Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations.
driveR is a tool for personalized or batch analysis of genomics data
for driver gene prioritization by combining genomics information and
prior biological knowledge. As features, driveR uses coding impact
metaprediction scores, non-coding impact scores, somatic copy number
alteration scores, hotspot gene/double-hit gene condition, ‘phenolyzer’
gene scores and memberships to cancer-related KEGG pathways. It uses
these features to estimate cancer-type-specific probabilities for each
gene of being a cancer driver using the related task of a multi-task
learning classification model.
The method is described in detail in Ülgen E, Sezerman OU. driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics. 2021 May 24;22(1):263.https://doi.org/10.1186/s12859-021-04203-7
You can install the latest released version of driveR from CRAN via:
install.packages("driveR")You can install the development version of driveR from
GitHub with:
# install.packages("devtools")
devtools::install_github("egeulgen/driveR", build_vignettes = TRUE)
driveR has two main objectives:
- Prediction of impact of coding variants (achieved via
predict_coding_impact()) - Prioritization of cancer driver genes (achieved via
create_features_df()andprioritize_driver_genes())
Note that driveR require operations outside of R and depends on the
outputs from the external tools ANNOVAR and phenolyzer.
For detailed information on how to use driveR, please see the vignette
“How to use driveR” via vignette("how_to_use")