从二维染色体构象捕获数据重建三维基因组结构的技术和挑战。
Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data.
机构信息
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA.
Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, 10012, NY, USA; New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Room 340, Geography Building, 3663 North Zhongshan Road, Shanghai, 200122, China; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA.
出版信息
Curr Opin Cell Biol. 2023 Aug;83:102209. doi: 10.1016/j.ceb.2023.102209. Epub 2023 Jul 26.
Chromosome conformation capture technologies that provide frequency information for contacts between genomic regions have been crucial for increasing our understanding of genome folding and regulation. However, such data do not provide direct evidence of the spatial 3D organization of chromatin. In this opinion article, we discuss the development and application of computational methods to reconstruct chromatin 3D structures from experimental 2D contact data, highlighting how such modeling provides biological insights and can suggest mechanisms anchored to experimental data. By applying different reconstruction methods to the same contact data, we illustrate some state-of-the-art of these techniques and discuss our gene resolution approach based on Brownian dynamics and Monte Carlo sampling.
染色体构象捕获技术为基因组区域之间的接触频率信息提供了重要支持，从而帮助我们加深了对基因组折叠和调控的理解。然而，此类数据并不能为染色质的空间 3D 结构提供直接证据。在这篇观点文章中，我们讨论了开发和应用计算方法，从实验的 2D 接触数据中重建染色质 3D 结构的问题，强调了这种建模如何提供生物学见解，并可以根据实验数据提出机制。通过将不同的重建方法应用于相同的接触数据，我们举例说明了这些技术的最新进展，并讨论了我们基于布朗动力学和蒙特卡罗抽样的基因分辨率方法。
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