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: 1955712
• 负责人: Albert Cheng
• 金额: $ 43.39万
• 依托单位: Jackson Laboratory
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955712&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.

在更高级的生物体中，长的DNA链紧密地堆积在细胞核中以适应空间限制。由于DNA结构的不同部分不断被功能分子机器访问和操作，因此包装是高度动态的，并且受到仔细控制。在DNA和支持蛋白质结构的螺旋和解螺旋过程中，不同基因组区域之间发生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，捕获不断变化的多途径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，并开发相应的机器学习算法，以便从获取的数据中高效提取信息。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，通过新的超图聚类方法提取动态染色质社区签名，并确定局部和长-通过PageRank方法的适当概括来确定交互模式的范围。此外，将特别注意在不同的生物背景下解释研究结果。为了最大限度地发挥新算法方案的效用，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都将随时向公众开放。该奖项反映了NSF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。