recipe bioconductor-geneattribution

Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/geneAttribution.html

License

Artistic-2.0

Recipe

/bioconductor-geneattribution/meta.yaml

Links

biotools: geneattribution, doi: 10.1093/bioinformatics/btw698

package bioconductor-geneattribution

Versions

1.8.0-0, 1.6.0-0, 1.4.0-0

Depends

• bioconductor-biocgenerics >=0.28.0,<0.29.0

• bioconductor-genomeinfodb >=1.18.0,<1.19.0

• bioconductor-genomicfeatures >=1.34.0,<1.35.0

• bioconductor-genomicranges >=1.34.0,<1.35.0

• bioconductor-iranges >=2.16.0,<2.17.0

• bioconductor-org.hs.eg.db >=3.7.0,<3.8.0

• bioconductor-rtracklayer >=1.42.0,<1.43.0

• r-base >=3.5.1,<3.5.2.0a0

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-geneattribution

and update with:

conda update bioconductor-geneattribution

or use the docker container:

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

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

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-geneattribution/README.html)