Zhao Hai-Yong, Li Qi, Tian Ye, Chen Yue-Hui, Alvi Haque A K, Yuan Xi-Guo
School of Computer Science and Technology, Liaocheng University, Liaocheng 252000, China.
School of Computer Science and Technology, Xidian University, Xi'an 710071, China.
Biology (Basel). 2021 Jun 25;10(7):584. doi: 10.3390/biology10070584.
Copy number variation (CNV) is a common type of structural variation in the human genome. Accurate detection of CNVs from tumor genomes can provide crucial information for the study of tumor genesis and cancer precision diagnosis. However, the contamination of normal genomes in tumor genomes and the crude profiles of the read depth make such a task difficult. In this paper, we propose an alternative approach, called CIRCNV, for the detection of CNVs from sequencing data. CIRCNV is an extension of our previously developed method CNV-LOF, which uses local outlier factors to predict CNVs. Comparatively, CIRCNV can be performed on individual tumor samples and has the following two new features: (1) it transfers the read depth profile from a line shape to a circular shape via a polar coordinate transformation, in order to improve the efficiency of the read depth (RD) profile for the detection of CNVs; and (2) it performs a second round of CNV declaration based on the truth circular RD profile, which is recovered by estimating tumor purity. We test and validate the performance of CIRCNV based on simulation and real sequencing data and perform comparisons with several peer methods. The results demonstrate that CIRCNV can obtain superior performance in terms of sensitivity and precision. We expect that our proposed method will be a supplement to existing methods and become a routine tool in the field of variation analysis of tumor genomes.
拷贝数变异（CNV）是人类基因组中常见的一种结构变异类型。从肿瘤基因组中准确检测CNV可为肿瘤发生机制研究及癌症精准诊断提供关键信息。然而，肿瘤基因组中正常基因组的污染以及测序深度图谱的粗糙使得这项任务颇具难度。在本文中，我们提出了一种名为CIRCNV的替代方法，用于从测序数据中检测CNV。CIRCNV是我们之前开发的方法CNV - LOF的扩展，CNV - LOF利用局部离群因子来预测CNV。相比之下，CIRCNV可在单个肿瘤样本上进行，并且具有以下两个新特性：（1）通过极坐标变换将测序深度图谱从线性形状转换为圆形形状，以提高测序深度（RD）图谱在检测CNV方面的效率；（2）基于通过估计肿瘤纯度恢复的真实圆形RD图谱进行第二轮CNV声明。我们基于模拟数据和真实测序数据测试并验证了CIRCNV的性能，并与几种同类方法进行了比较。结果表明，CIRCNV在灵敏度和精度方面能够获得卓越的性能。我们期望我们提出的方法将成为现有方法的补充，并成为肿瘤基因组变异分析领域的常规工具。
