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基于表观遗传数据的拓扑结构域形成的半非参数建模

Semi-nonparametric modeling of topological domain formation from epigenetic data.

作者信息

Sefer Emre, Kingsford Carl

机构信息

1Machine Learning Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 USA.

2Computational Biology Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, 15213 USA.

出版信息

Algorithms Mol Biol. 2019 Mar 5;14:4. doi: 10.1186/s13015-019-0142-y. eCollection 2019.

DOI:10.1186/s13015-019-0142-y
PMID:30867673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6399866/
Abstract

BACKGROUND

Hi-C experiments capturing the 3D genome architecture have led to the discovery of topologically-associated domains (TADs) that form an important part of the 3D genome organization and appear to play a role in gene regulation and other functions. Several histone modifications have been independently associated with TAD formation, but their combinatorial effects on domain formation remain poorly understood at a global scale.

RESULTS

We propose a convex semi-nonparametric approach called based on Bernstein polynomials to explore the joint effects of histone markers on TAD formation as well as predict TADs solely from the histone data. We find a small subset of modifications to be predictive of TADs across species. By inferring TADs using our trained model, we are able to predict TADs across different species and cell types, without the use of Hi-C data, suggesting their effect is conserved. This work provides the first comprehensive joint model of the effect of histone markers on domain formation.

CONCLUSIONS

Our approach, , can form the basis of a unified, explanatory model of the relationship between epigenetic marks and topological domain structures. It can be used to predict domain boundaries for cell types, species, and conditions for which no Hi-C data is available. The model may also be of use for improving Hi-C-based domain finders.

摘要

背景

用于捕获三维基因组结构的Hi-C实验已促使人们发现了拓扑相关结构域（TADs），这些结构域构成了三维基因组组织的重要部分，并且似乎在基因调控和其他功能中发挥作用。几种组蛋白修饰已分别与TAD形成相关联，但在全球范围内，它们对结构域形成的组合效应仍知之甚少。

结果

我们提出了一种基于伯恩斯坦多项式的凸半非参数方法，称为 ，以探索组蛋白标记对TAD形成的联合效应，并仅从组蛋白数据预测TADs。我们发现一小部分修饰可预测跨物种的TADs。通过使用我们训练的模型推断TADs，我们能够在不使用Hi-C数据的情况下预测不同物种和细胞类型的TADs，这表明它们的效应是保守的。这项工作提供了第一个关于组蛋白标记对结构域形成影响的全面联合模型。

结论

我们的方法 可以构成一个关于表观遗传标记与拓扑结构域结构之间关系的统一解释模型的基础。它可用于预测没有Hi-C数据的细胞类型、物种和条件下的结构域边界。该模型也可能有助于改进基于Hi-C的结构域查找器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/f104c78eb9f9/13015_2019_142_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/777966606679/13015_2019_142_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/02d2b38bd3b6/13015_2019_142_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/b32bf22d13a8/13015_2019_142_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/dbc1326ec7ea/13015_2019_142_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/f104c78eb9f9/13015_2019_142_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/777966606679/13015_2019_142_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/02d2b38bd3b6/13015_2019_142_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/b32bf22d13a8/13015_2019_142_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/dbc1326ec7ea/13015_2019_142_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c55/6399866/f104c78eb9f9/13015_2019_142_Fig5_HTML.jpg

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