An Integrative Computational Framework for DNA Hydroxymethylation Data Mining and Interpretation

项目摘要/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防止 转录因子(TF)对特定DNA序列的访问，在核之间移动基因 隔间，并建立或移除相邻基因组区域之间的绝缘。随着 表观基因组和染色质组织可以破坏精确的转录调控程序来改变细胞 分化状态或诱导病理状态，李博士实验室的研究试图提高我们的能力 定义和理解这种变化在细胞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甲基化在其先前和正在进行的 工作，现在将其重点扩展到羟甲基化。5-羟甲基胞嘧啶(5hmC)是一种重要的表观遗传学 修饰与转录激活有关；然而，到目前为止，5hmC数据及其基因组属性 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，没有一个完整的 用于解释5-甲基胞嘧啶中5HmC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。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，以挖掘5hmC数据 为表观基因组的基本机制研究奠定基础并促进潜力的发现 疾病中的治疗靶点。以研究人员在揭示5HmC及其动力学方面的进展为基础 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、 和胚胎干细胞(ESC)分化。未来五年的主要目标包括开发 挖掘短读和长读测序数据的计算框架，以回答以下问题：(1) 5hmC如何促进表观遗传异质性？(2)5hmC表观异质性如何贡献？ 转录组的异质性？(3)5hmC水平和表观遗传异质性如何与组蛋白联系 修饰、增强子活性、染色质相互作用和染色质组织？我们将联合机器 学习和网络挖掘算法，支持来自不同领域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符合以下条件的5hmC模式 与胚胎干细胞分化、B细胞分化相关，并有助于癌症的健康优势 细胞。这项工作意义重大，因为它将是对5hmC对局地和远程的贡献的第一次剖析 表观遗传异质性和揭示DNA间串扰的第一个计算框架 通过染色质相互作用数据进行修饰和其他转录调节。总体而言，这项工作将产生 对细胞状态和行为发生根本变化的分子事件有了更全面的了解。

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.