LGPLv3 GNU Lesser General Public License, LGPL-3
This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel.
Release Version: v1.44.1
http://www.bioconductor.org/packages/release/bioc/html/gdsfmt.html
http://bioconductor.org/packages/release/bioc/vignettes/gdsfmt/inst/doc/gdsfmt.html
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.
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.
Dr. Xiuwen Zheng
https://bioconductor.org/packages/gdsfmt
https://github.com/zhengxwen/gdsfmt
- Bioconductor repository:
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("gdsfmt")
- Development version from Github (for developers/testers only):
library("devtools") install_github("zhengxwen/gdsfmt")
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.
- CoreArray C++ library, LGPL-3 License, 2007-2021, Xiuwen Zheng
- zlib, zlib License, 1995-2017, Jean-loup Gailly and Mark Adler
- LZ4, BSD 2-clause License, 2011-2019, Yann Collet
- liblzma, public domain, 2005-2018, Lasse Collin and other xz contributors
- README
In the R environment,
install.packages("getopt", repos="http://cran.r-project.org") install.packages("optparse", repos="http://cran.r-project.org") install.packages("crayon", repos="http://cran.r-project.org") if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("gdsfmt")
viewgds is a shell script written in R (viewgds.R), to view the contents of a GDS file. The R packages gdsfmt, getopt and optparse should be installed before running viewgds, and the package crayon is optional.
Usage: viewgds [options] file
Installation with command line,
curl -L https://raw.githubusercontent.com/zhengxwen/Documents/master/Program/viewgds.R > viewgds chmod +x viewgds ## Or wget -qO- --no-check-certificate https://raw.githubusercontent.com/zhengxwen/Documents/master/Program/viewgds.R > viewgds chmod +x viewgds
diffgds is a shell script written in R (diffgds.R), to compare two files GDS files. The R packages gdsfmt, getopt and optparse should be installed before running diffgds.
Usage: diffgds [options] file1 file2
Installation with command line,
curl -L https://raw.githubusercontent.com/zhengxwen/Documents/master/Program/diffgds.R > diffgds chmod +x diffgds ## Or wget -qO- --no-check-certificate https://raw.githubusercontent.com/zhengxwen/Documents/master/Program/diffgds.R > diffgds chmod +x diffgds
library(gdsfmt) # create a GDS file f <- createfn.gds("test.gds") add.gdsn(f, "int", val=1:10000) add.gdsn(f, "double", val=seq(1, 1000, 0.4)) add.gdsn(f, "character", val=c("int", "double", "logical", "factor")) add.gdsn(f, "logical", val=rep(c(TRUE, FALSE, NA), 50)) add.gdsn(f, "factor", val=as.factor(c(NA, "AA", "CC"))) add.gdsn(f, "bit2", val=sample(0:3, 1000, replace=TRUE), storage="bit2") # list and data.frame add.gdsn(f, "list", val=list(X=1:10, Y=seq(1, 10, 0.25))) add.gdsn(f, "data.frame", val=data.frame(X=1:19, Y=seq(1, 10, 0.5))) folder <- addfolder.gdsn(f, "folder") add.gdsn(folder, "int", val=1:1000) add.gdsn(folder, "double", val=seq(1, 100, 0.4)) # show the contents f # close the GDS file closefn.gds(f)
File: test.gds (1.1K)
+ [ ]
|--+ int { Int32 10000, 39.1K }
|--+ double { Float64 2498, 19.5K }
|--+ character { Str8 4, 26B }
|--+ logical { Int32,logical 150, 600B } *
|--+ factor { Int32,factor 3, 12B } *
|--+ bit2 { Bit2 1000, 250B }
|--+ list [ list ] *
| |--+ X { Int32 10, 40B }
| \--+ Y { Float64 37, 296B }
|--+ data.frame [ data.frame ] *
| |--+ X { Int32 19, 76B }
| \--+ Y { Float64 19, 152B }
\--+ folder [ ]
|--+ int { Int32 1000, 3.9K }
\--+ double { Float64 248, 1.9K }
pygds: Python interface to CoreArray Genomic Data Structure (GDS) files
jugds.jl: Julia interface to CoreArray Genomic Data Structure (GDS) files