GNU General Public License, GPLv3
Data management of whole-genome sequence variant calls with hundreds of thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.
The SeqArray package is built on top of Genomic Data Structure (GDS) data format, and defines required data structure for a SeqArray file. GDS is a flexible and portable data container with hierarchical structure to store multiple scalable array-oriented data sets. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. It also offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. A high-level R interface to GDS files is available in the package gdsfmt.
Release Version: v1.46.0
http://www.bioconductor.org/packages/SeqArray
- Help Documents
- Tutorials: Data Management, R Integration, Overview Slides
- News
Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D (2017). SeqArray -- A storage-efficient high-performance data format for WGS variant calls. Bioinformatics. DOI: 10.1093/bioinformatics/btx145.
Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012). A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics. DOI: 10.1093/bioinformatics/bts606.
- Bioconductor repository:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("SeqArray")
- Development version from Github (for developers/testers only):
library("devtools")
install_github("zhengxwen/gdsfmt")
install_github("zhengxwen/SeqArray")
The install_github()
approach requires that you build from source, i.e. make
and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.
wget --no-check-certificate https://github.com/zhengxwen/gdsfmt/tarball/master -O gdsfmt_latest.tar.gz
wget --no-check-certificate https://github.com/zhengxwen/SeqArray/tarball/master -O SeqArray_latest.tar.gz
R CMD INSTALL gdsfmt_latest.tar.gz
R CMD INSTALL SeqArray_latest.tar.gz
## Or
curl -L https://github.com/zhengxwen/gdsfmt/tarball/master/ -o gdsfmt_latest.tar.gz
curl -L https://github.com/zhengxwen/SeqArray/tarball/master/ -o SeqArray_latest.tar.gz
R CMD INSTALL gdsfmt_latest.tar.gz
R CMD INSTALL SeqArray_latest.tar.gz
library(SeqArray)
gds.fn <- seqExampleFileName("gds")
# open a GDS file
f <- seqOpen(gds.fn)
# display the contents of the GDS file
f
# close the file
seqClose(f)
## Object of class "SeqVarGDSClass"
## File: SeqArray/extdata/CEU_Exon.gds (298.6K)
## + [ ] *
## |--+ description [ ] *
## |--+ sample.id { Str8 90 LZMA_ra(35.8%), 258B } *
## |--+ variant.id { Int32 1348 LZMA_ra(16.8%), 906B } *
## |--+ position { Int32 1348 LZMA_ra(64.6%), 3.4K } *
## |--+ chromosome { Str8 1348 LZMA_ra(4.63%), 158B } *
## |--+ allele { Str8 1348 LZMA_ra(16.7%), 902B } *
## |--+ genotype [ ] *
## | |--+ data { Bit2 2x90x1348 LZMA_ra(26.3%), 15.6K } *
## | |--+ ~data { Bit2 2x1348x90 LZMA_ra(29.3%), 17.3K }
## | |--+ extra.index { Int32 3x0 LZMA_ra, 19B } *
## | \--+ extra { Int16 0 LZMA_ra, 19B }
## |--+ phase [ ]
## | |--+ data { Bit1 90x1348 LZMA_ra(0.91%), 138B } *
## | |--+ ~data { Bit1 1348x90 LZMA_ra(0.91%), 138B }
## | |--+ extra.index { Int32 3x0 LZMA_ra, 19B } *
## | \--+ extra { Bit1 0 LZMA_ra, 19B }
## |--+ annotation [ ]
## | |--+ id { Str8 1348 LZMA_ra(38.4%), 5.5K } *
## | |--+ qual { Float32 1348 LZMA_ra(2.26%), 122B } *
## | |--+ filter { Int32,factor 1348 LZMA_ra(2.26%), 122B } *
## | |--+ info [ ]
## | | |--+ AA { Str8 1348 LZMA_ra(25.6%), 690B } *
## | | |--+ AC { Int32 1348 LZMA_ra(24.2%), 1.3K } *
## | | |--+ AN { Int32 1348 LZMA_ra(19.8%), 1.0K } *
## | | |--+ DP { Int32 1348 LZMA_ra(47.9%), 2.5K } *
## | | |--+ HM2 { Bit1 1348 LZMA_ra(150.3%), 254B } *
## | | |--+ HM3 { Bit1 1348 LZMA_ra(150.3%), 254B } *
## | | |--+ OR { Str8 1348 LZMA_ra(20.1%), 342B } *
## | | |--+ GP { Str8 1348 LZMA_ra(24.4%), 3.8K } *
## | | \--+ BN { Int32 1348 LZMA_ra(20.9%), 1.1K } *
## | \--+ format [ ]
## | \--+ DP [ ] *
## | |--+ data { Int32 90x1348 LZMA_ra(25.1%), 118.8K } *
## | \--+ ~data { Int32 1348x90 LZMA_ra(24.1%), 114.2K }
## \--+ sample.annotation [ ]
## \--+ family { Str8 90 LZMA_ra(57.1%), 222B }
Function | Description |
---|---|
seqVCF2GDS | Reformat VCF files » |
seqSetFilter | Define a data subset of samples or variants » |
seqGetData | Get data from a SeqArray file with a defined filter » |
seqApply | Apply a user-defined function over array margins » |
seqBlockApply | Apply a user-defined function over array margins via blocking » |
seqParallel | Apply functions in parallel » |
... |
- seqVCF2GDS(): Format conversion from VCF to GDS
- gds2bgen: Format conversion from BGEN to GDS
- JSeqArray.jl: Data manipulation of whole-genome sequencing variant data in Julia
- PySeqArray: Data manipulation of whole-genome sequencing variant data in Python