Genomes in 3D: from maps to mechanisms

3D 基因组：从图谱到机制

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10613581
• 关键词: 3-Dimensional; Atlases; Automobile Driving; Bioinformatics; Biological Models; Cell Nucleus; Cells; Chromosome Structures; Chromosomes; Clinical; Communication; Computer Models; DNA; DNA Sequence; Development; Disease; Engineering; Enhancers; Gene Expression Regulation; Genetic Transcription; Genome; Genome engineering; Genomics; Individual; Learning; Machine Learning; Maps; Meiosis; Methods; Modeling; Molecular Mechanisms of Action; Nucleotides; Pathogenicity; Play; Regulator Genes; Regulatory Element; Role; Synaptonemal Complex; Time; Tissues; Untranslated RNA; Variant; biophysical model; biophysical techniques; cell type; cohesin; frontier; genetic information; genetic variant; genome-wide; improved; in silico; insight; meter; model building; novel; novel strategies; promoter

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 在我们数以万亿计的细胞中，遗传信息存储在空间组织的一米长的染色体上 在微米级的原子核内。在过去的十年里，大规模的努力已经建立了越来越详细的地图集 转录、调控元件和基因组在不断扩大的细胞类型和组织集合中折叠。 尽管如此，这些美丽的图谱本身并不能揭示作用于 这些不同的蜂窝环境。借鉴生物物理学、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 潜在的3D基因组组织，以及这反过来如何与基因组功能相关。借鉴最新的 随着机器学习的突破，我们将对单个核苷酸如何对基因组折叠做出贡献进行建模。我们 将应用这些模型来表征细胞类型的特定基因组折叠，开发设计DNA的方法 电子计算机中的序列，以及模型增强子-启动子的影响。同时，我们将建立生物物理模型 深入了解基因组折叠的保守机制。利用减数分裂染色体折叠作为 模型系统，我们将开发模型来学习管理粘连动力学和环挤出的新规则。至 揭开挤压与其他机制的接口，我们将建立联会复合体的模型 组装，以及跨物种的减数分裂染色体组织模型。通过磨练 对特定于基因座的基因组折叠和描述驱动基因组的机制最关键的序列 广泛的折叠，我们开发的计算模型和我们发现的机制将使新的 精确基因组工程的方法。这将包括如何重新布线基因调节电路，不仅 通过靶向增强子和启动子，也通过调节它们的细胞类型特异性通信。在 同时，这里描述的目标将与3D染色体组织的基本见解联系在一起 临床基因组学，并极大地提高了非编码DNA变体的可解释性。

项目成果

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)
• 作者: Open2C;Abdennur,Nezar;Fudenberg,Geoffrey;Flyamer,IlyaM;Galitsyna,AleksandraA;Goloborodko,Anton;Imakaev,Maxim;Venev,Sergey
• 通讯作者: Venev,Sergey