20/20+: Ratiometric prediction of cancer driver genes

Author:Collin Tokheim
Contact:ctokheim AT jhu.edu
Source code:GitHub
Q&A:Biostars (tag: 2020+)

Next-generation DNA sequencing of the exome has detected hundreds of thousands of small somatic variants (SSV) in cancer. However, distinguishing genes containing driving mutations rather than simply passenger SSVs from a cohort sequenced cancer samples requires sophisticated computational approaches. 20/20+ integrates many features indicative of positive selection to predict oncogenes and tumor suppressor genes from small somatic variants. The features capture mutational clustering, conservation, mutation in silico pathogenicity scores, mutation consequence types, protein interaction network connectivity, and other covariates (e.g. replication timing). Contrary to methods based on mutation rate, 20/20+ uses ratiometric features of mutations by normalizing for the total number of mutations in a gene. This decouples the genes from gene-level differences in background mutation rate.

Contents:

Releases

  • 2020plus v1.2.2 - 9/10/2018 - Added option to handle mutational data sets where silent mutations are not reported
  • 2020plus v1.2.1 - 8/2/2018 - Fixed bug where configuration file would not load
  • 2020plus v1.2.0 - 3/21/2018 - Change to null distribution simulation
  • 2020plus v1.1.3 - 8/17/2017 - Bug fixes for different versions of rpy2
  • 2020plus v1.1.2 - 7/3/2017 - Further bug fixes for latest versions of 20/20+ dependencies
  • 2020plus v1.1.1 - 5/22/2017 - Bug fixes to work with newest version of pandas
  • 2020plus v1.1.0 - 11/21/2016 - Improved training procedure and added p-value diagnostic plots
  • 2020plus v1.0.3 - 10/12/2016 - Fixed error in logging
  • 2020plus v1.0.2 - 10/03/2016 - Fixed python3 conversion bug
  • 2020plus v1.0.1 - 6/26/2016 - Added ability to run 20/20+ as a pipeline
  • 2020plus v1.0.0 - 5/1/2016 - Initial release

Citation

Collin J. Tokheim, Nickolas Papadopoulos, Kenneth W. Kinzler, Bert Vogelstein, and Rachel Karchin. Evaluating the evaluation of cancer driver genes. PNAS 2016 ; published ahead of print November 22, 2016, doi:10.1073/pnas.1616440113