Genomes in 3D: from maps to mechanisms

3D 基因组：从图谱到机制

基本信息

批准号：
10613581
负责人：
GEOFFREY FUDENBERG
金额：
$ 41.25万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-05-31
项目状态：
未结题

项目摘要

Project Summary In each of our trillions of cells, genetic information is stored on meter-long chromosomes spatially organized inside micron-scale nuclei. In the past decade, large-scale efforts have built increasingly detailed atlases of transcription, regulatory elements, and genome folding across ever-expanding sets of cell types and tissues. Still, these beautiful maps do not by themselves reveal the sequences or molecular mechanisms acting in these diverse cellular contexts. Drawing on approaches from biophysics, bioinformatics, and machine learning, this project will develop novel computational approaches to determine the DNA sequences and mechanisms underlying 3D genome organization, and how this in turn relates to genomic functions. Drawing on the latest breakthroughs in machine learning, we will model how individual nucleotides contribute to genome folding. We will apply these models to characterize cell-type specific genome folding, develop methods to engineer DNA sequences in silico, and model enhancer-promoter influences. Concurrently, we will build biophysical models to understand deeply conserved mechanisms of genome folding. Using meiotic chromosome folding as a model system, we will develop models to learn new rules governing cohesin dynamics and loop extrusion. To uncover how extrusion interfaces with other mechanisms, we will build models of synaptonemal complex assembly, as well as models of meiotic chromosome organization across species. By honing in on the sequences most crucial for locus-specific genome folding and characterizing the mechanisms driving genome- wide folding, the computational models we develop and the mechanisms we discover will enable new approaches to precision genome engineering. This will include how to re-wire gene-regulatory circuits, not only by targeting enhancers and promoters, but also by modulating their cell-type specific communication. At the same time, the aims described here will bridge fundamental insights into 3D chromosome organization with clinical genomics, and greatly improve the interpretability of non-coding DNA variants.

项目摘要在我们万亿个细胞中的每一个中，遗传信息都存储在空间组织的米长染色体上在微米尺度的核内。在过去的十年中，大规模的努力建立了越来越详细的地图集转录，调节元件和基因组在不断扩展的细胞类型和组织中折叠。尽管如此，这些美丽的地图本身并不能揭示作用在内的序列或分子机制这些多样化的细胞环境。利用生物物理学，生物信息学和机器学习的方法，该项目将开发新的计算方法来确定DNA序列和机制 3D基因组组织的基础，以及这又如何与基因组功能相关。绘制最新在机器学习中的突破，我们将建模单个核苷酸如何促进基因组折叠。我们将应用这些模型来表征细胞类型特异性基因组折叠，开发方法来工程DNA 计算机中的序列和模型增强子促销的影响。同时，我们将建立生物物理模型了解基因组折叠的深处保守机制。使用减数分裂染色体折叠作为模型系统，我们将开发模型，以了解有关粘蛋白动态和循环挤出的新规则。到揭示挤压与其他机制的接口，我们将构建突发型复合物的模型组装以及跨物种的减数分裂染色体组织的模型。通过磨练序列对于基因座特异性基因组折叠和表征驱动基因组的机制至关重要的序列 - 宽折叠，我们开发的计算模型以及我们发现的机制将启用新的精确基因组工程的方法。这将包括如何重新连接基因调控电路，不仅通过靶向增强子和启动子，还可以通过调节其细胞类型的特定通信。在同时，此处描述的目的将与3D染色体组织的基本见解联系起来临床基因组学，并大大提高了非编码DNA变异的解释性。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bioframe: operations on genomic intervals in Pandas dataframes.

Bioframe：对 Pandas 数据框中的基因组间隔进行操作。

DOI：
10.1093/bioinformatics/btae088
发表时间：
2024
期刊：
Bioinformatics (Oxford, England)
影响因子：
0
作者：
Open2C;Abdennur,Nezar;Fudenberg,Geoffrey;Flyamer,IlyaM;Galitsyna,AleksandraA;Goloborodko,Anton;Imakaev,Maxim;Venev,Sergey
通讯作者：
Venev,Sergey