Noise cancellation using total variation for copy number variation detection.
Noise cancellation using total variation for copy number variation detection.
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DOI：
10.1186/s12859-018-2332-x
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发表时间：
2018-10-22
影响因子：
3
通讯作者：
Nabavi S
中科院分区：
文献类型：
--
作者：
Zare F;Hosny A;Nabavi S
由于测序技术的最新进展，基于序列的分析已被广泛应用于检测拷贝数变异（CNV）。有几种使用下一代测序（NGS）数据鉴定CNV的技术，然而，采用覆盖深度或读取深度（RD）的方法最近已成为鉴定CNV的主要技术。基于RD的CNV检测方法的主要假设是，特定基因组位置处的读数计数值与该位置处的拷贝数相关。然而，读数数据的噪声和偏差扭曲了读数和拷贝数之间的关联。为了更准确地识别CNV，需要减轻这些偏差和噪声。在这项工作中，为了更精确和有效地检测CNVs，我们提出了一种新的去噪方法的基础上的总变分方法和Taut字符串算法。为了研究所提出的去噪方法的性能，我们使用模拟和真实的数据计算了CNV检测的灵敏度、错误发现率和特异性。我们还比较了所提出的去噪方法，Taut字符串，与常用的方法，如移动平均（MA）和离散小波变换（DWT）的检测真正的CNV和时间复杂度的灵敏度方面的性能。结果表明，Taut String比DWT和MA更好，并且具有更好的能力来识别非常窄的CNV。Taut String去噪在保留CNV片段的断点和窄CNV方面的能力提高了分割算法的检测准确性，从而提高了灵敏度和降低了错误发现率。在这项研究中，我们提出了一种新的去噪方法基于序列的CNV检测的信号处理技术的基础上。现有的CNV检测算法识别出许多错误的CNV片段，并且由于噪声和偏差而无法检测到短CNV片段。采用有效的去噪方法可以显著提高CNV分割算法的检测精度。可以采用来自信号处理领域的高级去噪方法来实现这样的算法。我们表明，考虑CNV数据的稀疏性和分段常数特性的非线性去噪方法在CNV检测中具有更好的性能。
Due to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data’s noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm. To investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments’ breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates. In this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.
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