recipe bioconductor-bionero

Biological Network Reconstruction Omnibus

Homepage:

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

License:

GPL-3

Recipe:

/bioconductor-bionero/meta.yaml

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

package bioconductor-bionero

versions:

1.10.0-0,  1.8.5-0,  1.6.0-0,  1.2.0-0,  1.0.0-0

depends bioconductor-biocparallel:

>=1.36.0,<1.37.0

depends bioconductor-complexheatmap:

>=2.18.0,<2.19.0

depends bioconductor-genie3:

>=1.24.0,<1.25.0

depends bioconductor-minet:

>=3.60.0,<3.61.0

depends bioconductor-summarizedexperiment:

>=1.32.0,<1.33.0

depends bioconductor-sva:

>=3.50.0,<3.51.0

depends r-base:

>=4.3,<4.4.0a0

depends r-dynamictreecut:

depends r-ggdendro:

depends r-ggnetwork:

depends r-ggplot2:

depends r-ggrepel:

depends r-igraph:

depends r-intergraph:

depends r-matrixstats:

depends r-netrep:

depends r-patchwork:

depends r-rcolorbrewer:

depends r-reshape2:

depends r-rlang:

depends r-wgcna:

requirements:

additional platforms:

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-bionero

and update with::

   mamba update bioconductor-bionero

To create a new environment, run:

mamba create --name myenvname bioconductor-bionero

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-bionero:<tag>

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

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