TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

The use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing, complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from technical errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel Bayesian background error suppression method that provides a robust estimation of background noise to enhance the specificity for ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects’ data demonstrate that the proposed algorithm consistently outperforms current position-specific models, particularly when the sample of is small. TNER is publicly available at https://github.com/ctDNA/TNER.

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