Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. While the experimental portion of duplex sequencing requires robust molecular biology expertise, it is well developed, leaving the data analysis portion of the procedure lagging. Here we describe a new, greatly streamlined, reference-free approach for the analysis of duplex sequencing data. Upon ensuring that the approach precisely reproduces previously published results, we applied it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we attempted to democratize the data analysis for duplex sequencing by providing all necessary tools as stand-alone components as well as integrating them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex.