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

• 批准号: 10210409
• 负责人: Sheng Li
• 金额: $ 47.25万
• 依托单位: JACKSON LABORATORY
• 依托单位国家: 美国
• 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/10210409
• 关键词: Address; Algorithms; B cell differentiation; Behavior; Cell Differentiation process; Cells; Chromatin; 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; Malignant Neoplasms; Methods; Mining; Modification; Molecular; Nuclear; Pathologic; Pattern; Property; Public Health; Research; Research Personnel; Role; Transcriptional Activation; Transcriptional Regulation; Work; base; cancer 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; learning network; next generation sequencing; prevent; programs; stem cell differentiation; therapeutic target; tool; transcription factor; transcriptome; user-friendly

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.

项目摘要/摘要 下一代 - 基于（NGS）的分子方法的最新进展已经阐明了 基因组的等级组织，并表明表观基因组的变化可以促进或预防 转录因子（TFS）访问特定DNA序列，在核之间移动基因 隔室，并在相邻基因组区域之间建立或去除绝缘层。随着变化 表观基因组和染色质组织可能会使精确的转录调节程序更改细胞 分化状况或诱导病理状态，Li博士实验室的研究旨在提高我们的能力 定义并了解细胞中多个转录调控层的这种变化的影响。 该实验室有效地解决了DNA甲基化在其以前和正在进行的持续的调节作用 工作，现在将其重点扩展到羟甲基。 5-羟基甲基胞嘧啶（5HMC）是关键的表观遗传学 与转录激活相关的修改；但是，到目前为止，5HMC数据及其基因组特性已经 对不同基因组数据类型的集成有限进行评估。而且，没有集成 计算框架旨在解释5HMC在5-甲基环肽中的功能作用 （5MC），增强剂活性，染色质相互作用，基因表达数据和DNA序列信息。这 提案将满足对挖掘5HMC数据的用户友好，可解释和可扩展工具的日益增长的需求 为表观基因组的基本机械研究奠定基础，并促进潜力 疾病的治疗靶标。基于调查员在揭示5HMC及其动力学方面的进步基础 对基因调节的影响，该提案现在将开发用于5HMC数据挖掘的创新计算工具 以及与其他NGS数据集集成的数据集成，重点是将这些工具应用于B细胞分化，癌症， 和胚胎干细胞（ESC）分化。接下来五年的关键目标包括开发 计算框架以挖掘简短和长阅读测序数据以回答以下问题：（1） 5HMC如何促进表观遗传异质性？ （2）5HMC表观遗传异质性如何贡献 转录组异质性？ （3）5HMC水平和表观遗传异质性如何与组蛋白通信 修改，增强剂活动，染色质相互作用和染色质组织？我们将组合机器 学习和网络挖掘算法以实现潜水员的知识发现和数据集成 基因组数据类型。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，这有助于癌症的适应性优势 细胞。这项工作很重要，因为它将是5HMC对本地和远程的贡献的第一次解剖 表观遗传异质性和第一个揭示DNA之间串扰的计算框架 通过染色质相互作用数据进行修改和其他转录调节器。总的来说，这项工作将产生 构成细胞状态和行为基本变化的分子事件的更全面图景。