Suppr超能文献

基于数据驱动的多聚体模型用于探索二倍体基因组结构的机制

Data-Driven Polymer Model for Mechanistic Exploration of Diploid Genome Organization.

机构信息

Departments of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts.

Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Department of Data Sciences, Dana Farber Cancer Institute, Boston, Massachusetts; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.

出版信息

Biophys J. 2020 Nov 3;119(9):1905-1916. doi: 10.1016/j.bpj.2020.09.009. Epub 2020 Sep 22.

Abstract

Chromosomes are positioned nonrandomly inside the nucleus to coordinate with their transcriptional activity. The molecular mechanisms that dictate the global genome organization and the nuclear localization of individual chromosomes are not fully understood. We introduce a polymer model to study the organization of the diploid human genome. It is data-driven because all parameters can be derived from Hi-C data; it is also a mechanistic model because the energy function is explicitly written out based on a few biologically motivated hypotheses. These two features distinguish the model from existing approaches and make it useful both for reconstructing genome structures and for exploring the principles of genome organization. We carried out extensive validations to show that simulated genome structures reproduce a wide variety of experimental measurements, including chromosome radial positions and spatial distances between homologous pairs. Detailed mechanistic investigations support the importance of both specific interchromosomal interactions and centromere clustering for chromosome positioning. We anticipate the polymer model, when combined with Hi-C experiments, to be a powerful tool for investigating large-scale rearrangements in genome structure upon cell differentiation and tumor progression.

摘要

染色体在细胞核内的位置是随机的,以协调其转录活性。决定基因组整体组织和个别染色体核定位的分子机制尚不完全清楚。我们引入了一个聚合物模型来研究二倍体人类基因组的组织。它是数据驱动的,因为所有参数都可以从 Hi-C 数据中推导出来;它也是一个机械模型,因为能量函数是根据一些基于生物学的假设明确写出的。这两个特点使该模型与现有方法区分开来,使其在重建基因组结构和探索基因组组织原则方面都非常有用。我们进行了广泛的验证,表明模拟的基因组结构再现了广泛的实验测量结果,包括染色体的径向位置和同源对之间的空间距离。详细的机制研究支持了特定的染色体间相互作用和着丝粒聚类对于染色体定位的重要性。我们预计聚合物模型与 Hi-C 实验相结合,将成为研究细胞分化和肿瘤进展过程中基因组结构大规模重排的有力工具。

相似文献

5
Inferring chromosome radial organization from Hi-C data.从 Hi-C 数据推断染色体的径向组织。
BMC Bioinformatics. 2020 Nov 10;21(1):511. doi: 10.1186/s12859-020-03841-7.

引用本文的文献

5
Hi-C-guided many-polymer model to decipher 3D genome organization.Hi-C 引导的多聚体模型解析 3D 基因组结构。
Biophys J. 2024 Aug 20;123(16):2574-2583. doi: 10.1016/j.bpj.2024.06.023. Epub 2024 Jun 25.
10
Computational methods for analysing multiscale 3D genome organization.分析多尺度 3D 基因组结构的计算方法。
Nat Rev Genet. 2024 Feb;25(2):123-141. doi: 10.1038/s41576-023-00638-1. Epub 2023 Sep 6.

本文引用的文献

1
Characterizing chromatin folding coordinate and landscape with deep learning.利用深度学习刻画染色质折叠坐标和图谱。
PLoS Comput Biol. 2020 Sep 28;16(9):e1008262. doi: 10.1371/journal.pcbi.1008262. eCollection 2020 Sep.
2
3D ATAC-PALM: super-resolution imaging of the accessible genome.3D ATAC-PALM:可及基因组的超高分辨率成像。
Nat Methods. 2020 Apr;17(4):430-436. doi: 10.1038/s41592-020-0775-2. Epub 2020 Mar 16.
3
Organization and regulation of gene transcription.基因转录的组织和调节。
Nature. 2019 Sep;573(7772):45-54. doi: 10.1038/s41586-019-1517-4. Epub 2019 Aug 28.
4
The Impact of Centromeres on Spatial Genome Architecture.着丝粒对空间基因组结构的影响。
Trends Genet. 2019 Aug;35(8):565-578. doi: 10.1016/j.tig.2019.05.003. Epub 2019 Jun 11.
5
Predicting three-dimensional genome organization with chromatin states.基于染色质状态预测三维基因组结构。
PLoS Comput Biol. 2019 Jun 10;15(6):e1007024. doi: 10.1371/journal.pcbi.1007024. eCollection 2019 Jun.
9
Organizational principles of 3D genome architecture.三维基因组结构的组织原则。
Nat Rev Genet. 2018 Dec;19(12):789-800. doi: 10.1038/s41576-018-0060-8.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验