论文标题
DNA甲基化水平的全基因组推断的贝叶斯框架
A Bayesian framework for genome-wide inference of DNA methylation levels
论文作者
论文摘要
DNA甲基化是一个重要的表观遗传标记，已广泛研究其在生物过程和疾病中的调节作用。 WGB允许在单基分辨率上对DNA甲基化的全基因组测量，但在识别不同生物学条件下明显不同的甲基化模式方面构成了挑战。我们提出了一个新型的甲基化变化点模型，该模型描述了一个病例的甲基化状态的关节动力学以及对照组的甲基化状态，并考虑了所有可用样品中邻近甲基化位点的信息。我们还设计了粒子过滤和平滑算法以对潜在甲基化模式进行有效的推断。我们说明我们的方法可以在控制I型错误测量时检测和测试具有高功率的非常灵活的差异甲基化特征。
DNA methylation is an important epigenetic mark that has been studied extensively for its regulatory role in biological processes and diseases. WGBS allows for genome-wide measurements of DNA methylation up to single-base resolutions, yet poses challenges in identifying significantly different methylation patterns across distinct biological conditions. We propose a novel methylome change-point model which describes the joint dynamics of methylation regimes of a case and a control group and benefits from taking into account the information of neighbouring methylation sites among all available samples. We also devise particle filtering and smoothing algorithms to perform efficient inference of the latent methylation patterns. We illustrate that our approach can detect and test for very flexible differential methylation signatures with high power while controlling Type-I error measures.
