Bulk RNAseq上游比对1：大致流程与conda环境 - 简书 (jianshu.com)
Bulk RNAseq上游比对2：下载数据、质控 - 简书 (jianshu.com)
Bulk RNAseq上游比对3：比对mapping - 简书 (jianshu.com)

Step1：下载数据

1.1 下载公共数据框的测序数据

• 示例数据：GSE158623 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158623
• SRR：https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA665998，可以下载.sra文件[不推荐]

• EBI：https://www.ebi.ac.uk/ena/browser/view/SRR12720999，提供fastq.gz的aspera(推荐)、ftp下载链接
  

cat > SraAccList.txt
SRR12720999
SRR12721000
SRR12721001
SRR12721002
SRR12721003
SRR12721004

conda activate download

方式1：aspera

# 批量生成下载链接
# era-fasp@fasp.sra.ebi.ac.uk:/vol1/fastq/SRR166/009/SRR1663609/SRR1663609_1.fastq.gz
touch ascp.link
cat SraAccList.txt | while read id 
do
echo "era-fasp@fasp.sra.ebi.ac.uk:/vol1/fastq/${id:0:6}/0${id:0-2}/${id}/${id}_1.fastq.gz" >> ascp.link
echo "era-fasp@fasp.sra.ebi.ac.uk:/vol1/fastq/${id:0:6}/0${id:0-2}/${id}/${id}_2.fastq.gz" >> ascp.link
done

#ascp高速下载
cat ascp.link |while read sample
do
ascp -QT -l 300m -P33001  \
-i ~/miniconda3/envs/download/etc/asperaweb_id_dsa.openssh   \
$sample  .
done

方式2：wget ftp

# 批量生成下载链接
# ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR127/099/SRR12720999/SRR12720999_1.fastq.gz
touch ftp.link
cat SraAccList.txt | while read id 
do
echo "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/${id:0:6}/0${id:0-2}/${id}/${id}_1.fastq.gz" >> ftp.link
echo "ftp://ftp.sra.ebi.ac.uk/vol1/fastq/${id:0:6}/0${id:0-2}/${id}/${id}_2.fastq.gz" >> ftp.link
done

cat ftp.link | while read id 
do
wget -c $id
done

方式3：prefetch下载sra文件，再转为fastq.gz

#单独下载
prefetch SRR12720999
#批量下载 SRR list
prefetch --option-file SraAccList.txt

# sra2fastq
cat SraAccList.txt | while read id
do
echo ${id}
fasterq-dump --split-files $id
done
#fastq2fastq.gz
gzip *fastq

• 然后查看下载得到的fastq.gz质量如何

#### fastqc
fastqc $(ls ${pare_dir}/raw/ebi/*gz) -o ${pare_dir}/raw/ebi/ -t 10
####QC merged report
multiqc ./ -n rawfq_multiqc_report.html

1.2 下载参考基因组及相关数据

refgenie init -c ~/refgenie/genome_config.yaml
refgenie listr -c ~/refgenie/genome_config.yaml
refgenie listr -g hg38 -c ~/refgenie/genome_config.yaml

#参考基因组
refgenie pull hg38/fasta -c ~/refgenie/genome_config.yaml
refgenie pull hg38_cdna/fasta -c ~/refgenie/genome_config.yaml

#参考注释信息
refgenie pull hg38/gencode_gtf -c ~/refgenie/genome_config.yaml

#比对软件的索引文件
refgenie pull hg38/bowtie2_index -c ~/refgenie/genome_config.yaml
refgenie pull hg38/bwa_index -c ~/refgenie/genome_config.yaml
refgenie pull hg38/star_index -c ~/refgenie/genome_config.yaml
refgenie pull hg38/hisat2_index -c ~/refgenie/genome_config.yaml
refgenie pull hg38_cdna/salmon_index -c ~/refgenie/genome_config.yaml

#列出本地已经下载的数据
refgenie list -c ~/refgenie/genome_config.yaml

Step2：质控

• trim_glaore

#以其中一个作为示例
pare_dir=/home/data/****/mapping
fq1=${pare_dir}/raw/ebi/SRR12720999_1.fastq.gz
fq2=${pare_dir}/raw/ebi/SRR12720999_2.fastq.gz

trim_galore -j 8 -q 25 --phred33 --length 36 \
-paired -o ${pare_dir}/trim \
$fq1 $fq2

#批量
cat ${pare_dir}/SraAccList.txt | while read id
do 
echo $id
trim_galore -j 8 -q 25 --phred33 --length 36 \
-paired -o ${pare_dir}/trim \
${pare_dir}/raw/ebi/${id}_1.fastq.gz \
${pare_dir}/raw/ebi/${id}_2.fastq.gz
done

• 查看质控之后的fatsq.gz质量

#### fastqc
fastqc $(ls ${pare_dir}/trim/*gz) -o ${pare_dir}/trim/ -t 10
####QC report
multiqc ./ -n trim_multiqc_report.html