BayVarC: an ultra-sensitive ctDNA variant caller using Bayesian approach

Che, D.; Yan, C.; Zha, J.; Niu, Z.; Yang, Q.; Cheng, X.; Han, M.; Du, C.; Zhang, K.; Hu, Y.; Yang, Y.-F.

doi:10.1101/2024.02.03.578772

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bioRxiv · 10.1101/2024.02.03.578772

BayVarC: an ultra-sensitive ctDNA variant caller using Bayesian approach

Che, D.·Yan, C.·Zha, J.·Niu, Z.·Yang, Q.·Cheng, X.·Han, M.·Du, C.·Zhang, K.·Hu, Y.·Yang, Y.-F.

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Abstract

In liquid biopsy, it is critical to detect variants of allele frequencies as low as 0.1% or even lower, especially when used to monitor secondary resistant mutations and minimal residual disease. Despite the efforts on improving experimental design, it remains challenging to distinguish low-frequency variants from technical noises in the downstream bioinformatic analysis. Here, we introduce BayVarC, a novel variant caller specifically designed for variant calling in liquid biopsy. It applies Bayesian inference to accurately quantify noise level in a locus-specific manner, enabling the discrimination between technical noise and low-frequency cancer variants. Detailed in-silico simulation and in-vitro experiments demonstrated BayVarC superior performance over existing state-of-the-art tools. BayVarC can effectively detect low frequency variants while maintaining low false positive rate (0.05 FP/KB). Meanwhile, it achieves Limit of Detection (LoD) as low as 0.1%. Furthermore, empowered by its architecture, BayVarC shows promising applicability in Minimal Residual Disease (MRD) detection. BayVarC is freely available at https://github.com/GenetronBioinfomatics/BayVarC.

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Che, D., Yan, C., Zha, J., Niu, Z., Yang, Q., Cheng, X., Han, M., Du, C., Zhang, K., Hu, Y., Yang, Y.-F.. 2024-02-07. BayVarC: an ultra-sensitive ctDNA variant caller using Bayesian approach. https://doi.org/10.1101/2024.02.03.578772

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