SeqArray: Data Management of Large-scale Whole-genome Sequence Variant Calls

Features

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.

Bioconductor:

Release Version: v1.22.3

http://www.bioconductor.org/packages/release/bioc/html/SeqArray.html

Help Documents
Tutorials: Data Management, R Integration, Overview Slides
News

Development Version: v1.23.4

http://www.bioconductor.org/packages/devel/bioc/html/SeqArray.html

Help Documents
Tutorials: Data Management, R Integration, Overview Slides
News

Citation

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.

Installation (requiring ≥ R_v3.5.0)

Bioconductor repository:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("SeqArray")

Development version from Github:

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.

Install the package from the source code: gdsfmt, SeqArray

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

SeqArray File Download

1000 Genomes Project

Examples

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 }

Key Functions in the SeqArray Package

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 »
...	»

Benchmarks

1. Reading genotypes in random sample subsets (BioC release 3.8 vs 3.4)

(the number of samples is ~100k)

Update ... (in progress)

File Format Conversion

gds2bgen: Format Conversion Between GDS and BGEN

Bug Fixes

v1.22.0: fix seqVCF2GDS() and seqBCF2GDS() since reading from connections in text mode is buffered in R >= v3.5.0

Also See

PySeqArray: data manipulation of whole-genome sequencing variant data in Python

JSeqArray.jl: data manipulation of whole-genome sequencing variant data in Julia

smgogarten/SeqArray