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

GNU General Public License, GPLv3

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: v1.44.0

http://www.bioconductor.org/packages/SeqArray

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• Tutorials: Data Management, R Integration, Overview Slides
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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.

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.

Installation (requiring ≥ R_v3.5.0)

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

• Development version from Github (for developers/testers only): R 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 ```sh wget --no-check-certificate https://github.com/zhengxwen/gdsfmt/tarball/master -O gdsfmtlatest.tar.gz wget --no-check-certificate https://github.com/zhengxwen/SeqArray/tarball/master -O SeqArraylatest.tar.gz R CMD INSTALL gdsfmtlatest.tar.gz R CMD INSTALL SeqArraylatest.tar.gz

Or

curl -L https://github.com/zhengxwen/gdsfmt/tarball/master/ -o gdsfmtlatest.tar.gz curl -L https://github.com/zhengxwen/SeqArray/tarball/master/ -o SeqArraylatest.tar.gz R CMD INSTALL gdsfmtlatest.tar.gz R CMD INSTALL SeqArraylatest.tar.gz ```

Examples

```R 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) ```

```R

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 » | | ... | |

File Format Conversion

• seqVCF2GDS(): Format conversion from VCF to GDS
• gds2bgen: Format conversion from BGEN to GDS

SeqArray GDS File Downloads

• HapMap Project Phase 2 (release 23)
• 1000 Genomes Project

See Also

• JSeqArray.jl: Data manipulation of whole-genome sequencing variant data in Julia
• PySeqArray: Data manipulation of whole-genome sequencing variant data in Python