Computational modeling of spatial genome organization and gene regulation

空间基因组组织和基因调控的计算模型

• 批准号: 10689214
• 负责人: Wenxiu Ma
• 金额: $ 37.83万
• 依托单位: UNIVERSITY OF CALIFORNIA RIVERSIDE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10689214
• 关键词: 3-Dimensional; Architecture; Base Sequence; Cell Line; Chromatin; Complement; Computer Models; Computing Methodologies; DNA Sequence; Data; Development; Disease; Face; Gene Expression; Gene Expression Regulation; Genes; Genome; Genome Stability; Genomics; Goals; Knowledge; Malignant Neoplasms; Maps; Mission; Play; Research; Resolution; Resources; Role; Statistical Methods; Structure; Techniques; Training; Variant; Work; cell type; chromosome conformation capture; deep sequencing; epigenomics; genetic variant; genome-wide; human disease; innovation; machine learning model; multidisciplinary; spatiotemporal

PROJECT SUMMARY/ABSTRACT The three-dimensional (3D) organization of the genome plays an essential role in genome stability, gene regulation, and many diseases, including cancer. The recent development of high-throughput chromatin conformation capture (Hi-C) and its variants provide an unprecedented opportunity to investigate higher-order chromatin organization. Despite the rapidly accumulating resources for investigating 3D genome organization, our understanding of the regulatory mechanisms and functions of the genome organization remain largely incomplete. Hi-C analyses and 3D genome research are still in their early stage and face several challenges. First, high-resolution chromatin contact maps require extremely deep sequencing and hence have been achieved only for a few cell lines. Second, it is computationally challenging to complement 3D genome structure with one-dimensional (1D) genomic and epigenomic features. Third, recent studies have just begun to infer associations between chromatin interactions and genetic variants and to identify potential target genes of those variants at the genome-wide scale. Given these challenges and my unique multi-disciplinary training, my long-term research goal is to develop innovative computational and statistical methods to uncover the interplay between 3D genome structure and function. Specifically, in the next five years, I will i) develop computational approaches to enhance the resolution of existing Hi-C data and investigate fine-scale 3D genome architecture as well as its spatiotemporal dynamics and ii) build scalable and interpretable machine learning models that leverage 1D epigenomic data to predict cell type-specific 3D chromatin interactions and gene expression and elucidate the function of 3D genome organization in gene regulation and human diseases. The completion of the proposed work will deepen our knowledge of 3D genome architecture as well as its functions in gene regulation and disease.

项目总结/文摘

项目成果

EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework.

• DOI: 10.1093/bioinformatics/btab272
• 发表时间: 2021-07-12
• 期刊: Bioinformatics (Oxford, England)
• 作者: Hu Y;Ma W
• 通讯作者: Ma W