Minor change to README

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