Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58–0.78 versus 1.43–3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1–76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.

中文翻译：

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CLement：非肿瘤亚克隆的基因组分解和重建


单个样本的基因组水平克隆分解已得到广泛研究；然而，它主要限于癌症研究。在本研究中，我们开发了一种新的算法CLMENT，该算法可以在非肿瘤（正常）样本的基因组测序中对多个亚克隆进行精确的分解和重建。 CLMENT 采用期望最大化 (EM) 算法以及针对非肿瘤亚克隆的优化策略，包括错误变异识别、非异质克隆模糊聚类和克隆等位基因分数限制。在模拟和体外细胞系混合数据中，CLement 在估计克隆数量（均方根误差 = 0.58–0.78 vs 1.43–3.34）和变异克隆成员一致性（∼ 85.5% 与 70.1–76.7%）。对人类多克隆正常组织测序的额外测试证实了对源自不同细胞类型的亚克隆的准确鉴定。克隆水平分析，包括突变负担和特征，提供了对正常组织组成的新理解。我们预计 CLMENT 将成为当前新兴的非肿瘤基因组分析领域的重要工具。