BACKGROUND With the rapid development of whole exome sequencing (WES), an increasing number of tools are being proposed for copy number variation (CNV) detection based on this technique. However, no comprehensive guide is available for the use of these tools in clinical settings, which renders them inapplicable in practice. To resolve this problem, in this study, we evaluated the performances of four WES-based CNV tools, and established a guideline for the recommendation of a suitable tool according to the application requirements. RESULTS In this study, first, we selected four WES-based CNV detection tools: CoNIFER, cn.MOPS, CNVkit and exomeCopy. Then, we evaluated their performances in terms of three aspects: sensitivity and specificity, overlapping consistency and computational costs. From this evaluation, we obtained four main results: (1) The sensitivity increases and subsequently stabilizes as the coverage or CNV size increases, while the specificity decreases. (2) CoNIFER performs better for CNV insertions than for CNV deletions, while the remaining tools exhibit the opposite trend. (3) CoNIFER, cn.MOPS and CNVkit realize satisfactory overlapping consistency, which indicates their results are trustworthy. (4) CoNIFER has the best space complexity and cn.MOPS has the best time complexity among these four tools. Finally, we established a guideline for tools' usage according to these results. CONCLUSION No available tool performs excellently under all conditions; however, some tools perform excellently in some scenarios. Users can obtain a CNV tool recommendation from our paper according to the targeted CNV size, the CNV type or computational costs of their projects, as presented in Table 1, which is helpful even for users with limited knowledge of computer science.

中文翻译：

---

基于全外显子组测序的拷贝数变异检测工具的比较研究。

背景技术随着全外显子组测序（WES）的快速发展，基于该技术，提出了越来越多的用于拷贝数变异（CNV）检测的工具。但是，没有全面的指南可用于在临床环境中使用这些工具，这使其在实践中不适用。为解决此问题，在本研究中，我们评估了四种基于WES的CNV工具的性能，并根据应用要求建立了推荐合适工具的指南。结果在本研究中，首先，我们选择了四个基于WES的CNV检测工具：CoNIFER，cn.MOPS，CNVkit和exomeCopy。然后，我们从三个方面评估了它们的性能：敏感性和特异性，重叠的一致性和计算成本。通过此评估，我们获得了四个主要结果：（1）灵敏度增加，随后随着覆盖率或CNV大小的增加而稳定，而特异性降低。（2）与CNV删除相比，CoNIFER对于CNV插入的执行效果更好，而其余工具则呈现相反的趋势。（3）CoNIFER，cn.MOPS和CNVkit实现了令人满意的重叠一致性，这表明它们的结果值得信赖。（4）在这四个工具中，CoNIFER具有最佳的空间复杂度，而cn.MOPS具有最佳的时间复杂度。最后，我们根据这些结果建立了工具使用指南。结论没有可用的工具在所有情况下都能表现出色；但是，某些工具在某些情况下表现出色。用户可以根据目标CNV大小，CNV类型或项目的计算成本从我们的论文中获得CNV工具推荐，