From 25900738e488eceeb521bf32a8242cfd1b9a6976 Mon Sep 17 00:00:00 2001 From: ctokheim Date: Thu, 6 Oct 2016 14:42:13 -0400 Subject: [PATCH] Minor change to README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 661d52a..04e79ed 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ 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 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+ uses monte carlo simulations to evaluate the significance of random forest scores based on an estimated p-value from an empirical null distribution. +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. 20/20+ uses monte carlo simulations to evaluate the significance of random forest scores based on an estimated p-value from an empirical null distribution. ## Documentation