用R包来下载sra数据

1）介绍

我们用SRAdb library来对SRA数据进行处理。 SRAdb 可以更方便更快的接入 metadata associated with submission, 包括study, sample, experiment, and run. SRAdb 包通过 NCBI SRA数据库中的metadata信息作用. 首先dbConnect ()接入 R system 中的local database systems, 所有的搜索就在本地文件的基础上进行。
the queries we tried with the dbGetQuery function are passed in the form of SQL queries, which is a Select From Where framework. This part actually requires the
RSQLite package, which is installed when installing the SRAdb package, as a dependency. The getSRA function can actually do a full text search in the SRA data again via RSQLite and fetch the data in the selected fields for the query.

2）下载

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

biocLite("SRAdb")

library(SRAdb)

3）了解SRA database

#sqlFile <- getSRAdbFile()  #在线获取，太大了，不要这样做。
sraCon <- dbConnect(SQLite(), 'SRAmetadb.sqlite')  #于是我下载了这个文件，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

4）从SRA database下载数据

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

5)下载完fq文件后，用R进行读取

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，要至少8G内存

readLines("SRR000648.fastq", 4)    # first four lines of the file

6)下载并读取比对数据(bam)

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

7）对原始raw NGS data 的预处理

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")  #画箱型图