Computational methods for modeling lineage-specific gene regulation

谱系特异性基因调控建模的计算方法

• 批准号: 8815902
• 负责人: JONATHAN K PRITCHARD
• 金额: $ 31.88万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-05 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8815902
• 关键词: Beryllium; Binding; Binding Sites; Cell Line; Chromatin; Collection; Communities; Complex; Computer software; Computing Methodologies; DNA; DNA Sequence; Data; Data Analyses; Development; Disease; Distal; Elements; Feedback; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genome; Genomics; Goals; Imagery; Indium; Joints; Knowledge; Lead; Learning; Link; Machine Learning; Maintenance; Methods; Modeling; Modification; Play; Process; RNA-Binding Proteins; Regulatory Element; Regulatory Pathway; Research Personnel; Resolution; Resources; Sensory; Signal Pathway; Statistical Methods; Techniques; Time; Tissues; Transcriptional Regulation; cell type; combinatorial; epigenome; epigenomics; experience; functional genomics; genetic regulatory protein; genome sequencing; insight; novel; programs; public health relevance; response; tool; transcription factor

DESCRIPTION (provided by applicant): Complex sensory and signaling pathways ultimately converge on the DNA in the form of complexes of interacting regulatory RNAs and proteins that bind specific gene-proximal and distal regulatory elements to active and repress genes resulting in the establishment and maintenance of cell-type specific transcriptional responses. These regulatory elements have static and dynamic components - the former encoded in the genomic sequence itself as combinations of transcription factor binding site sequence motifs and the latter a consequence of locus and cell-type specific chromatin accessibility and epigenomic modifications as well as the dynamic linking of distal regulatory elements to target genes. In addition, transcriptional and post-transcriptional feedback and control of the levels of regulatory proteins and RNAs also play a key role in dynamic cell-type specific gene regulation. One of the central challenges in modern genomics is to learn these rules by which the genome and epigenome encode regulatory information. How does a single genome sequence encode the information for exquisitely precise, and yet highly distinctive programs of gene regulation for different cell types, and at different time points? What are the key sequence determinants, and grammatical rules that determine the function of any given DNA sequence under any particular set of conditions? In this project we seek to use public data from The Roadmap Epigenomics project and The ENCODE project to make progress on these questions. We propose to build methods for interpreting shared and distinctive regulatory features, including especially transcription factor binding, across related cell types. In parallel, we will implement novel methods for identifying high-resolution, context-specific dynamic regulatory elements, decipher their underlying regulatory sequence grammars and learn predictive, integrative models of transcriptional regulation to decipher the effects of heterogeneous regulatory components on lineage-specific gene expression dynamics and provide regulatory annotations for large collections of curated and disease-associated gene sets.

描述（由申请人提供）：复杂的感觉和信号传导途径最终以相互作用的调节RNA和蛋白质的复合物的形式汇聚在DNA上，这些复合物将特定的基因近端和远端调节元件结合到活性和抑制基因上，从而导致建立和维持细胞类型特异性转录反应。这些调控元件具有静态和动态成分——前者作为转录因子结合位点序列基序的组合在基因组序列本身中编码，后者是基因座和细胞类型特异性染色质可及性和表观基因组修饰以及动态连接的结果。靶基因的远端调控元件。此外，转录和转录后反馈以及调节水平的控制 蛋白质和 RNA 在动态细胞类型特异性基因调控中也发挥着关键作用。现代基因组学的核心挑战之一是了解基因组和表观基因组编码调控信息的规则。单个基因组序列如何编码信息，为不同细胞类型和不同时间点提供极其精确且高度独特的基因调控程序？决定任何给定 DNA 序列在任何特定条件下的功能的关键序列决定因素和语法规则是什么？在这个项目中，我们寻求使用来自 The Roadmap Epigenomics 项目和 The ENCODE 项目的公共数据来在这些问题上取得进展。我们建议建立方法来解释相关细胞类型之间共享和独特的调控特征，特别是转录因子结合。与此同时，我们将实施新方法来识别高分辨率、特定于上下文的动态调控元件，破译其潜在的调控序列语法，并学习转录调控的预测性、整合模型，以破译异质调控成分对谱系特异性基因表达的影响动态并为大量精选的和疾病相关的基因集提供调控注释。