Dig is a computational method that leverages transfer-learning to test for positive selection across arbitrary genomic elements in arbitrary cohorts while requiring the resources only of a personal computer and is described in “Learning the mutational landscape of the cancer genome” by Maxwell Sherman, Adam Yaari, Oliver Priebe, Felix Dietlein, Po-Ru Loh and Bonnie Berger.

Predictions for 37 PCAWG cohorts can be explored using our interactive cancer map browser