Bioinformatics Techniques to Analyze Dynamic Changes of 3D Genome

分析 3D 基因组动态变化的生物信息学技术

• 批准号: 10707922
• 负责人: Kaifu Chen
• 金额: $ 44.25万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707922
• 关键词: 3-Dimensional; Algorithms; Architecture; Benchmarking; Bioinformatics; Biological; Biological Assay; Biological Models; Biological Process; Blood Vessels; C10; CCCTC-binding factor; Cardiac; Cardiovascular Diseases; Cells; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromosome Territory; Communities; DNA; DNA Repair; Data; Detection; Development; Disease; Embryonic Development; Endothelial Cells; Endothelium; Enhancers; Feedback; Fibroblasts; Frequencies; Gene Expression; Genes; Genetic Transcription; Genome; Genomics; Hi-C; Individual; Inflammatory; Investigation; Length; Location; Malignant - descriptor; Measures; Mesenchymal; Methods; Modeling; Molecular; Outcome; Pathogenesis; Pattern; Performance; Play; Regulation; Reporting; Research Personnel; Resolution; Role; Sampling; Scientist; Site; Statistical Models; Techniques; Technology; Transcriptional Regulation; Variant; Width; Work; cell type; chromatin modification; chromosome conformation capture; epigenetic regulation; human disease; improved; indexing; insight; novel; tool; transcription factor

Project Summary Three-dimensional (3D) folding of the genome plays fundamental roles in the regulation of transcription, replication, DNA repair and many other biological processes. Facilitated by Hi-C and related techniques, it is becoming clear that the eukaryotic genome folds at multiple genomic scales to form different types of 3D architecture, including topologically associated domains (TADs) and stripes. Different physical patterns of change may happen to a type of 3D architecture, e.g., a TAD may show change of overall connectivity, or split into smaller TADs. Whereas the existence and functional importance of the genome’s 3D architecture is increasingly recognized, analyzing its dynamic changes is currently a major challenge to biologists. The community urgently needs novel bioinformatics techniques to define potential physical patterns of change for each type of 3D architecture, to systematically detect all changes in the genome, and to statistically determine the significance of each change. Our preliminary data strongly suggest that two physical patterns of change to the genome’s 3D architecture -- TAD splittings and stripe strengthenings -- regulate cell identity transitions. Accordingly, we propose to develop TADsplit and StripeDiff, two bioinformatics toolkits to systematically define these and additionally physical patterns of change to TADs and stripes between samples. As a proof of principle, we will utilize the new techniques to investigate 3D genome alterations during endothelial-to- mesenchymal transition (EndMT), a cell identity transition that plays critical roles in both normal development and many prevalent cardiovascular diseases. We will illustrate new mechanisms by which transcription factors regulate genome’s 3D architectures to oppose EndMT. These investigations have the potential to better guide the treatment of many diseases in which EndMT plays important roles. The novel bioinformatics techniques in TADsplit and StripeDiff will enable researchers to investigate 3D genome changes in diverse biological models of development and diseases.

项目摘要 基因组的三维（3D）折叠在转录调控中起着重要作用， 复制，DNA修复和许多其他生物过程。通过Hi-C和相关技术的促进， 越来越清楚的是，真核基因组在多个基因组尺度上折叠，形成不同类型的3D 架构，包括拓扑关联域（TAD）和条带。不同的物理模式 一种类型的3D架构可能发生变化，例如，网络连接可能会显示整体连接的更改或拆分 变成更小的TADs而基因组三维结构的存在和功能重要性， 分析其动态变化是目前生物学家面临的一个重大挑战。的 社区迫切需要新的生物信息学技术来定义潜在的物理变化模式， 每种类型的3D结构，系统地检测基因组中的所有变化，并统计确定 每一个变化的意义。我们的初步数据有力地表明，两种物理模式的变化， 基因组的三维结构--染色体分裂和条纹强化--调节细胞身份的转变。 因此，我们建议开发TADsplit和StripeDiff两个生物信息学工具包，以系统地定义 这些以及另外的样品之间的TAD和条纹的物理变化模式。作为服务的证明 原则上，我们将利用新技术来研究内皮细胞到 间充质转化（EndMT）是一种细胞身份转变，在正常发育和 和许多流行的心血管疾病。我们将阐明转录因子 调节基因组的3D结构以对抗EndMT。这些调查有可能更好地指导 EndMT在许多疾病的治疗中发挥重要作用。生物信息学新技术在 TADsplit和StripeDiff将使研究人员能够在不同的生物模型中研究3D基因组变化 发展和疾病。