利用新一代测序数据重建组蛋白修饰网络

Ngoc Tu Le, T. Ho
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引用次数: 4

摘要

其音调蛋白的翻译后修饰(PTMs)在染色质上建立功能分离结构域和调节转录等重要生物过程中起着至关重要的作用。这些修饰往往以合作的方式起作用,形成复杂的“组蛋白密码”。因此,阐明它们之间的功能关系将大大增加我们对细胞分化、发育和癌症发病机制的理解。生物学证据表明,核小体定位可以提供有关PTMs相互作用的宝贵信息。然而,据我们所知,以前的作品都没有利用这些信息来重建他的音调修饰网络。我们提出了一种基于贝叶斯网络的计算方法来重建一个表示他的语气修饰函数关系的网络。我们的方法采用搜索和评分方法,利用他的音调修饰的交互信息来推断网络结构,这是通过每个修饰与核小体定位之间的相关性来衡量的。当应用于人CD4+ T细胞ChIP-Seq数据集时,包含38种不同的his音调修饰和其他三种蛋白H2A的结合信息。在Z, PolII和CTCF中,我们的方法不仅在恢复已知关系方面优于以往的方法,而且还提出了许多新的方法,证实了它的有效性和效率。我们的无偏推断网络结构的方法也可以应用于重建其他表观遗传因素的相互作用网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstruction of Histone Modification Network from Next-generation Sequencing Data
Post-translational modifications (PTMs) of his tone proteins play critical roles in establishing functionally separated domains on chromatin and regulating important biological processes, such as transcription. These modifications often act in cooperative manner, forming complicated "histone codes". Elucidation of functional relationships among them will, therefore, significantly increase our understanding of cell differentiation, development, and cancer pathogenesis. Biological evidence has shown that nucleosome positioning can provide invaluable information about interactive effects of PTMs. However, to our knowledge, none of previous works has exploited this information in the reconstruction of his tone modification networks. We propose a computational approach based on Bayesian network to reconstruct a network representing functional relationships of his tone modifications. Our approach employed the search-and-score method to infer the network structure using interactive information of his tone modifications, which is measured by the correlation between each modification with nucleosome positioning. When applied on human CD4+ T cell ChIP-Seq dataset, containing 38 different his tone modifications and binding information of three other proteins, H2A.Z, PolII and CTCF, our method not only outperformed previous approaches in recovering known relationships but also suggested many new ones, confirming its validity and efficiency. Our unbiased method for inferring the network structure can also be applied to reconstruct interaction networks of other epigenetic factors.
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