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
代码语言：javascript复制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 yes2. 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：
代码语言：javascript复制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！！！
代码语言：javascript复制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/SRRAccList.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- 拷贝事先下载好的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
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
代码语言：javascript复制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 &
done- 得到fastq数据如下
代码语言：javascript复制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.gzstep2: check quality of sequence reads
代码语言：javascript复制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idfastqc.html都是一个质量报告，multiqcreport.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
代码语言：javascript复制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.gzstep4: 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
cd $wkd/test
source activate rna
#比对流程
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.samstep5: counts
代码语言：javascript复制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 0step5: 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/hisat2mm10htseq.R 其实都在GitHub哦，视频讲解了这个代码如何使用。
- 更多干货直接扫描看视频吧，还等什么！！！
