Inferential modeling of 3D chromatin structure.
Inferential modeling of 3D chromatin structure.
复制标题
3D 染色质结构的推理建模
DOI：
10.1093/nar/gkv100
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发表时间：
2015-04-30
影响因子：
14.9
通讯作者：
Zeng J
中科院分区：
文献类型：
--
作者：
Wang S;Xu J;Zeng J
对于真核细胞，涉及DNA调控元件的生物学过程在细胞周期中起重要作用。了解染色体的3D空间排列和揭示长距离染色质相互作用对于破译这些生物学过程至关重要。近年来，染色体构象捕获（3C）相关技术已经发展到测量远程基因组位点之间的相互作用频率，这为解码基因组的3D结构提供了很好的机会。在本文中，我们开发了一个新的贝叶斯框架，从基于3C的数据中获得染色体的3D结构。通过将每个染色体建模为一个聚合物链，我们根据我们目前对聚合物物理学的了解定义了构象能，并将其用作贝叶斯框架中的先验信息。我们还提出了一种基于期望最大化（EM）的算法来估计贝叶斯模型的未知参数，并根据相互作用频率数据推断染色质结构的集合。我们已经通过交叉验证验证了我们的贝叶斯推理方法，并验证了计算的染色质构象使用来自荧光原位杂交（FISH）实验的几何约束。我们进一步证实了推断的染色质结构使用已知的遗传相互作用来自其他研究文献。我们的测试结果表明，我们的贝叶斯框架可以计算出一个准确的合奏的3D染色质构象，最好的解释来自3C为基础的数据的距离约束，也同意来自实验证据在以前的研究中的几何约束的其他来源。我们的方法的源代码可以在https://github.com/wangsy11/InfMod3DGen中找到。
For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen.