An Integrative Computational Framework for DNA Hydroxymethylation Data Mining and Interpretation

项目概要/摘要 基于下一代测序（NGS）的分子方法的最新进展已经阐明了 基因组的等级组织，并已表明，表观基因组的变化可以促进或防止 转录因子（TF）进入特定DNA序列，在核之间移动基因 区室，并建立或去除相邻基因组区域之间的绝缘。作为变化， 表观基因组和染色质组织可以破坏精确的转录调节程序， 分化状态或诱导病理状态，李博士实验室的研究旨在提高我们的能力， 定义和理解这种变化在细胞中转录调控的多个层面上的影响。 该实验室已经有效地解决了DNA甲基化的调节作用，在其以前和正在进行的 工作，现在将其重点扩展到羟甲基化。5-羟甲基胞嘧啶（5 hmC），是一个关键的表观遗传 与转录激活有关的修饰;然而，迄今为止，5 hmC数据及其基因组特性 在有限整合不同基因组数据类型的情况下进行了评估。此外，没有综合 设计用于解释5-甲基胞嘧啶背景下5 hmC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。这 该提案将满足对用户友好，可解释和可扩展的工具的日益增长的需求，用于挖掘5 hmC数据 为表观基因组的基本机制研究奠定基础，并促进发现潜在的 疾病的治疗目标。基于研究者在揭示5 hmC及其动力学方面的进展， 基因调控的影响，该提案现在将开发用于5 hmC数据挖掘的创新计算工具 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， 胚胎干细胞（ESC）分化。未来五年的主要目标包括： 计算框架来挖掘短读和长读测序数据以回答以下问题：（1） 5 hmC如何促进表观遗传异质性？(2)5 hmC表观遗传异质性如何促成 到转录组异质性(3)5 hmC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？我们将联合收割机 学习和网络挖掘算法，使知识发现和数据集成，从不同的 基因组数据类型然后，我们将利用5 hmC数据挖掘框架来识别5 hmC模式， 与ESC分化、B细胞分化相关，并有助于癌症的适应性优势 细胞这项工作是有意义的，因为它将是第一次解剖5 hmC的贡献，本地和远程 表观遗传异质性和第一个揭示DNA之间串扰的计算框架 修饰和其他转录调节因子。总的来说，这项工作将产生 更全面地了解细胞状态和行为发生根本变化的分子事件。

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.