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High-throughput Discovery of Novel Genome Organization Regulators

新型基因组组织调节因子的高通量发现

基本信息

批准号：
10777403
负责人：
Bo Xia
金额：
$ 29.27万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-20 至 2024-09-19
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10777403
关键词：
3-Dimensional Address Affect Algorithms Binding Binding Proteins Binding Sites Boundary Elements CCCTC-binding factor Candidate Disease Gene Categories Cell Line Chromatin Chromatin Loop Clustered Regularly Interspaced Short Palindromic Repeats Complex Consumption DNA Disease Elements Enhancers Frequencies Funding Future Gene Expression Gene Expression Regulation Genes Genetic Genetic Screening Genome Hi-C Human Human BioMolecular Atlas Program Interphase Interphase Chromosome Investigation Knock-out Knowledge Measures Mediating Nature Network-based Neural Network Simulation Pilot Projects Process Regulation Regulatory Element Reporter Resolution Resources Standardization Structure System T-Lymphocyte Technology United States National Institutes of Health Validation Work YY1 Transcription Factor acute T-cell lymphoblastic leukemia cell cell type chromosome conformation capture cohesin deep neural network denoising design epigenomics experimental study follow-up genomic data in silico multimodality novel programs promoter reverse genetics scale up screening

项目摘要

ABSTRACT CTCF binding at its convergent-orientated DNA motifs has been implicated in establishing TAD boundary. CTCF protein regulates the genome organization through cohesin complex-mediated loop extrusion mechanism. While a few more factors have been recently discovered to regulate genome organization, such as NIPBL, WAPL, YY1, ZNF143, and MAZ, it is still far from a comprehensive mechanistic understanding of how the genome is organized. Discovering novel regulators of genome organization is still challenging due to the intensive nature of chromatin conformation capture technologies. To address the technical challenge in measuring genome organization, we have recently demonstrated that a deep neural network approach can enable de novo prediction of cell type- specific chromatin organization at high resolution. Moreover, this deep neural network model enables high- throughput in silico genetic screen (ISGS) for identifying cell type-specific DNA elements that are important for chromatin interactions. To fully unlock the discovery potential of this deep neural network-based ISGS approach, here we propose to leverage the NIH Common Fund-supported large-scale genomic data across human bio- samples for discovering novel regulators in 3D genome organization. We will predict a list of high-confidence trans-acting regulators, and experimentally validate 3-5 top hits in pilot studies to generate cross-cutting hypotheses for future research in 3D genome regulation.

摘要