We extracted the ±40 bp sequence context around each mutation, and used DeepBind to predict the binding scores of 472 TFs for the reference (ref score) and mutated sequences (alt score) of each mutation. Since the binding scores output by DeepBind are on an arbitrary scale and vary between different TF models, we estimated the background distributions of the binding scores of each TF by applying DeepBind to 10,000 randomly sampled non-hotspot mutations. For a particular TF, a mutation is predicted to be motif-disrupting if its reference sequence scores higher than 99.9% of the random mutations, and the score difference between its alternate and reference sequences (alt score – ref score) is smaller than 99.9% of the random mutations for that TF. A mutation is predicted to create a motif for a specific TF if its alternate sequence scores higher than 99.9% of the random mutations, and the score difference between its alternate and reference sequences (alt score – ref score) is greater than 99.9% of the random mutations for that TF.