使用监督分割模型提高过分散ChIP-seq数据的峰值检测精度
Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models
摘要：
动机：组蛋白修饰构成了基因表达遗传调控的基本机制。在21世纪初，出现了一种强大的技术，将染色质免疫沉淀与高通量测序（ChIP-seq）结合起来。该技术提供了与这些修饰相关的DNA区域的直接调查。为了实现这项技术的全部潜力，越来越复杂的统计算法已经被开发或调整，以分析它产生的大量数据。这些算法中的许多都是围绕自然假设（例如泊松假设）构建的，以模拟计数数据中的噪声。在这项工作中，我们从这些自然假设开始，并表明有可能改进它们。结果如下：我们对7个组蛋白修饰参考数据集（H3 K36 me 3和H3 K4 me 3）的比较结果表明，在应用条件下，自然假设并不总是现实的。我们表明，无约束的多个变点检测模型，替代噪声假设和适当的设置，减少了计数数据所表现出的过度分散，并证明比依赖于这些自然假设的算法更准确地检测峰值。
英文摘要：
Motivation: Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson one to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them. Results: The results of our comparisons on seven reference datasets of histone modifications (H3K36me3 and H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model, with alternative noise assumptions and a suitable setup, reduces the over-dispersion exhibited by count data and turns out to detect peaks more accurately than algorithms which rely on these natural assumptions.
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