在深度测序数据中识别显著细胞间基因组异质性的可行路线图
A feasible roadmap to identifying significant intercellular genomic heterogeneity in deep sequencing data
摘要：
细胞间异质性既是研究单个克隆的混杂因素，也是表征任何异质组织（如血液、肿瘤系统）的信息来源。由于不可避免的测序错误和其他样品制备伪像，如PCR错误，表征细胞间基因组异质性的系统性努力必须有效区分真正的克隆序列与假衍生物。我们开发了一种新的方法（SIGH），用于直接从混合测序读数中识别重要的真正克隆序列，这可以改善许多生物背景下的基因组分析。该方法提供了几个有吸引力的特征：（1）它自动地从原始序列读数估计错误率并识别真正的克隆序列;（2）它对由于各种实验条件而导致的大量错误率变化是鲁棒的;（3）它由充分利用测序错误的概率特性的基础统计框架支持;（4）其无偏策略允许检测稀有克隆，尽管该克隆相对丰度;以及（5）其估计每个样品中的组成比例。广泛的现实模拟研究表明，我们的方法可以可靠地估计错误率，并忠实地区分真正的克隆假衍生品，铺平了道路，否则破坏了往往占主导地位的假克隆的后续分析。
英文摘要：
Intercellular heterogeneity serves as both a confounding factor in studying individual clones and an information source in characterizing any heterogeneous tissues, such as blood, tumor systems. Due to inevitable sequencing errors and other sample preparation artifacts such as PCR errors, systematic efforts to characterize intercellular genomic heterogeneity must effectively distinguish genuine clonal sequences from fake derivatives. We developed a novel approach (SIGH) for identifying significant genuine clonal sequences directly from mixed sequencing reads that can improve genomic analyses in many biological contexts. This method offers several attractive features: (1) it automatically estimates the error rate from raw sequence reads and identifies genuine clonal sequences; (2) it is robust to the large variety of error rate due to the various experimental conditions; (3) it is supported by a well grounded statistical framework that exploits probabilistic characteristics of sequencing errors; (4) its unbiased strategy allows detecting rare clone(s) despite that clone relative abundance; and (5) it estimates constituent proportions in each sample. Extensive realistic simulation studies show that our method can reliably estimate the error rates and faithfully distinguish the genuine clones from fake derivatives, paving the way for follow up analysis that is otherwise ruined by the often dominant fake clones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
