BioNERO

BioNERO aims to integrate all aspects of biological network inference in a single package, so users don’t have to learn the syntaxes of several packages and how to communicate among them. BioNERO features:

• Expression data preprocessing using state-of-the-art techniques for network inference.
• Automated exploratory data analyses, including principal component analysis (PCA) and heatmaps of gene expression or sample correlations.
• Inference of gene coexpression networks (GCNs) using the popular WGCNA algorithm.
• Inference of gene regulatory networks (GRNs) based on the “wisdom of the crowds” principle. This principle consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair.
• Exploration of network topology of GCNs, GRNs, and protein-protein interaction networks.
• Network visualization.
• Network comparison, including identification of consensus modules across independent expression sets, and calculation of intra and interspecies module preservation statistics between different networks.

Installation instructions

Get the latest stable R release from CRAN. Then install BioNERO from Bioconductor using the following code:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("BioNERO")

And the development version from GitHub with:

BiocManager::install("almeidasilvaf/BioNERO")

Citation

Below is the citation output from using citation('BioNERO') in R. Please run this yourself to check for any updates on how to cite BioNERO.

print(citation('BioNERO'), bibtex = TRUE)
# 
# To cite BioNERO in publications use:
# 
#   Almeida-Silva, F., Venancio, T.M. BioNERO: an all-in-one
#   R/Bioconductor package for comprehensive and easy biological network
#   reconstruction. Funct Integr Genomics 22, 131-136 (2022).
#   https://doi.org/10.1007/s10142-021-00821-9
# 
# A BibTeX entry for LaTeX users is
# 
#   @Article{,
#     title = {BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction},
#     author = {Fabricio Almeida-Silva and Thiago M. Venancio},
#     journal = {Functional And Integrative Genomics},
#     year = {2022},
#     volume = {22},
#     number = {1},
#     pages = {131-136},
#     url = {https://link.springer.com/article/10.1007/s10142-021-00821-9},
#     doi = {10.1007/s10142-021-00821-9},
#   }

Please note that the BioNERO was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.