Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling.
Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling.
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DOI：
10.1186/1471-2105-12-414
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发表时间：
2011-10-25
影响因子：
3
通讯作者：
Blanchette M
中科院分区：
文献类型：
--
作者：
Rousseau M;Fraser J;Ferraiuolo MA;Dostie J;Blanchette M
增强子、绝缘子和启动子等 DNA 调控元件之间的长程相互作用在转录调控中发挥着重要作用。由于在整个人类基因组和不同细胞类型中都发现了染色质接触，空间转录控制现在被视为基因表达调控的一般机制。染色体构象捕获碳拷贝 (5C) 及其变体 Hi-C 是用于测量基因组特定区域之间相互作用频率 (IF) 的技术。我们的目标是使用这些实验生成的 IF 数据来计算建模和分析三维染色质组织。我们制定了一个将 5C/Hi-C 数据与物理距离联系起来的概率模型，并描述了一种称为 MCMC5C 的马尔可夫链蒙特卡罗 (MCMC) 方法，用于根据 IF 数据结构的后验分布生成代表性样本。在同一数据集上运行的并行 MCMC 生成的结构表明，我们的 MCMC 方法可以快速混合，并且能够从结构的后验分布中进行采样并找到结构的子类。定义了结构特性（碱基循环、凝结和局部密度），并测量了它们在生成的结构整体中的分布。我们将这些方法应用于人类骨髓单核细胞分化的生物模型，并确定了与每种细胞状态相对应的不同染色质构象特征（CCS）。我们还证明了我们的方法能够在 Hi-C 数据上运行并生成 1Mb 分辨率的人类 14 号染色体模型，该模型与之前通过 3D-FISH 测量观察到的结构特性一致。我们相信，像 MCMC5C 这样的工具对于可靠地分析 5C 和 Hi-C 等 3C 衍生技术的数据至关重要。通过将复杂、高维和嘈杂的数据集集成到易于解释的三维构象集合中，MCMC5C 使研究人员能够可靠地解释其测定结果并在不同条件下对比构象。 http://Dostielab.biochem.mcgill.ca
Long-range interactions between regulatory DNA elements such as enhancers, insulators and promoters play an important role in regulating transcription. As chromatin contacts have been found throughout the human genome and in different cell types, spatial transcriptional control is now viewed as a general mechanism of gene expression regulation. Chromosome Conformation Capture Carbon Copy (5C) and its variant Hi-C are techniques used to measure the interaction frequency (IF) between specific regions of the genome. Our goal is to use the IF data generated by these experiments to computationally model and analyze three-dimensional chromatin organization. We formulate a probabilistic model linking 5C/Hi-C data to physical distances and describe a Markov chain Monte Carlo (MCMC) approach called MCMC5C to generate a representative sample from the posterior distribution over structures from IF data. Structures produced from parallel MCMC runs on the same dataset demonstrate that our MCMC method mixes quickly and is able to sample from the posterior distribution of structures and find subclasses of structures. Structural properties (base looping, condensation, and local density) were defined and their distribution measured across the ensembles of structures generated. We applied these methods to a biological model of human myelomonocyte cellular differentiation and identified distinct chromatin conformation signatures (CCSs) corresponding to each of the cellular states. We also demonstrate the ability of our method to run on Hi-C data and produce a model of human chromosome 14 at 1Mb resolution that is consistent with previously observed structural properties as measured by 3D-FISH. We believe that tools like MCMC5C are essential for the reliable analysis of data from the 3C-derived techniques such as 5C and Hi-C. By integrating complex, high-dimensional and noisy datasets into an easy to interpret ensemble of three-dimensional conformations, MCMC5C allows researchers to reliably interpret the result of their assay and contrast conformations under different conditions. http://Dostielab.biochem.mcgill.ca
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影响因子：
7
作者：
Kharchenko, Peter V.;Woo, Caroline J.;Park, Peter J.
通讯作者：
Park, Peter J.
α-珠蛋白基因结构域的三维折叠揭示了染色质球的形成。
影响因子：
16.8
作者：
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影响因子：
64.8
作者：
Hark, AT;Schoenherr, CJ;Tilghman, SM
通讯作者：
Tilghman, SM
影响因子：
5.6
作者：
COHEN, FE;STERNBERG, MJE
通讯作者：
STERNBERG, MJE
影响因子：
12.3
作者：
Fraser J;Rousseau M;Shenker S;Ferraiuolo MA;Hayashizaki Y;Blanchette M;Dostie J
通讯作者：
Dostie J