Learning Context-Dependent DNA Mutation Patterns in Error-Prone Polymerase Chain Reaction.

We present a novel statistical learning method for studying context-dependent error rates in error-prone polymerase chain reaction (PCR) experiments. We demonstrate the method by applying it to error-prone PCR sequencing data and show how it may be exploited to improve the evolvability of genes in protein engineering.