Is this the right normalization? A diagnostic tool for ChIP-seq normalization.
Is this the right normalization? A diagnostic tool for ChIP-seq normalization.
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DOI：
10.1186/s12859-015-0579-z
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发表时间：
2015-05-09
影响因子：
3
通讯作者：
Yekutieli D
中科院分区：
文献类型：
--
作者：
Angelini C;Heller R;Volkinshtein R;Yekutieli D
Chip-seq实验正在成为全基因组分析蛋白质-DNA相互作用的标准方法，例如检测转录因子结合位点，组蛋白修饰标记和RNA聚合酶II占用。然而，当将ChIP样品与对照样品（如输入DNA）进行比较时，必须应用归一化程序以消除偏倚的实验来源。尽管标准化方法的选择可能对ChIP-seq数据分析的结果产生重大影响，但文献中并未充分探讨其评估。特别是，没有诊断工具可以显示所应用的归一化是否确实适用于正在分析的数据。在这项工作中，我们提出了一种新的诊断工具，以检查适当的估计归一化过程。通过在对数转换后绘制相等读数计数的箱中的对数相对风险的经验密度，沿着估计的归一化常数，研究人员能够评估估计的归一化常数的适当性。我们使用诊断图来评估由CisGenome、NCIS和CCAT在几个真实的数据示例上获得的估计值的适当性。此外，我们显示的影响，标准化常数的选择可以对标准工具的峰值调用，如MACS或SICER。最后，我们提出了一种新的程序，用于控制FDR使用样本交换。该程序利用估计的归一化常数，以获得优于初始选择的常数（用于MACS和SICER）的功效，初始选择的常数是ChIP和输入样本中读数总数的比值。线性归一化方法旨在估计比例因子r，以在比较ChIP与输入样品时针对不同的测序深度进行调整。所估计的比例因子可以容易地并入许多峰值调用器算法中以提高峰值识别的准确性。本文中提出的诊断图可用于评估ChIP/输入归一化常数的适当性，从而允许用户选择最适当的估计值进行分析。本文的在线版本（doi：10.1186/s12859-015-0579-z）包含补充材料，可供授权用户使用。
Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature. In particular, there are no diagnostic tools that show whether the applied normalization is indeed appropriate for the data being analyzed. In this work we propose a novel diagnostic tool to examine the appropriateness of the estimated normalization procedure. By plotting the empirical densities of log relative risks in bins of equal read count, along with the estimated normalization constant, after logarithmic transformation, the researcher is able to assess the appropriateness of the estimated normalization constant. We use the diagnostic plot to evaluate the appropriateness of the estimates obtained by CisGenome, NCIS and CCAT on several real data examples. Moreover, we show the impact that the choice of the normalization constant can have on standard tools for peak calling such as MACS or SICER. Finally, we propose a novel procedure for controlling the FDR using sample swapping. This procedure makes use of the estimated normalization constant in order to gain power over the naive choice of constant (used in MACS and SICER), which is the ratio of the total number of reads in the ChIP and Input samples. Linear normalization approaches aim to estimate a scale factor, r, to adjust for different sequencing depths when comparing ChIP versus Input samples. The estimated scaling factor can easily be incorporated in many peak caller algorithms to improve the accuracy of the peak identification. The diagnostic plot proposed in this paper can be used to assess how adequate ChIP/Input normalization constants are, and thus it allows the user to choose the most adequate estimate for the analysis. The online version of this article (doi:10.1186/s12859-015-0579-z) contains supplementary material, which is available to authorized users.
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通讯作者：
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