recipe bioconductor-csar

Statistical tools for the analysis of ChIP-seq data

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/CSAR.html

License:

Artistic-2.0

Recipe:

/bioconductor-csar/meta.yaml

Links:

biotools: csar

Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation.

package bioconductor-csar

versions:

1.58.0-0,  1.54.0-1,  1.54.0-0,  1.52.0-0,  1.50.0-1,  1.50.0-0,  1.46.0-2,  1.46.0-1,  1.46.0-0, 

1.58.0-0,  1.54.0-1,  1.54.0-0,  1.52.0-0,  1.50.0-1,  1.50.0-0,  1.46.0-2,  1.46.0-1,  1.46.0-0,  1.44.0-0,  1.42.0-1,  1.42.0-0,  1.40.0-0,  1.38.0-0,  1.36.0-1,  1.34.0-1,  1.34.0-0

depends bioconductor-genomeinfodb:

>=1.42.0,<1.43.0

depends bioconductor-genomeinfodb:

>=1.42.0,<1.43.0a0

depends bioconductor-genomicranges:

>=1.58.0,<1.59.0

depends bioconductor-genomicranges:

>=1.58.0,<1.59.0a0

depends bioconductor-iranges:

>=2.40.0,<2.41.0

depends bioconductor-iranges:

>=2.40.0,<2.41.0a0

depends bioconductor-s4vectors:

>=0.44.0,<0.45.0

depends bioconductor-s4vectors:

>=0.44.0,<0.45.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc:

>=13

depends liblapack:

>=3.9.0,<4.0a0

depends r-base:

>=4.4,<4.5.0a0

requirements:

additional platforms:

linux-aarch64

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-csar

and update with::

   mamba update bioconductor-csar

To create a new environment, run:

mamba create --name myenvname bioconductor-csar

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-csar:<tag>

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

Download stats

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