(cor(log2(exprSet+1))))
dev.off()

检查通过之前就可以正式做差异分析以及后续的富集分析，代码参考：https://github.com/jmzeng1314/my-R/blob/master/10-RNA-seq-3-groups/hisat2_mm10_htseq.R
更多干货直接扫描看视频吧，还等什么！！！

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