recipe bioconductor-snprelate

Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/SNPRelate.html

License:

GPL-3

Recipe:

/bioconductor-snprelate/meta.yaml

Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.

package bioconductor-snprelate

(downloads) docker_bioconductor-snprelate

versions:
1.36.0-01.34.1-01.32.0-11.32.0-01.28.0-21.28.0-11.28.0-01.26.0-01.24.0-1

1.36.0-01.34.1-01.32.0-11.32.0-01.28.0-21.28.0-11.28.0-01.26.0-01.24.0-11.24.0-01.22.0-01.20.0-01.18.1-01.18.0-01.16.0-01.14.0-01.12.2-0

depends bioconductor-gdsfmt:

>=1.38.0,<1.39.0

depends bioconductor-gdsfmt:

>=1.38.0,<1.39.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-snprelate

and update with::

   mamba update bioconductor-snprelate

To create a new environment, run:

mamba create --name myenvname bioconductor-snprelate

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-snprelate:<tag>

(see `bioconductor-snprelate/tags`_ for valid values for ``<tag>``)

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