用R包来下载sra数据

linux从零开始

1）下载

[Python] 纯文本查看 复制代码

source("http://bioconductor.org/biocLite.R")
biocLite("SRAdb")
library(SRAdb)

2）了解SRA database

[Python] 纯文本查看 复制代码

#sqlFile <- getSRAdbFile()  #在线获取，太大了，不要这样做。
sraCon <- dbConnect(SQLite(), 'SRAmetadb.sqlite')  #我下载了这个文件，压缩文件2个G（解压后36个G），然后读取了这个文件，相当于下载nr库到本地。
sraTables <- dbListTables(sraCon)      # investigate the content of the database
dbListFields(sraCon,"study")
#########关键词keyword： embryo
myHit <- dbGetQuery(sraCon, paste("select study_accession,study_title from study where","study_description like'%embryo'",sep=" ")) #
myHit <- getSRA( search_terms = "brain", out_types = c('run','study'), sraCon)  #free text收索
myHit <- getSRA( search_terms ='Alzheimers OR "EPILEPSY"', out_types = c('sample'), sraCon)  #逻辑收索

3）从SRA database下载数据

[Python] 纯文本查看 复制代码

myHit <- getSRA( search_terms ='ALZHEIMERS OR "EPILEPSY"', out_types = c('sample'), sraCon)   #关键词收索
conversion <- sraConvert( c('ERS354366','SRS266589'), sra_con = sraCon)          #选择其中的2个，查看信息
conversion
rs <- getSRAinfo( c("SRX100465"), sraCon, sraType = "sra")  #选择其中一个看相应的信息，会显示出ftp地址
getSRAfile( c("SRR351672", "SRR351673"), sraCon, fileType='fastq')  ##下载感兴趣的run

4)下载完fq文件后，用R进行读取

[Python] 纯文本查看 复制代码

install.packages("R.utils")
library(R.utils)  #下载数据用
download.file(url="ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/fastq/SRA000/SRA000241/SRX000122/SRR000648.fastq.bz2", destfile = "SRR000648.fastq.bz2")
bunzip2(list.files(pattern = ".fastq.bz2$")) #解压
biocLite("ShortRead")             
library(ShortRead)               #读取fq文件
MyFastq <- readFastq(getwd(), pattern=".fastq")  #小心运行
readLines("SRR000648.fastq", 4)    # first four lines of the file

5)下载并读取比对数据(bam)

[Python] 纯文本查看 复制代码

download.file(url="http://genome.ucsc.edu/goldenPath/help/examples/bamExample.bam", destfile = "bamExample.bam")
library(Rsamtools)
bam <- scanBam("bamExample.bam")    #读取bam
names(bam[[1]])                  #查看bam的信息
countBam("bamExample.bam")      #统计bam信息
 
what <- c("rname", "strand", "pos", "qwidth", "seq")  #只读取其中的几列
param <- ScanBamParam(what=what)        
bam2 <- scanBam("bamExample.bam", param=param) 
names(bam2[[1]])
bam_df <- do.call("DataFrame", bam[[1]])  # Read the data as a DataFrame object
head(bam_df)
table(bam_df$rname == '21' & bam_df$flag == 16) #提取符合指定要求的sequences，即flag=16为reverse strands

6）对原始raw NGS data 的预处理

[Python] 纯文本查看 复制代码

prefetch SRR000648
prefetch SRR000657
fastq-dump  --split-3 -O ./ SRR000657
fastq-dump --split-3 -O ./ SRR000648
library(ShortRead)
myFiles <- list.files(getwd(), "fastq", full=TRUE)
myFQ <- lapply(myFiles, readFastq)
myQual <- FastqQuality(quality(quality(myFQ[[1]])))   #读取质量
readM <- as(myQual, "matrix")      #将质控转化为矩阵
boxplot(data.frame(readM), outline = FALSE, main="Per Cycle Read Quality", xlab="Cycle", ylab="Phred Quality")  #画箱型

11