Comparison of sequencing data from a tumor sample with data from a matched germline control is a key step for accurate detection of somatic mutations. Detection sensitivity for somatic variants is greatly reduced when the matched normal sample is contaminated with tumor cells. To overcome this limitation, we developed deTiN, a method that estimates the tumor-in-normal (TiN) contamination level and, in cases affected by contamination, improves sensitivity by reclassifying initially discarded variants as somatic.

中文翻译：

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DeTiN：克服正常肿瘤的污染

将肿瘤样品的测序数据与匹配的种系对照的数据进行比较是准确检测体细胞突变的关键步骤。当匹配的正常样品被肿瘤细胞污染时，体细胞变异的检测灵敏度大大降低。为了克服这一局限性，我们开发了deTiN，这是一种估计正常肿瘤（TiN）污染水平的方法，并且在受到污染影响的情况下，通过将最初丢弃的变体重新分类为体细胞来提高敏感性。