精準醫療中，體細胞變異點偵測非常重要。然而因為定序錯誤的關係使得準確地分析體細胞變異點變得困難。次世代定序的錯誤率約為0.1-1%，當突變等位基因頻率也約為1%或更低時，容易將定序錯誤誤判成突變。為了區分定序錯誤與突變，可以利用分子標籤 (unique molecular identifier, UMI) 的技術，標記序列的來源，再進行聚合酶連鎖反應 (polymerase chain reaction, PCR) 放大序列。當同樣分子標籤的序列被組成共通序列，大部分的定序錯誤會被移除。但PCR錯誤仍會影響判斷體細胞點突變。目前已有變異點偵測軟體能考慮UMI序列中的PCR錯誤，但他們利用基於現象的模型去評估PCR錯誤率。本研究提出一個基於PCR放大過程的統計架構來評估共通序列的錯誤率，利用貝式定理同時考慮PCR錯誤與定序錯誤。此外本研究也建立一套UMI模擬資料流程，用來驗證錯誤模型的效能。針對模擬的UMI資料，相比MuTect2與MAGERI，本研究的統計模型有較高的真陽性率與很低的偽陽性率。

Somatic variant calling is important in precision medicine. Accurate somatic variant calling, however, is difficult because of sequencing errors. Because next-generation sequencing error rate and variant allele frequency are often comparable (0.1-1%), sequencing errors can be misidentified as variants. Fortunately, most sequencing errors can be corrected via unique molecular identifiers (UMIs). In the approach, each DNA molecule is tagged by a UMI and the tagged molecules are amplified via polymerase chain reaction (PCR) into multiple copies and then sequenced. By assembling sequences of the same UMI into a consensus, sequencing errors should be removed. However, PCR errors may still exist. Current somatic variant calling tools that accept UMI data indeed consider PCR error, but they all evaluate PCR error using a phenomenon logical model. Such a model does not reflect the nature of PCR and cannot be connected to the fundamental parameters of PCR error rates. In this study, we develop a statistical framework that echoes the PCR amplification process. Based on the framework, the error probability of consensus bases is accurately estimated using Bayes’ theorem while considering both PCR error and sequencing error. We also set up a pipeline for generating UMI simulation data and test the performance. Comparing to MuTect2 and MAGERI, our approach shows a higher true positive rate and comparable false positive rate.

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