Increased yields of duplex sequencing data by a series of quality control tools

Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tierbased system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

Automated summary

Duplex sequencing is the most reliable method for identifying ultra-low frequency DNA variants, but only a small proportion of reads are assembled into duplex consensus sequences. A bioinformatics toolset was developed to analyze tag and family composition, identifying errors that contribute to data loss and proposing modifications to maximize data output. This tool also re-examines variant calls from raw reads, categorizing the confidence level of a variant call and increasing sequencing depth for variant calling.