基于马尔可夫随机场模型的ChIP-seq数据联合建模
Joint modelling of ChIP-seq data via a Markov random field model
摘要：
染色质免疫荧光测序（ChIP-seq）实验现在已经成为生物学中检测蛋白质结合位点的常规方法。在本文中，我们提出了一个马尔可夫随机场模型的多个ChIP-seq实验的联合分析。所提出的模型自然占空间依赖性的数据，假设一阶马尔可夫性，并为大比例的零计数，通过使用零膨胀的混合分布。与所有其他可用的实现方式相比，该模型允许通过结合试验设计的关键方面来对多个实验进行联合建模。特别地，该模型使用关于重复和关于实验中使用的不同抗体的信息。大量的仿真研究表明，在相同的错误发现率下，与现有方法相比，该方法具有更低的错误未发现率。最后，我们对来自8个ChIP-seq实验的两种染色质修饰剂的组蛋白修饰检测的真实数据进行了分析，包括具有不同IP效率的技术重复。
英文摘要：
Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first order Markov properties, and for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modelling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared to existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
