An evaluation of copy number variation detection tools for cancer using whole exome sequencing data.
An evaluation of copy number variation detection tools for cancer using whole exome sequencing data.
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DOI：
10.1186/s12859-017-1705-x
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发表时间：
2017-05-31
影响因子：
3
通讯作者：
Nabavi S
中科院分区：
文献类型：
--
作者：
Zare F;Dow M;Monteleone N;Hosny A;Nabavi S
近年来，拷贝数变异(CNV)作为一种在疾病易感性中起重要作用的基因组/遗传变异引起了人们的极大兴趣。测序技术的进步为更准确地检测CNV创造了机会。最近，全外显子组测序(WES)已成为对患者样本进行测序和研究其基因组异常的主要策略。然而，与全基因组测序相比，WES引入了更多的偏差和噪声，这使得CNV的检测非常具有挑战性。此外，肿瘤的复杂性使得检测癌症特异性CNV变得更加困难。自从引入NGS数据以来，虽然已经开发了许多CNV检测工具，但用于肿瘤WES数据的体细胞CNV检测的工具还很少。在这项研究中，我们评估了最新和最常用的癌症WES数据CNV检测工具的性能，以解决它们的局限性，并为开发新的工具提供指导。我们把重点放在已经设计或有能力检测癌症体细胞异常的工具上。我们使用真实数据和模拟数据比较了这些工具在敏感度和错误发现率(FDR)方面的性能。对工具结果的比较分析表明，工具之间对调用CNV的共识较低。使用真实数据，工具表现出中等的敏感性(~50%-~80%)、中等的特异性(~70%-~94%)和较差的FDR(~27%-~60%)。此外，使用模拟数据我们观察到，将外显子区域的覆盖率增加到10倍以上并不能显著提高工具的检测能力。目前的CNV检测工具对癌症WES数据的性能有限，这表明需要开发更有效和更精确的CNV检测方法。由于肿瘤的复杂性和WES数据中的高水平噪声和偏差，采用专门针对癌症数据设计的先进的新型分割、归一化和去噪技术是必要的。此外，CNV检测的发展也受到缺乏绩效评估的黄金标准的影响。最后，开发具有用户友好的用户界面和可视化功能的工具可以为更广泛的用户增强CNV研究。本文的在线版本(doi：10.1186/s12859-0171705-x)包含补充材料，授权用户可以使用。
Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors’ complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer. In this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly. The limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users. The online version of this article (doi:10.1186/s12859-017-1705-x) contains supplementary material, which is available to authorized users.