The goal of cageminer is to integrate SNP data from GWAS results with
gene coexpression networks to identify high-confidence candidate genes
involved in a particular phenotype. To identify high-confidence
candidate genes, cageminer considers 3 criteria:
Presence in coexpression modules enriched in guide genes (i.e.,
“reference” genes that are known to be associated with the
phenotype).
Significant altered expression levels in a condition of interest
(e.g., stress, disease, etc).
By default, cageminer defines genes as high-confidence candidates if
they satisfy all of the 3 criteria above, but users can choose to use
only one/some of them.
Installation instructions
Get the latest stable R release from
CRAN. Then install cageminer from
Bioconductor using the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("cageminer")
Below is the citation output from using citation('cageminer') in R.
Please run this yourself to check for any updates on how to cite
cageminer.
print(citation('cageminer'), bibtex = TRUE)
#>
#> To cite cageminer in publications use:
#>
#> Almeida-Silva, F., & Venancio, T. M. (2022). cageminer: an
#> R/Bioconductor package to prioritize candidate genes by integrating
#> genome-wide association studies and gene coexpression networks. in
#> silico Plants, 4(2), diac018.
#> https://doi.org/10.1093/insilicoplants/diac018
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks},
#> author = {Fabricio Almeida-Silva and Thiago M. Venancio},
#> journal = {in silico Plants},
#> year = {2022},
#> volume = {4},
#> number = {2},
#> pages = {diac018},
#> url = {https://doi.org/10.1093/insilicoplants/diac018},
#> doi = {10.1093/insilicoplants/diac018},
#> }
Code of Conduct
Please note that the cageminer project is released with a Contributor
Code of Conduct. By
contributing to this project, you agree to abide by its terms.
cageminer
The goal of
cagemineris to integrate SNP data from GWAS results with gene coexpression networks to identify high-confidence candidate genes involved in a particular phenotype. To identify high-confidence candidate genes,cageminerconsiders 3 criteria:By default,
cageminerdefines genes as high-confidence candidates if they satisfy all of the 3 criteria above, but users can choose to use only one/some of them.Installation instructions
Get the latest stable
Rrelease from CRAN. Then installcageminerfrom Bioconductor using the following code:And the development version from GitHub with:
Citation
Below is the citation output from using
citation('cageminer')in R. Please run this yourself to check for any updates on how to cite cageminer.Code of Conduct
Please note that the
cageminerproject is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.Development tools
For more details, check the
devdirectory.This package was developed using biocthis.