Package 'SRAdb'

Title: A compilation of metadata from NCBI SRA and tools
Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.
Authors: Jack Zhu and Sean Davis
Maintainer: Jack Zhu <[email protected]>
License: Artistic-2.0
Version: 1.67.0
Built: 2024-07-03 06:03:07 UTC
Source: https://github.com/bioc/SRAdb

Help Index

Query NCBI SRA metadata within R or from a local SQLite database

Description

The Sequence Read Archive (SRA) represents largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. SRAdb is simply a thin wrapper around the SQLite database along with associated tools and documentation. Fulltext search in the package make querying metadata very flexible and powerful. SRA data files (sra or sra-lite) can be downloaded for doing alignment locally. Available BAM files in local or in the Meltzerlab sraDB can be loaded into IGV for visualization easily. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.

Details

Package: SRAdb
Type: Package
Date of creation: 2012-02-13
License: What license is it under?
LazyLoad: yes

Author(s)

Jack Zhu and Sean Davis

Maintainer: Jack Zhu <[email protected]>

References

https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Examples

## Using the SRAmetadb demo database
  
    library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
    sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
  
    ## Get column descriptions
    a <- colDescriptions(sra_con=sra_con)[1:5,]
  
    ## Convert SRA experiment accessions to other types
    b <- sraConvert( in_acc=c(" SRR000137", "SRR000138 "), out_type=c('sample'), sra_con=sra_con )
  
    ## Fulltext search SRA meta data using SQLite fts3 module
    rs <- getSRA (search_terms ='breas* NEAR/2 can*', out_types=c('run','study'), sra_con=sra_con)
    rs <- getSRA (search_terms ='breast', out_types=c('run','study'), sra_con=sra_con)
    rs <- getSRA (search_terms ='"breas* can*"', out_types=c('study'), sra_con=sra_con) 
    rs <- getSRA (search_terms ='MCF7 OR "MCF-7"', out_types=c('sample'), sra_con=sra_con) 
    rs <- getSRA (search_terms ='study_title: brea* can*', out_types=c('run','study'), sra_con=sra_con)  
    rs <- getSRA (search_terms ='study_title: brea* can*', out_types=c('run','study'), sra_con=sra_con, acc_only=TRUE) 

    ## List fastq file ftp or fasp addresses associated with "SRX000122"
    listSRAfile (in_acc = c("SRX000122"), sra_con = sra_con, fileType = 'sra')
    listSRAfile (in_acc = c("SRX000122"), sra_con = sra_con, fileType = 'sra', srcType='fasp')
  
    ## Get file size and date from NCBI ftp site for available fastq files associated with "SRS012041","SRS000290" 
    ## Not run:   
    getSRAinfo (in_acc=c("SRS012041","SRS000290"), sra_con=sra_con, sraType='sra')
    
## End(Not run)

    ## Download sra files from NCBI SRA using ftp protocol:
    ## Not run: 
    getSRAfile( in_acc = c("SRR000648","SRR000657"), sra_con = sra_con, destDir = getwd(), fileType = 'sra' )
    ## Download fastq files from EBI using ftp protocol:
    getSRAfile( in_acc, sra_con, destDir = getwd(), fileType = 'fastq', srcType = 'ftp', makeDirectory = FALSE, method = 'curl', ascpCMD = NULL )
    
## End(Not run)
    
    ## Download fastq files from EBI  ftp siteusing fasp protocol:
    ## Not run: 
    ascpCMD <-  'ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty'
    getSRAfile( in_acc, sra_con,  fileType = 'fastq', srcType = 'fasp',  ascpCMD = ascpCMD )
## End(Not run)
   
    ## Start IGV from R if no IGV running
    ## Not run: startIGV(memory='mm')

    ## load BAM files to IGV
    ## Not run: 
    exampleBams = file.path(system.file('extdata',package='SRAdb'), dir(system.file('extdata',package='SRAdb'),pattern='bam$'))
    sock <- IGVsocket()
    IGVload(sock,exampleBams)
    
## End(Not run)
    ## Change the IGV genome
    ## Not run: 
    IGVgenome(sock,genome='hg18')
    
## End(Not run)
    ## Go to a specified region in IGV
    ## Not run: 
    IGVgoto(sock,'chr1:1-10000')
    IGVgoto(sock,'TP53')
    
## End(Not run)

    ## Make a snapshot of the current IGV window
    ## Not run:   
    IGVsnapshot(sock)
    dir()
    
## End(Not run)
  
    ## create a graphNEL object from SRA accessions, which are full text search results of terms 'primary thyroid cell line'
    g <- sraGraph('MCF7 OR "MCF-7"', sra_con)
  
    ## Not run: 
    library(Rgraphviz)
    attrs <- getDefaultAttrs(list(node=list(fillcolor='lightblue', shape='ellipse')))
    plot(g, attrs=attrs)
    
## End(Not run)
    dbDisconnect(sra_con) 

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth.  Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Fasp file downloading using the ascp command line program

Description

This function downloads files by fasp protocol using Aspera's ascp command line program, which is include in Aspera Connect software (http://www.asperasoft.com/).

Usage

ascpR( ascpCMD, ascpSource, destDir = getwd() )

Arguments

ascpCMD

ascp main commands, which should be constructed by a user according to the actual installation of Aspera Connect in the system, with proper options to be used. Example commands: "ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty" (Linux) or "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'" (Mac OS X). More about ascp please see the help ('ascp -h' in a shell).

ascpSource

character vector of fasp file sources for the ascp command, e.g. [email protected]:vol1/fastq/SRR000/SRR000648/SRR000648.fastq.gz (EBI), [email protected]:/sra/sra-instant/reads/ByExp/sra/SRX/SRX000/SRX000122/SRR000657/SRR000657.sra (NCBI).

destDir

destination directory to save downloaded files.

Details

The function takes advatage of Aspera's fasp transport technology (http://www.asperasoft.com/), which provides high-speed transfering large files over the Internet. Due to complexity with options with ascp and installation difference between different systems, this funciton asks users to supply main ascp comands. Users who are not familiar with ascp command line program should have IT support personnel to install the software and constrct main ascp comands.

Value

A data.frame containing all matched SRA accessions and ftp or fasp addresses.

Author(s)

Jack Zhu <[email protected]>

References

http://www.asperasoft.com/

See Also

ascpSRA, getSRAfile, getFASTQinfo, getSRAinfo

Examples

## Using the SRAmetadb demo database
## Not run: 
	library( SRAdb )
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
	rs <- getFASTQinfo (in_acc=c("SRR000648","SRR000657"), sra_con, srcType='fasp')
	
	ascpSource <- rs$fasp
	ascpCMD <- 'ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty'
	## common ascpCMD in Mac OS X:
	#ascpCMD = "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'"
	
	ascpR( ascpCMD, ascpSource, destDir = getwd() )
	dbDisconnect( sra_con )
    
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Fasp SRA data file downloading using the ascp command line program

Description

This function downloads SRA data files (fastq, sra ) by fasp protocol using Aspera's ascp command line program, which is included in Aspera Connect software (http://www.asperasoft.com/).

Usage

ascpSRA ( in_acc, sra_con, ascpCMD, fileType = 'sra', destDir = getwd() )

Arguments

in_acc

character vector of SRA accessions, which should be in same SRA data type, either submission, study, sample, experiment or run.

sra_con

connection to the SRAmetadb SQLite database.

ascpCMD

ascp main commands, which should be constructed by a user according to the actual installation of Aspera Connect in the system, with proper options to be used. Example commands: "ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty" (Linux) or "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'" (Mac OS X). More about ascp please see the help ('ascp -h' in a shell).

fileType

type of SRA data files, which should be "sra", or "fastq" ('litesra' has phased out ).

destDir

destination directory to save downloaded files.

Details

This function will get fasp file sources first using funciton listSRAfile and then download data files using function ascpR.

Value

A data.frame of all matched SRA accessions and ftp or fasp file addresses.

Author(s)

Jack Zhu <[email protected]>

References

http://www.asperasoft.com/

See Also

ascpR, listSRAfile, getSRAfile, getFASTQinfo, getSRAinfo

Examples

## Using the SRAmetadb demo database
	## Not run: 
	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
	in_acc <- c("SRR000648","SRR000657")
	ascpCMD <- 'ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty'
	## common ascpCMD for a system with Mac OS X:
	#ascpCMD <- "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'"
	
	sraFiles <- ascpSRA( in_acc, sra_con, ascpCMD, fileType = 'sra', destDir=getwd() )
	dbDisconnect(sra_con)
	
## End(Not run)
## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Get column descriptions of SRAmetadb.sqlite

Description

Get column descriptions of SRAmetadb.sqlite, including table, field, field data type, description and default values

Usage

colDescriptions( sra_con )

Arguments

sra_con

Connection of the SRAmetadb SQLite database

Value

A seven-column data.frame including table_name, field_name, type, description, value_list.

Author(s)

Jack Zhu<[email protected]> and Sean Davis <[email protected]>

Examples

## Using the SRAmetadb demo database
  
    library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
    sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
  
    ## Get column descriptions
    a <- colDescriptions(sra_con=sra_con)[1:5,]

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Create a new graphNEL object from an input entity matrix or data.frame

Description

This function will create a new graphNEL object from an input entity matrix or data.frame

Usage

entityGraph(df)

Arguments

df

A matrix or data.frame

Details

A graphNEL object with edgemode='directed' is created from input data.frame and the plot function will draw a graph

Value

A graphNEL object with edgemode='directed'

Author(s)

Jack Zhu <[email protected]> and Sean Davis <[email protected]>

See Also

getSRA, sraConvert, sraGraph

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)

	## create a graphNEL object from SRA accessions, which are full text search results of terms 'primary thyroid cell line'
  	acc <- getSRA (search_terms ='MCF7 OR "MCF-7"', out_types=c('sra'), sra_con=sra_con, acc_only=TRUE) 
  	g <- entityGraph(acc)
  	## Not run: 
  	library(Rgraphviz)
  	attrs <- getDefaultAttrs(list(node=list(fillcolor='lightblue', shape='ellipse')))
  	plot(g, attrs= attrs)
  	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Download SRA fastq files from EBI ENA through ftp or fasp

Description

This function downloads SRA fastq data files through ftp or fasp from EBI ENA site for a given list of SRA accessions.

Usage

getFASTQfile( in_acc, sra_con, destDir = getwd(), srcType = 'ftp', makeDirectory = FALSE, method = 'curl', ascpCMD = NULL)

Arguments

in_acc

character vector of SRA accessions that could be be in one or more SRA sata types: study, sample, experiment and/or run.

sra_con

connection to the SRAmetadb SQLite database

destDir

destination directory to save downloaded fastq files

srcType

type of transfer protocol, which should be "ftp" or "fasp".

makeDirectory

logical, TRUE or FALSE. If TRUE and baseDir does not exists, storedir will be created to save downloaded files, otherwise downloaded fastq files will be saved to current directory.

method

character vector of length 1, passed to the identically named argument of download.file.

ascpCMD

ascp main commands, which should be constructed by a user according to the actual installation of Aspera Connect in the system, with proper options to be used. Example commands: "ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty" (Linux) or "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'" (Mac OS X). More about ascp please see the help ('ascp -h' in a shell).

Details

The function first gets ftp/fasp addresses of SRA fastq files using funcitn getFASTQinfo for a given list of input SRA accessions; then downloads the fastq files through ftp or fasp.

Warning

Downloading SRA fastq files through ftp over long distance could take long time and should consider using using 'fasp'.

Author(s)

Jack Zhu <[email protected]>

See Also

getFASTQinfo, getSRAfile, ascpR

Examples

## Using the SRAmetadb demo database

	## Not run: 
	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect( dbDriver("SQLite"), sra_dbname )
	
	## Download fastq files from EBI ENA  through ftp
	getFASTQfile( in_acc = c("SRR000648","SRR000657"), sra_con, destDir = getwd(), srcType = 'ftp', ascpCMD = NULL )
		
	## Download fastq files from EBI ENA  through fasp
	ascpCMD <- 'ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty'
	## common ascpCMD for a system with Mac OS X:
	#ascpCMD <- "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'"	
	getFASTQfile( in_acc = c("SRR000648","SRR000657"), sra_con, srcType='fasp', ascpCMD=ascpCMD )
	
	dbDisconnect( sra_con )
	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Get SRA fastq file information and associated meta data from EBI ENA

Description

This function gets SRA fastq file information and essential associated meta data from EBI ENA web site ( http://www.ebi.ac.uk/ena/data/view/reports/sra/fastq_files/ ) for SRA accessions given.

Usage

getFASTQinfo( in_acc, sra_con, srcType = 'ftp' )

Arguments

in_acc

character vector of SRA accessions that could be be in one or more SRA sata types: study, sample, experiment and/or run.

sra_con

Connection to the SRAmetadb SQLite database

srcType

option for listing either 'ftp' or 'fasp' addresses. The default is 'ftp'.

Details

EBI ENA web site ( http://www.ebi.ac.uk/ena/data/view/reports/sra/fastq_files/ ) is the souce for parsing infromation from, which is updated and verified daily. Ftp or fasp addresses got from this funciton can be used in either getFASTQfile or getSRAfile to download the files.

Value

A data.frame of ftp/fasp inftomation ( addresses, file size, read number, etc) and associated meta data ( study, sample, experiment, run, organism, instrument.platform, instrument.model, library.name, library.layout, library.source, library.selection, run.read.count, run.base.count, etc. ).

Author(s)

Jack Zhu <[email protected]>

See Also

getFASTQfile, listSRAfile, getSRAfile

Examples

## Using the SRAmetadb demo database
	## Not run: 
	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)	
	getFASTQinfo( in_acc = c("SRR000648","SRR000657"), sra_con, srcType = 'ftp' )
	getFASTQinfo( in_acc = c("SRR000648","SRR000657"), sra_con, srcType = 'fasp' )
	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Fulltext search SRA meta data using SQLite fts3 module

Description

This function does Fulltext search on any SRA fields in any SRA data types with Fulltext capacity in the SQLite and returns SRA records

Usage

getSRA(search_terms, out_types=c('sra','submission','study','experiment','sample','run'), sra_con, acc_only=FALSE)

Arguments

search_terms

Free text search terms constructed according to SQLite query syntax defined here: http://www.sqlite.org/fts3.html#section_1_3

out_types

Character vector of the following SRA data types: 'sra','submission','study','sample','experiment','run'. Note: if 'sra' is within out_types, the out_types will be set to c('submission','study','sample','experiment').

sra_con

Connection to the SRAmetadb SQLite database

acc_only

logical, if TRUE, the function will return SRA accession for each out_types

Details

Queries performed by this function could be Phrase queries, e.g. '"lin* app*"', or NEAR queries, e.g. '"ACID compliant" NEAR/2 sqlite', or with the Enhanced Query Syntax. Check Full Text Search section on the SQLite site for details. if 'acc_only=TRUE', a data.frame containing only SRA accessions will be returned, which can be used as input for sraGraph.

Value

A data.frame containing all returned SRA records with fields defined by out_types.

If acc_only=FALSE, a data.frame of matched accessions of out_types will be returned.

Author(s)

Jack Zhu <[email protected]>

References

http://www.sqlite.org/

See Also

sraConvert

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
  
	## Fulltext search SRA meta data using SQLite fts3 module:
	# find all records with words of 'breast' and 'cancer' in a filed and there could be one to many words between 'breast' and 'cancer':   
	rs <- getSRA (search_terms ='breast cancer', out_types=c('run','study'), sra_con=sra_con)
 
	# find all records with exact phrase of 'breast cancer' in a filed:   
	rs <- getSRA (search_terms ='"breast cancer"', out_types=c('run','study'), sra_con=sra_con)
 
	# find records with words beginning with 'braes' and 'can', and the distance between them is equal or less than two words:
	rs <- getSRA (search_terms ='breas* NEAR/2 can*', out_types=c('run','study'), sra_con=sra_con)

	# the same as above except that only one space between the two words  
	rs <- getSRA (search_terms ='"breas* can*"', out_types=c('study'), sra_con=sra_con) 
 
	# find records with 'MCF7' or 'MCF-7' - adding double quote to avoid the SQLite to break down 'MCF-7' to 'MCF' and '7':
	rs <- getSRA (search_terms ='MCF7 OR "MCF-7"', out_types=c('sample'), sra_con=sra_con) 

	# the same as above, but only search the field of 'study_title':
	rs <- getSRA (search_terms ='study_title: brea* can*', out_types=c('run','study'), sra_con=sra_con)  

	# the same as above, but only search the field of 'study_title' and return only accessions:
	rs <- getSRA (search_terms ='study_title: brea* can*', out_types=c('run','study'), sra_con=sra_con, acc_only=TRUE) 

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Download and unzip last version of SRAmetadb.sqlite.gz from the server

Description

This function is the standard method for downloading and unzipping the most recent SRAmetadb SQLite file from the server.

Usage

getSRAdbFile(destdir = getwd(), destfile = "SRAmetadb.sqlite.gz",
             method)

Arguments

destdir

The destination directory of the downloaded file
destfile

The filename of the downloaded file. This filename should end in ".gz" as the unzipping assumes that is the case
method

Character vector of length 1, passed to the identically named argument of download.file.

Value

Prints some diagnostic information to the screen.

Returns the local filename for use later.

Author(s)

Jack Zhu <[email protected]>, Sean Davis <[email protected]>

Examples

## the SRAmetadb demo database can be used to test
sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	

## Not run: geometadbfile <- getSRAdbFile()
## Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Download SRA data file through ftp or fasp

Description

This function downloads sra data files associated with input SRA accessions from NCBI SRA or downloads fastq files from EBI ENA through ftp or fasp protocol.

Usage

getSRAfile( in_acc, sra_con, destDir = getwd(), fileType = 'sra', srcType = 'ftp', makeDirectory = FALSE, method = 'curl', ascpCMD = NULL )

Arguments

in_acc

character vector of SRA accessions, which should be in same SRA data type, either submission, study, sample, experiment or run.

sra_con

Connection to the SRAmetadb SQLite database

destDir

destination directory to save downloaded files.

fileType

type of SRA data files, which should be "sra", or "fastq" ('litesra' has phased out ).

srcType

type of transfer protocol, which should be "ftp" or "fasp".

makeDirectory

logical, TRUE or FALSE. If TRUE and baseDir does not exists, storedir will be created to save downloaded files, otherwise downloaded fastq files will be saved to current directory.

method

Character vector of length 1, passed to the identically named argument of download.file.

ascpCMD

ascp main commands, which should be constructed by a user according to the actual installation of Aspera Connect in the system, with proper options to be used. Example commands: "ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty" (Linux) or "'/Applications/Aspera Connect.app/Contents/Resources/ascp' -QT -l 300m -i '/Applications/Aspera Connect.app/Contents/Resources/asperaweb_id_dsa.putty'" (Mac OS X). More about ascp please see the help ('ascp -h' in a shell).

Details

The function first gets ftp/fasp addresses of SRA data files with funcitn getSRAinfo for a given list of input SRA accessions; then downloads the SRA data files through ftp or fasp. The sra or sra-lite data files are downloaded from NCBI SRA and the fastq files are downloaded from EBI ENA.

Warning

Downloading SRA data files through ftp over long distance could take long time and should consider using using 'fasp'.

Author(s)

Jack Zhu <[email protected]>

See Also

listSRAfile, getSRAinfo, getFASTQinfo, getFASTQfile

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect( dbDriver("SQLite"), sra_dbname )
	
	## Not run: 
	## Download sra files from NCBI SRA using ftp protocol:
	in_acc = c("SRR000648","SRR000657")
	getSRAfile( in_acc, sra_con = sra_con, destDir = getwd(), fileType = 'sra', srcType = 'ftp')

	## Convert NCBI SRA format (.sra) data to fastq:
	## Download SRA Toolkit: http://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software 
	## Run fastq-dump to 
	## system ("fastq-dump SRR000648.sra")
	
	## Download fastq files from EBI using ftp protocol:
	getSRAfile( in_acc, sra_con, destDir = getwd(), fileType = 'fastq', srcType = 'ftp', makeDirectory = FALSE, method = 'curl', ascpCMD = NULL )
	
	## Download fastq files from EBI  ftp siteusing fasp protocol:
	ascpCMD <-  'ascp -QT -l 300m -i /usr/local/aspera/connect/etc/asperaweb_id_dsa.putty'
	getSRAfile( in_acc, sra_con,  fileType = 'fastq', srcType = 'fasp',  ascpCMD = ascpCMD )
	dbDisconnect( sra_con )
	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Get SRA data file information from NCBI SRA

Description

This function gets SRA .sra file information from NCBI SRA ftp site for a given list SRA accessions.

Usage

getSRAinfo( in_acc, sra_con, sraType = 'sra' )

Arguments

in_acc

character vector of SRA accessions, which should be in same SRA data type, either submission, study, sample, experiment or run.

sra_con

connection to the SRAmetadb SQLite database

sraType

type of SRA data files, which should be 'sra' ('litesra' has phased out ).

Details

The function first gets ftp addressed of sra or sra-lite data files with function listSRAfile and then get file size and date from NCBI SRA ftp sites.

Value

A data.frame of ftp addresses of SRA data files, and file size and date along with input SRA accessions.

Author(s)

Jack Zhu <[email protected]>

See Also

listSRAfile, getSRAfile

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
	
	## Get file size and date from NCBI ftp site for available fastq files associated with "SRS012041","SRS000290"
	# getSRAinfo (in_acc=c("SRS012041","SRS000290"), sra_con=sra_con, sraType='sra')

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Clear IGV tracks loaded.

Description

Clear IGV tracks loaded in the current IGV.

Usage

IGVclear(sock)

Arguments

sock

A socket connection to IGV.

Author(s)

Jack Zhu <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVload, IGVgoto

Examples

## Not run: 
  ## Create a file list from example bam files in the package
  exampleBams = file.path(system.file('extdata',package='SRAdb'),
    dir(system.file('extdata',package='SRAdb'),pattern='bam$'))
  
  ##Create a socket connection to IGV
  sock <- IGVsocket()
  ## Load the bam files into IGV
  IGVload(sock, exampleBams)

  ## Clear loaded tracks in the current IGV
  IGVclear(sock)
  
## End(Not run)

Collapse tracks in the IGV

Description

Using the remote command port of IGV, this function collapses tracks in the IGV.

Usage

IGVcollapse(sock)

Arguments

sock

A socket connection to IGV.

Author(s)

Jack Zhu <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVload

Examples

## Not run: 
  sock <- IGVsocket()
  IGVcollapse(sock)
  
## End(Not run)

Set the IGV genome.

Description

Set the IGV genome via the remote command port.

Usage

IGVgenome(sock, genome="hg18")

Arguments

sock

A socket connection to IGV.

genome

String representing a genome that IGV knows about.

Author(s)

Sean Davis <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV

Examples

## Not run: 
sock <- IGVsocket()
IGVgenome(sock, genome='hg18')

## End(Not run)

Go to a specified region in IGV.

Description

Using the remote command port of IGV, go to a specified region.

Usage

IGVgoto(sock, region)

Arguments

sock

A socket connection to IGV.

region

Scrolls to a locus. Use any text that is valid in the IGV search box.

Author(s)

Sean Davis <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVload

Examples

## Not run: 
  sock <- IGVsocket()
  IGVgoto(sock, 'chr1:1-10000')
  IGVgoto(sock, 'TP53')
  
## End(Not run)

Load data into IGV via remote port call.

Description

Loads data via a remote call to IGV.

Usage

IGVload(sock, files)

Arguments

sock

A socket connection to IGV.

files

Character vector of one or more filenames with full path or urls to load. Among supported file types are BAM and IGV session file, for other file types please check IGV web site: http://www.broadinstitute.org/igv/ControlIGV.

Author(s)

Sean Davis <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVgoto

Examples

## Not run: 
  ## Create a file list from example bam files in the package
  exampleBams = file.path(system.file('extdata',package='SRAdb'),
    dir(system.file('extdata',package='SRAdb'),pattern='bam$'))
  
  ## Create a socket connection to IGV
  sock <- IGVsocket()
  ## Load the bam files into IGV
  IGVload(sock, exampleBams)
  
## End(Not run)

Create an IGV session file

Description

This function will create an IGV session file

Usage

IGVsession(files, sessionFile, genome='hg18', VisibleAttribute='', destdir=getwd())

Arguments

files

Character vector of one or more filenames or urls to load - required.

sessionFile

String representing session file name - required

genome

String representing a genome that IGV knows about.

VisibleAttribute

Character vector of one or more IGV Visible Attributes to annotate data tracks to be loaded - optional.

destdir

Path where to save the IGV session file.

Details

While the current state of an IGV session can be saved to a named session file that can be opened to restore the IGV session later on, a IGV session file can be manually or programmatically created to achieve more efficient data loading and better control of IGV. IGVsession function was developed to create such IGV session files. For details please check IGV web site: http://www.broadinstitute.org/igv/ControlIGV

Value

An IGV session file with full file path.

Author(s)

Jack Zhu <[email protected]>

See Also

IGVload, IGVgenome, IGVgoto

Examples

library(SRAdb)
	exampleBams = file.path(system.file('extdata',package='SRAdb'),
	    dir(system.file('extdata',package='SRAdb'),pattern='bam$'))
	exampleSessionFile <- IGVsession(exampleBams, 'exampleBams.xml');
  	## Not run: 
  	## Start IGV within R. You only need one IGV instance with listen port 60151 open.
    startIGV()
	
	## Create a socket connection to IGV
	sock <- IGVsocket()
	## Wait until IGV fully launched and make sure the listen port for IGV is open (If not configured in IGV, follow these steops: IGV --> Perferences --> Advanced --> Check the checkbox 'Enable port' 60151.)
  	IGVload(sock, exampleSessionFile)
  	
## End(Not run)

Make a file snapshot of the current IGV screen.

Description

From the IGV documentation: "Saves a snapshot of the IGV window to an image file. If filename is omitted, writes a .png file with a filename generated based on the locus. If filename is specified, the filename extension determines the image file format, which must be .png or .eps."

Usage

IGVsnapshot(sock, fname = "", dirname=getwd())

Arguments

sock

A socket connection to IGV.

fname

The filename to save. Alternatively, if not specified, IGV will create a filename based on the locus being viewed.

dirname

The directory name as a string for where to save the snapshot file.

Author(s)

Sean Davis <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV

Examples

## Not run: 
  ## Create a snapshot of the current IGV window, which is usually the first launched IGV with listen port 60151 open
  sock <- IGVsocket()
  IGVsnapshot(sock)
  dir()
  
## End(Not run)

Create a Socket Connection to IGV.

Description

Create a Socket Connection to IGV by a specified port and host.

Usage

IGVsocket(host='localhost', port=60151)

Arguments

host

The name of remote host where IGV is running.

port

The port to connect to/listen on.

Author(s)

Sean Davis <[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVgoto

Examples

## Not run: 
  ## Create a socket connection to IGV
  sock <- IGVsocket()
  
## End(Not run)

Sort an alignment track by the specified option.

Description

Using the remote command port of IGV, Sorts an alignment track by the specified option. Recognized values for the option parameter are: base, position, strand, quality, sample, and readGroup.

Usage

IGVsort(sock, option)

Arguments

sock

A socket connection to IGV.

option

Recognized values for the option parameter are: base, position, strand, quality, sample, and readGroup.

Author(s)

Jack Zhu<[email protected]>

References

http://www.broadinstitute.org/igv/PortCommands

See Also

startIGV, IGVload

Examples

## Not run: 
  sock <- IGVsocket()
  IGVsort(sock, 'position')
  IGVsort(sock, 'base')
  IGVsort(sock, 'sample')
  
## End(Not run)

List sra, sra-lite or fastq data file names associated with input SRA accessions

Description

This function lists all sra, sra-lite or fastq data files associated with input SRA accessions

Usage

listSRAfile( in_acc, sra_con, fileType = 'sra', srcType = 'ftp' )

Arguments

in_acc

character vector of SRA accessions, which should be in same SRA data type, either submission, study, sample, experiment or run.

sra_con

connection to the SRAmetadb SQLite database

fileType

types of SRA data files, which should be 'sra' or 'fastq'. ('litesra' has phased out ).

srcType

type of transfer protocol, which should be "ftp" or "fasp".

Details

SRA fastq files are hosted at EBI ftp site (ftp://ftp.sra.ebi.ac.uk/vol1/fastq/) and .sra files are hosted at NCBI ftp site (ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/). 'litesra' has phased out.

Value

A data frame of matched SRA accessions and data file names with ftp or fasp addresses.

Author(s)

Jack Zhu <[email protected]>

See Also

getSRAfile

Examples

## Using the SRAmetadb demo database
	## Not run: 
	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)

	## List ftp or fasp addresses of sra files associated with "SRX000122"
	listSRAfile (in_acc = c("SRX000122"), sra_con = sra_con, fileType = 'sra')
	listSRAfile (in_acc = c("SRX000122"), sra_con = sra_con, fileType = 'sra', srcType='fasp')
	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Cross-reference between GEO data types

Description

A common task is to find all the SRA entities of one type associated with another SRA entity (eg., find all SRA samples associated with SRA study 'SRP001990'). This function provides a very fast mapping between entity types to facilitate queries of this type.

Usage

sraConvert(in_acc, out_type = c("sra", "submission", "study", "sample", "experiment", "run"), sra_con)

Arguments

in_acc

Character vector of SRA accessions and should be of same SRA data type, either one of SRA submission, SRA study, SRA sample, SRA experiment and SRA run'

out_type

Character vector of the following SRA data types: 'sra', 'submission','study','sample','experiment','run'; if 'sra' is in out_type, out_type will be c("submission", "study", "sample", "experiment", "run")

sra_con

Connection to the SRAmetadb SQLite database

Value

A data.frame containing all matched SRA accessions.

Author(s)

Jack Zhu <[email protected]>

See Also

getSRA, listSRAfile, getSRAinfo

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)

	## Convert SRA experiment accessions to other types
	a <- sraConvert( in_acc=c(" SRR000137", "SRR000138 "), out_type=c('sample'), sra_con=sra_con )
	b <- sraConvert (in_acc=c("SRX000122"), sra_con=sra_con) 

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Create a new graphNEL object of SRA accessios from SRA full text search

Description

This function will create a new graphNEL object from SRA accessions using function of entityGraph and SRA accessions are returned from SRA full text search using function of getSRA

Usage

sraGraph(search_terms, sra_con)

Arguments

search_terms

Free text search terms constructed according to SQLite query syntax defined here: http://www.sqlite.org/fts3.html#section_1_3

sra_con

Connection to the SRAmetadb SQLite database

Details

This function is a wrapper of two functions: acc <- getSRA(search_terms, out_types='sra', sra_con, acc_only=TRUE) and g <- entityGraph(acc). A graphNEL object with edgemode='directed' is created from input data.frame of SRA accessions and the plot function will draw a graph

Value

A graphNEL object with edgemode='directed'

Author(s)

Jack Zhu <[email protected]> and Sean Davis <[email protected]>

See Also

getSRA, sraConvert, entityGraph

Examples

## Using the SRAmetadb demo database

	library(SRAdb)
  	library(Rgraphviz)
	
	sra_dbname <- file.path(system.file('extdata', package='SRAdb'), 'SRAmetadb_demo.sqlite')	
	sra_con <- dbConnect(dbDriver("SQLite"), sra_dbname)
	
	## create a graphNEL object from SRA accessions, which are full text search results of terms 'primary thyroid cell line'
	g <- sraGraph('MCF7 OR "MCF-7"', sra_con)
	attrs <- getDefaultAttrs(list(node=list(fillcolor='lightblue', shape='ellipse')))
	plot(g, attrs=attrs)

	## similiar search as the above, returned much larger data.frame and graph is too clouded
	g <- sraGraph('MCF', sra_con)
	## Not run: 
	plot(g)	
	
## End(Not run)

## The actual SRAmetadb sqlite database can be downloaded using function: getSRAdbFile. Warning: the actual SRAmetadb sqlite database is pretty large (> 35GB as of May, 2018) after uncompression. So, downloading and uncompressing of the actual SRAmetadb sqlite could take quite a few minutes depending on your network bandwidth. Direct links for downloading the SRAmetadb sqlite database:  https://s3.amazonaws.com/starbuck1/sradb/SRAmetadb.sqlite.gz https://gbnci-abcc.ncifcrf.gov/backup/SRAmetadb.sqlite.gz

Start IGV from R with different amount maximum memory support

Description

This function is to start the Integrative Genomics Viewer (IGV) within R, which is a high-performance visualization tool for interactive exploration of large, integrated datasets. It supports a wide variety of data types including sequence alignments, microarrays, and genomic annotations. In the SRAdb, functions of load2IGV and load2newIGV can be used to load BAM format of sequencing data into IGV conveniently.

Usage

startIGV(memory = "mm", devel=FALSE)

Arguments

memory

Maximum usable memory support for the IGV to be launched, which is defined as the following: 'mm' - 1.2 GB , 'lm' - 2 GB, 'hm' - 10 GB, ” - 750 MB

devel

Start development version of IGV.

Details

IGV with 1.2 GB maximum usable memory ('mm') is usually for 32-bit Windows; IGV with 2 GB maximum usable memory ('lm') is usually for 32-bit MacOS; IGV with 10 GB maximum usable memory is for large memory 64-bit java machines; IGV with 750 MB (”) is sufficient for most applications. The IGV will be launched through Java Web Start. For details about how IGV is launched or have problems to launch it, please refer to this site: http://www.broadinstitute.org/igv/StartIGV . Note: if IGVload will be used to load BAM files to the new launched IGV, a connection port needs to be enabled in the IGV. This is how to enable connection port in the IGV: in IGV, go View->Preferences->Advanced->Enable port and check the checkbox.

Author(s)

Jack Zhu

References

http://www.broadinstitute.org/igv/

See Also

IGVload, IGVgoto, IGVgenome

Examples

## launch IGV with 1.2 GB maximum usable memory support
## Not run: startIGV("lm"))