Computational methods for modeling lineage-specific gene regulation

描述（由申请人提供）：复杂的感觉和信号通路最终以相互作用的调控rna和蛋白质复合物的形式汇聚到DNA上，这些rna和蛋白质结合特定的基因-近端和远端调控元件来激活和抑制基因，从而建立和维持细胞类型特异性转录反应。这些调控元件有静态和动态组成部分——前者编码在基因组序列中，是转录因子结合位点序列基序的组合，后者是位点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连接。此外，还存在转录和转录后反馈及调控水平的调控

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.