20/20+ @ Karchin lab

20/20+

predicts oncogenes and tumor suppressor genes from exome sequencing cancers in a cohort.

Next-generation DNA sequencing of the exome has detected millions 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 ratio-metric 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.

20/20+ is free for non-commercial use. For more details please refer to our Software License. Commercial users should contact the Johns Hopkins Technology Transfer office.

Current stable release is 2020plus.1.0.1, last updated on 06/26/2016.

Source Code Releases

You can view the current source code on github.

2020plus-1.0.1.tar.gz    06/26/2016    added pipeline code.

Documentation

Please consult the software documentation web page for installation and usage details.

Example workflow

An example of running 20/20+ is described on the quick start page. Please follow the wiki page for a step-by-step walk-through.

Platform

20/20+ is intended to run on unix operating systems. It needs both python and R. Please see the installation instructions.

Primary citation

If you use our software for a publication, please cite the following:

Tokheim C, Papdopoulis N, Kinzler KW, Vogelstein B, Karchin R (2016) Evaluating the evaluation of cancer driver genes. Submitted [bioRxiv preprint]

Software primary contact/developer

Collin Tokheim:  ctokheim at jhu dot edu