Systematic comparison of enzymatic error correction methods using deep sequencing

Advances in de novo gene and genome synthesis will broadly benefit biology and biotechnology. Currently, one of the main bottlenecks for accurate, affordable, and high-throughput gene synthesis is the frequent occurrence of errors in the synthesized DNA sequence. Although various enzymatic error correction methods have been developed to address this problem, statistically effective evaluation and comparison of methods is difficult. Here we couple error-enriched template synthesis with next-generation sequencing to benchmark most previously reported enzymatic error correction methods. We report the landscape of synthesis errors before and after enzymatic corrections and compare the performance of each method in correcting deletions, insertions, and mismatches. This represents the first study that leverages deep sequencing to characterize error correction methods on a scale that is orders of magnitude larger than any past report and provides a method to benchmark novel enzymatic error correction techniques.