Kuśmirek Wiktor
Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.
Bioinform Biol Insights. 2022 Aug 3;16:11779322221115534. doi: 10.1177/11779322221115534. eCollection 2022.
There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing-counting the depth of coverage in the investigated sequencing regions. However, each tool implements this stage in a slightly different way. Herein, we used data from the 1000 Genomes Project to present the impact of another depth of coverage counting strategies on the results of the CNVs detection process. In the study, we used 7 CNV calling tools: CODEX, CANOES, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind; from each of these applications, we separated the process of counting the depth of coverage into independent modules. Then, we counted the depth of coverage by mentioned modules, and finally, the obtained depth of coverage tables were used as the input data set to other CNV calling tools. The performed experiments showed that the best methods of counting the depth of coverage are the algorithms implemented in the CLAMMS and CNVkit applications. Both ways allow obtaining much better sets of detected CNVs compared to counting the depth of coverage implemented in other tools. What is more, some CNV detection tools are reasonably resistant to changing the input depth of coverage table. In this study, we proved that the exomeCopy application gives an approximately similar set of the resulting rare CNVs, regardless of the method of counting the depth of coverage table.
有许多基于覆盖深度的拷贝数变异（CNV）检测工具。所有基于覆盖深度的工具的一个特征是数据处理的第一阶段——计算所研究测序区域的覆盖深度。然而，每个工具实现这一阶段的方式略有不同。在此，我们使用了千人基因组计划的数据来展示另一种覆盖深度计数策略对CNV检测过程结果的影响。在这项研究中，我们使用了7种CNV检测工具：CODEX、CANOES、exomeCopy、ExomeDepth、CLAMMS、CNVkit和CNVind；从这些应用程序中的每一个，我们将覆盖深度计数过程分离为独立的模块。然后，我们通过上述模块计算覆盖深度，最后，将获得的覆盖深度表用作其他CNV检测工具的输入数据集。所进行的实验表明，最佳的覆盖深度计数方法是CLAMMS和CNVkit应用程序中实现的算法。与其他工具中实现的覆盖深度计数相比，这两种方法都能获得更好的检测到的CNV集合。此外，一些CNV检测工具对改变输入的覆盖深度表具有合理的抗性。在这项研究中，我们证明了exomeCopy应用程序给出的罕见CNV结果集大致相似，无论覆盖深度表的计数方法如何。
