长链非编码RNA(lncRNA)参与调控多种细胞生理过程，RNA结合蛋白(RBP)能介导lncRNA到特定亚细胞结构发挥功能,但其作用机制有待进一步研究。本项目拟通过整合多组学数据和分子生物学实验，研究RBP介导lncRNA亚细胞定位的机制。用机器学习算法筛选对lncRNA亚细胞定位有重要作用的RBP；用序列富集算法筛选RBP结合位点邻近序列中对lncRNA亚细胞定位有重要作用的特征序列和RNA二级结构；依据特征序列，用REL-seq和RNA-Protein pull down技术解析RBP介导lncRNA亚细胞定位的机制；并根据决定lncRNA亚细胞定位的特征序列和二级结构，构建lncRNA亚细胞定位预测模型。本研究将为lncRNA如何定位到特定亚细胞结构提供新的理论依据,将有助于深入理解lncRNA在疾病发病过程中的作用，为设计、开发新药物及制定新治疗方法提供基础。

Long noncoding RNA (lncRNA) plays a vital role in various biological processes. RNA binding protein (RBP) binds to lncRNAs and directs them to act their functions in specific subcellular localizations. However, the underlying mechanism needs further investigation. In this proposal, we intend to integrate multi-omics datasets and molecular experiments to study the mechanism of RBP-mediated lncRNA subcellular localization. We propose to screen RBPs that may play an essential role in lncRNA subcellular localization using machine learning algorithms. To retrieve enriched sequence features and secondary structures by analyzing the franking of region of RBP binding sites; by leveraging the REL-seq and RNA-Protein pull down techniques, we intend to decipher the mechanism of lncRNA subcellular localization; we plan to construct a machine learning model to predict lncRNA subcellular localization by integrating the interactions between RBPs and sequence features. This study will provide new insights into how lncRNAs function through interacting with RBPs. Our research will benefit for the functional studies of lncRNAs in human disease and provide a theoretical basis for the development of new therapeutic strategies.