Computational methods for modeling lineage-specific gene regulation

描述(申请人提供)：复杂的感觉和信号通路最终汇聚在DNA上，形成相互作用的调控RNA和蛋白质的复合体，将特定的基因近端和远端调控元件与激活和抑制基因结合在一起，从而建立和维持细胞类型的特定转录反应。这些调控元件有静态和动态的成分--前者在基因组序列中编码为转录因子结合位点序列基序的组合，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的结果，以及远端调控元件与靶基因的动态链接。此外，转录和转录后反馈以及对调控水平的控制 蛋白质和RNA也在动态的细胞类型特异性基因调控中发挥关键作用。现代基因组学的核心挑战之一是学习基因组和表观基因组编码调控信息的这些规则。对于不同的细胞类型和不同的时间点，单个基因组序列如何编码精确而又高度独特的基因调控程序的信息？什么是关键的序列决定因素，以及在任何特定条件下决定任何给定DNA序列的功能的语法规则？在这个项目中，我们寻求使用路线图表观基因组学项目和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共享的和独特的调节功能，特别是转录因子结合，跨相关的细胞类型。同时，我们将实施新的方法来识别高分辨率、上下文特定的动态调控元件，破译其潜在的调控序列语法，并学习转录调控的预测性、集成性模型，以破译异质调控成分对谱系特异性基因表达动力学的影响，并为大量精选的和疾病相关的基因集提供调控注释。

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.