Package Recipe 'bioconductor-crupr'

recipe bioconductor-crupr

An R package to predict condition-specific enhancers from ChIP-seq data

Homepage:: https://bioconductor.org/packages/3.22/bioc/html/crupR.html
License:: GPL-3
Recipe:: /bioconductor-crupr/meta.yaml

An R package that offers a workflow to predict condition-specific enhancers from ChIP-seq data. The prediction of regulatory units is done in four main steps: Step 1 - the normalization of the ChIP-seq counts. Step 2 - the prediction of active enhancers binwise on the whole genome. Step 3 - the condition-specific clustering of the putative active enhancers. Step 4 - the detection of possible target genes of the condition-specific clusters using RNA-seq counts.

package bioconductor-crupr¶

Versions:

1.2.0-0

Depends:

on bioconductor-bamsignals >=1.42.0,<1.43.0
on bioconductor-biocparallel >=1.44.0,<1.45.0
on bioconductor-genomicalignments >=1.46.0,<1.47.0
on bioconductor-genomicfeatures >=1.62.0,<1.63.0
on bioconductor-genomicranges >=1.62.0,<1.63.0
on bioconductor-iranges >=2.44.0,<2.45.0
on bioconductor-preprocesscore >=1.72.0,<1.73.0
on bioconductor-rsamtools >=2.26.0,<2.27.0
on bioconductor-rtracklayer >=1.70.0,<1.71.0
on bioconductor-s4vectors >=0.48.0,<0.49.0
on bioconductor-seqinfo >=1.0.0,<1.1.0
on bioconductor-summarizedexperiment >=1.40.0,<1.41.0
on bioconductor-txdb.hsapiens.ucsc.hg19.knowngene >=3.22.0,<3.23.0
on bioconductor-txdb.hsapiens.ucsc.hg38.knowngene >=3.22.0,<3.23.0
on bioconductor-txdb.mmusculus.ucsc.mm10.knowngene >=3.10.0,<3.11.0
on bioconductor-txdb.mmusculus.ucsc.mm9.knowngene >=3.2.0,<3.3.0
on r-base >=4.5,<4.6.0a0
on r-dplyr
on r-fs
on r-ggplot2
on r-magrittr
on r-matrixstats
on r-randomforest
on r-reshape2

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-crupr

to add into an existing workspace instead, run:

pixi add bioconductor-crupr

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-crupr

Alternatively, to install into a new environment, run:

conda create -n envname bioconductor-crupr

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-crupr:<tag>

(see bioconductor-crupr/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-crupr/README.html)