An Integrative Computational Framework for DNA Hydroxymethylation Data Mining and Interpretation

DNA 羟甲基化数据挖掘和解释的综合计算框架

基本信息

批准号：
10707018
负责人：
Sheng Li
金额：
$ 47.25万
依托单位：
JACKSON LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10707018
关键词：
Address Algorithms B cell differentiation Behavior Cell Differentiation process Cells Chromatin Communication DNA DNA Methylation DNA Modification Process DNA Sequence Data Data Analyses Data Set Disease Dissection Enhancers Epigenetic Process Event Foundations Gene Expression Gene Expression Regulation Genes Genetic Transcription Genome Genomic Segment Goals Heterogeneity Knowledge Discovery Laboratories Lead Link Machine Learning Methods Mining Modification Molecular Nuclear Pathologic Pattern Property Public Health Research Research Personnel Role Transcriptional Activation Transcriptional Regulation Work cancer cell cancer stem cell cell behavior cell type computer framework computerized tools data integration data mining design embryonic stem cell epigenome fitness genomic data histone modification improved innovation next generation sequencing prevent programs stem cell differentiation therapeutic target tool transcription factor transcriptome user-friendly

项目摘要

PROJECT SUMMARY/ABSTRACT Recent advances in next generation–sequencing (NGS)-based molecular methods have illuminated the hierarchical organization of the genome and have shown that changes in the epigenome can promote or prevent the access of transcription factors (TFs) to specific DNA sequences, move genes between nuclear compartments, and build or remove the insulation between neighboring genomic regions. As changes in the epigenome and chromatin organization can derail precise transcriptional regulatory programs to change cell differentiation status or induce a pathological state, research in Dr. Li’s laboratory seeks to improve our ability to define and understand the impact of such changes across multiple layers of transcriptional regulation in the cell. The laboratory has effectively addressed the regulatory roles of DNA methylation in its previous and ongoing work and now extends its focus to hydroxymethylation. 5-hydroxymethylcytosine (5hmC), is a key epigenetic modification linked to transcriptional activation; however, 5hmC data and its genome properties have thus far been evaluated with limited integration of different genomic data types. Moreover, there is no integrative computational framework designed to interpret the functional role of 5hmC in the context of 5-methycytosine (5mC), enhancer activities, chromatin interactions, gene expression data, and DNA sequence information. This proposal will fill the growing need for user-friendly, interpretable, and extendable tools for mining 5hmC data toward laying a foundation for basic mechanistic studies of the epigenome and facilitate discovery of potential therapeutic targets in disease. Building on the investigator’s progress in revealing the dynamics of 5hmC and its impact on gene regulation, the proposal will now develop innovative computational tools for 5hmC data mining and data integration with other NGS datasets, with a focus on applying these tools to B cell differentiation, cancer, and embryonic stem cell (ESC) differentiation. Key goals over the next five years include developing a computational framework to mine short- and long-read sequencing data to answer the following questions: (1) How does 5hmC contribute to epigenetic heterogeneity? (2) How does 5hmC epigenetic heterogeneity contribute to transcriptome heterogeneity? (3) How do 5hmC levels and epigenetic heterogeneity communicate with histone modifications, enhancer activities, chromatin interactions, and chromatin organization? We will combine machine learning and network mining algorithms to enable knowledge discovery and data integration from diverse genomic data types. We will then harness the 5hmC data-mining framework to identify 5hmC patterns that correlate with ESC differentiation, B cell differentiation, and that contribute to the fitness advantage of cancer cells. This work is significant because it will be the first dissection of 5hmC’s contribution to local and long-range epigenetic heterogeneity and the first computational framework to uncover the cross-talk between DNA modifications and other transcriptional regulators via chromatin interaction data. Collectively, this work will yield a fuller picture of the molecular events that underlie fundamental changes in cell state and behavior.

项目概要/摘要基于下一代测序（NGS）的分子方法的最新进展阐明了基因组的分层组织，并表明表观基因组的变化可以促进或预防转录因子 (TF) 接触特定 DNA 序列，在核之间移动基因区室，并随着基因组区域的变化而建立或消除相邻基因组区域之间的隔离。表观基因组和染色质组织可能会破坏精确的转录调控程序以改变细胞分化状态或诱发病理状态，李博士实验室的研究旨在提高我们的能力定义并理解此类变化对细胞多层转录调控的影响。该实验室在之前和正在进行的研究中有效地解决了 DNA 甲基化的调控作用。工作，现在将其重点扩展到羟甲基化（5hmC），它是一个关键的表观遗传学。与转录激活相关的修饰；然而，迄今为止，5hmC 数据及其基因组特性尚未确定。通过不同基因组数据类型的有限整合进行评估此外，没有整合。旨在解释 5hmC 在 5-甲基胞嘧啶背景下的功能作用的计算框架 (5mC)、增强子活性、染色质相互作用、基因表达数据和 DNA 序列信息。该提案将满足对用户友好、可解释和可扩展的 5hmC 数据挖掘工具日益增长的需求为表观基因组的基本机制研究奠定基础并促进潜在的发现以研究人员在揭示 5hmC 及其动态方面取得的进展为基础。对基因调控的影响，该提案现在将为 5hmC 数据挖掘开发创新的计算工具以及与其他 NGS 数据集的数据集成，重点是将这些工具应用于 B 细胞分化、癌症、未来五年的主要目标包括开发胚胎干细胞（ESC）分化。挖掘短读长和长读长测序数据的计算框架，以回答以下问题：(1) 5hmC 如何影响表观遗传异质性？ (2) 5hmC 表观遗传异质性如何影响？ (3) 5hmC 水平和表观遗传异质性如何与组蛋白沟通？修饰、增强子活性、染色质相互作用和染色质组织？我们将结合机器？学习和网络挖掘算法，以实现来自不同领域的知识发现和数据集成然后，我们将利用 5hmC 数据挖掘框架来识别 5hmC 模式。与 ESC 分化、B 细胞分化相关，有助于癌症的健康优势这项工作意义重大，因为它将是首次剖析 5hmC 对局部和远程的贡献。表观遗传异质性和第一个揭示 DNA 之间串扰的计算框架总的来说，这项工作将通过染色质相互作用数据产生修饰和其他转录调节因子。更全面地了解细胞状态和行为发生根本变化的分子事件。