miRNA

本章会介绍一下miRNA的整体流程(待填坑)

需要Linux操作系统和R语言

miRNA-Human-PE

#!/bin/bash

#把sra格式转化为fastq
raw_data='SRR*'
for each in ${raw_data}
do ~/biosoft/sratoolkit.2.6.3-centos_linux64/bin/fastq-dump --split-3 $each -O ./
done

#进行第一次fastqc,输出到本目录下firstQC
fastq='*.fastq'
for data in ${fastq}
do  ~/biosoft/FastQC/fastqc -o ./firstQC -t 2 $data
done

#用Trimmomatic进行去接头和低质量过滤,-phred33根据实际情况更改，一般为33
fortrimmer1='*1.fastq'
fortrimmer2='*2.fastq'
for data1 in ${fortrimmer1} & for data2 in ${fortrimmer2}
do java -jar ~/biosoft/Trimmomatic-0.39/trimmomatic-0.39.jar PE -threads 2 -phred33 $data1 $data2 $data1.paired.clean.fastq $data1.unpaired.clean.fastq $data2.paired.clean.fastq $data2.unpaired.clean.fastq ILLUMINACLIP:/home/yannis/biosoft/Trimmomatic-0.39/adapters/TruSeq3-SE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:2:25 MINLEN:16
done

#进行第二次质控，输出到本目录下的secondQC
second_clean='*.clean.fastq'
for clean_data in ${second_clean}
do ~/biosoft/FastQC/fastqc -o ./secondQC -t 2 $clean_data
done

#比对,比对的接头序列按需要修改，与去接头质量过滤一致,与全基因组进行的比对
clean='*.clean.fastq'
for clean_data in ${clean}
do mapper.pl $clean_data -e -h -i -j -m -k AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC -l 16 -p ~/reference/index/mature.human -s $clean_data.fa -t $clean_data.arf -o 1 -n
done

#定量,最后一个参数根据物种修改
mapresult='*.fa'
for result in ${mapresult}
do quantifier.pl -p ~/reference/hairpin.human.fa -m ~/reference/mature.human.fa -r $result -t hsa
done