1）介紹

我們用SRAdb library來對SRA數據進行處理。 SRAdb 可以更方便更快的接入 metadata associated with submission, 包括study, sample, experiment, and run. SRAdb 包通過 NCBI SRA數據庫中的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礎上進行。
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）下載

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

3）了解SRA database

#sqlFile <- getSRAdbFile()  #在線獲取，太大了，不要這樣做。
sraCon <- dbConnect(SQLite(), 'SRAmetadb.sqlite')  #于是我下載了這個文件，壓縮文件2個G（解壓后36個G），然后讀取了這個文件，相當于下載nr庫到本地。
sraTables <- dbListTables(sraCon)      # investigate the content of the database
dbListFields(sraCon,"study")
#########關鍵詞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)  #邏輯收索

4）從SRA database下載數據

myHit <- getSRA( search_terms ='ALZHEIMERS OR "EPILEPSY"', out_types = c('sample'), sraCon)   #關鍵詞收索
conversion <- sraConvert( c('ERS354366','SRS266589'), sra_con = sraCon)          #選擇其中的2個，查看信息
conversion
rs <- getSRAinfo( c("SRX100465"), sraCon, sraType = "sra")  #選擇其中一個看相應的信息，會顯示出ftp地址
getSRAfile( c("SRR351672", "SRR351673"), sraCon, fileType='fastq')  ##下載感興趣的run

5)下載完fq文件后，用R進行讀取

install.packages("R.utils")
library(R.utils)  #下載數據用
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$")) #解壓
biocLite("ShortRead")             
library(ShortRead)               #讀取fq文件
MyFastq <- readFastq(getwd(), pattern=".fastq")  #小心運行，要至少8G內存
readLines("SRR000648.fastq", 4)    # first four lines of the file

6)下載并讀取比對數據(bam)

download.file(url="http://genome.ucsc.edu/goldenPath/help/examples/bamExample.bam", destfile = "bamExample.bam")
library(Rsamtools)
bam <- scanBam("bamExample.bam")    #讀取bam
names(bam[[1]])                  #查看bam的信息
countBam("bamExample.bam")      #統計bam信息

what <- c("rname", "strand", "pos", "qwidth", "seq")  #只讀取其中的幾列
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為reverse strands

7）對原始raw NGS data 的預處理

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]])))   #讀取質量
readM <- as(myQual, "matrix")      #將質控轉化為矩陣
boxplot(data.frame(readM), outline = FALSE, main="Per Cycle Read Quality", xlab="Cycle", ylab="Phred Quality")  #畫箱型圖

總結

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