Package Recipe 'bioconductor-gbscleanr'

recipe bioconductor-gbscleanr

Error correction tool for noisy genotyping by sequencing (GBS) data

Homepage:: https://bioconductor.org/packages/3.20/bioc/html/GBScleanR.html
License:: GPL-3 + file LICENSE
Recipe:: /bioconductor-gbscleanr/meta.yaml

GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population.

package bioconductor-gbscleanr¶

Versions:

2.4.4-0, 2.0.2-0, 1.6.0-0, 1.4.4-0, 1.2.0-1, 1.2.0-0

Depends:

on bioconductor-gdsfmt >=1.46.0,<1.47.0
on bioconductor-gdsfmt >=1.46.0,<1.47.0a0
on bioconductor-seqarray >=1.50.0,<1.51.0
on bioconductor-seqarray >=1.50.1,<1.51.0a0
on libblas >=3.9.0,<4.0a0
on libgcc >=14
on liblapack >=3.9.0,<4.0a0
on liblzma >=5.8.2,<6.0a0
on libstdcxx >=14
on libzlib >=1.3.1,<2.0a0
on r-base >=4.5,<4.6.0a0
on r-expm
on r-ggplot2
on r-rcpp
on r-rcppparallel
on r-tidyr
on tbb-devel >=2022.3.0,<2022.4.0a0

Additional platforms:

Installation¶

You need a conda-compatible package manager (currently either pixi, conda, or micromamba) and the Bioconda channel already activated (see Usage). Below, we show how to install with either pixi or conda (for micromamba and mamba, commands are essentially the same as with conda).

Pixi¶

With pixi installed and the Bioconda channel set up (see Usage), to install globally, run:

pixi global install bioconductor-gbscleanr

to add into an existing workspace instead, run:

pixi add bioconductor-gbscleanr

In the latter case, make sure to first add bioconda and conda-forge to the channels considered by the workspace:

pixi workspace channel add conda-forge
pixi workspace channel add bioconda

Conda¶

With conda installed and the Bioconda channel set up (see Usage), to install into an existing and activated environment, run:

conda install bioconductor-gbscleanr

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-gbscleanr

with envname being the name of the desired environment.

Container¶

Alternatively, every Bioconda package is available as a container image for usage with your preferred container runtime. For e.g. docker, run:

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

(see bioconductor-gbscleanr/tags for valid values for <tag>).

Integrated deployment¶

Finally, note that many scientific workflow management systems directly integrate both conda and container based software deployment. Thus, workflow steps can be often directly annotated to use the package, leading to automatic deployment by the respective workflow management system, thereby improving reproducibility and transparency. Check the documentation of your workflow management system to find out about the integration.

Download stats¶

Link to this page¶

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-gbscleanr/README.html)