sraConvert: Cross-reference between GEO data types
In SRAdb: A compilation of metadata from NCBI SRA and tools

Description Usage Arguments Value Author(s) See Also Examples

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.

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

`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

A data.frame containing all matched SRA accessions.

Jack Zhu <zhujack@mail.nih.gov>

getSRA, listSRAfile, getSRAinfo

## 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