Collaborative Research: CIF: Medium: New Methods for Learning on Hypergraphs for Single-Cell Chromatin Data Analysis

合作研究：CIF：Medium：用于单细胞染色质数据分析的超图学习新方法

• 批准号: 2229306
• 负责人: Albert Cheng
• 金额: $ 43.39万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229306&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Medium; New

In higher-order organisms, long DNA strands are tightly packed in the cell nucleus to accommodate spatial constraints. As different parts of the DNA structure are continuously accessed and operated on by a machinery of functional molecules, the packing is highly dynamic and carefully controlled. During the process of coiling and uncoiling of the DNA and supporting protein structures, important physical interactions between different genomic regions take place. These so-called chromatin interactions regulate cellular functions and aid in responding to external biological signals. Capturing evolving multiway chromatin interaction patterns is therefore of crucial importance for understanding genetic regulatory mechanisms and genomic network modules. This project aims to advance platforms for measuring single-cell chromatin interactions and develop accompanying machine learning algorithms that enable efficient information extraction from the acquired data.The specific machine learning questions to be addressed include denoising and imputing multiway chromatin measurements, extracting dynamic chromatin community signatures via new hypergraph clustering methods and determining local and long-range interactions patterns through appropriate generalizations of PageRank methods. Furthermore, special attention will be placed on interpreting the findings within different biological contexts. To maximize the utility of the new algorithmic schemes, all relevant implementations supporting single-cell chromatin interaction data mining and analysis will be made readily available to the public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在高级生物体中，长DNA链被紧密地包裹在细胞核中以适应空间限制。由于DNA结构的不同部分被功能分子的机器不断地接触和操作，因此包装是高度动态和精心控制的。在DNA和支持蛋白质结构的盘绕和展开过程中，不同基因组区域之间发生了重要的物理相互作用。这些所谓的染色质相互作用调节细胞功能并帮助响应外部生物信号。因此，捕捉进化的多路染色质相互作用模式对于理解遗传调控机制和基因组网络模块至关重要。该项目旨在推进测量单细胞染色质相互作用的平台，并开发配套的机器学习算法，以便从获取的数据中有效地提取信息。需要解决的具体机器学习问题包括去噪和输入多路染色质测量值，通过新的超图聚类方法提取动态染色质群落特征，以及通过适当的PageRank方法概括确定本地和远程相互作用模式。此外，将特别注意在不同的生物学背景下解释这些发现。为了使新算法方案的效用最大化，所有支持单细胞染色质相互作用数据挖掘和分析的相关实现将随时向公众提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。