driveR
Prioritizing Cancer Driver Genes Using Genomics Data
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 genomic data for driver gene prioritization
by combining genomic 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
probability 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 Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for
prioritizing cancer driver genes using somatic genomics data. BMC
Bioinformatics
driveR: An R Package for Prioritizing Cancer Driver Genes Using Genomics Data
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
Installation
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)
Usage
driveR workflow
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")