SeqArray: Data Management of Large-scale Whole-genome Sequence Variant Calls ===  [GNU General Public License, GPLv3](http://www.gnu.org/copyleft/gpl.html) [](http://www.bioconductor.org/packages/release/bioc/html/SeqArray.html) [](http://www.bioconductor.org/packages/release/bioc/html/SeqArray.html) [](https://github.com/zhengxwen/SeqArray/actions/workflows/r.yml) ## 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)](https://github.com/zhengxwen/gdsfmt) 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](https://github.com/zhengxwen/gdsfmt). ## Bioconductor: Release Version: v1.42.3 [http://www.bioconductor.org/packages/release/bioc/html/SeqArray.html](http://www.bioconductor.org/packages/SeqArray) * [Help Documents](https://rdrr.io/bioc/SeqArray/man) * Tutorials: [Data Management](http://www.bioconductor.org/packages/release/bioc/vignettes/SeqArray/inst/doc/SeqArrayTutorial.html), [R Integration](http://www.bioconductor.org/packages/release/bioc/vignettes/SeqArray/inst/doc/SeqArray.html), [Overview Slides](http://www.bioconductor.org/packages/release/bioc/vignettes/SeqArray/inst/doc/OverviewSlides.html) * [News](http://www.bioconductor.org/packages/release/bioc/news/SeqArray/NEWS) Development Version: v1.43.1 [http://www.bioconductor.org/packages/devel/bioc/html/SeqArray.html](http://www.bioconductor.org/packages/devel/bioc/html/SeqArray.html) * Tutorials: [Data Management](http://www.bioconductor.org/packages/devel/bioc/vignettes/SeqArray/inst/doc/SeqArrayTutorial.html), [R Integration](http://bioconductor.org/packages/devel/bioc/vignettes/SeqArray/inst/doc/SeqArray.html), [Overview Slides](http://www.bioconductor.org/packages/devel/bioc/vignettes/SeqArray/inst/doc/OverviewSlides.html) * [News](./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](http://dx.doi.org/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](http://dx.doi.org/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](https://cran.r-project.org/faqs.html) for your operating system; you may also need to install dependencies manually. * Install the package from the source code: [gdsfmt](https://github.com/zhengxwen/gdsfmt), [SeqArray](https://github.com/zhengxwen/SeqArray) ```sh 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 ``` ## 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 [»](https://rdrr.io/bioc/SeqArray/man/seqVCF2GDS.html) | | seqSetFilter | Define a data subset of samples or variants [»](https://rdrr.io/bioc/SeqArray/man/seqSetFilter.html) | | seqGetData | Get data from a SeqArray file with a defined filter [»](https://rdrr.io/bioc/SeqArray/man/seqGetData.html) | | seqApply | Apply a user-defined function over array margins [»](https://rdrr.io/bioc/SeqArray/man/seqApply.html) | | seqBlockApply | Apply a user-defined function over array margins via blocking [»](https://rdrr.io/bioc/SeqArray/man/seqBlockApply.html) | | seqParallel | Apply functions in parallel [»](https://rdrr.io/bioc/SeqArray/man/seqParallel.html) | | [...](https://rdrr.io/bioc/SeqArray/man/) | | ## File Format Conversion * [seqVCF2GDS()](https://rdrr.io/bioc/SeqArray/man/seqVCF2GDS.html): Format conversion from VCF to GDS * [gds2bgen](https://github.com/zhengxwen/gds2bgen): Format conversion from BGEN to GDS ## SeqArray GDS File Downloads * [HapMap Project Phase 2 (release 23)](https://gds-stat.s3.amazonaws.com/download/hapmap/index.html) * [1000 Genomes Project](https://gds-stat.s3.amazonaws.com/download/1000g/index.html) ## See Also * [JSeqArray.jl](https://github.com/CoreArray/JSeqArray.jl): Data manipulation of whole-genome sequencing variant data in Julia * [PySeqArray](https://github.com/CoreArray/PySeqArray): Data manipulation of whole-genome sequencing variant data in Python