酵母中全转录组RNA和蛋白质组结合的高通量分析及研究

传统的RNA-蛋白质相互作用实验技术都仅仅局限于单个RNA结合蛋白所结合的RNA片段，是以某一个蛋白质为中心，研究会有哪些RNA和其相互结合并发生作用。随着现在学术界开始将重点转向对RNA尤其是新型的非编码RNA的研究和分析上，本项目的出发点和创新之处是突破性地以RNA为中心，去探索性地研究和整个蛋白质组的所有蛋白质相结合的RNA序列和结构。我们创新性地改进了PAR-CLIP这一研究RNA-蛋白质相互作用的高通量技术，并将同步开发与之匹配的最新生物信息算法。最终，我们将在整个基因组水平上，利用改进的高通量PAR-CLiP技术和生物信息算法，从酵母开始，在单核苷酸的高精度上测出 "每一个表达的RNA" 和 "全蛋白质组" 相结合的位点、一级序列motif、RNA二级结构以及生物功能。同时，我们将利用这些全新的信息革新已被广泛承认和使用的RNA二级结构算法。

RNA-protein interaction study previously focused on RNA-binding proteins (RBP). Usually, RNAs bound by a certain protein were pooled and profiled. Recently, people found that many RNAs, especially noncoding RNAs, function in different ways by interacting with different proteins. It is quite helpful to know which proteins a certain RNA molecule binds in order to infer the RNA's new functions. In this project, we propose to analyze the transcriptome-wide RNA structures and RNA sequence motifs bound by the whole proteome. We reinvent the PAR-CLIP method to pull down the RNA fragments bound by all kinds of proteins in yeast. And we are also developing the bioinformatics tools to analyze the new data to refine the binding profile. Subsequently, we would identify the transcriptome-wide protein binding RNA motifs on a single nucleotide level. We would use the binding features to build RNA-protein interaction network and infer RNAs' new functions in different pathways. Meanwhile, using the whole proteome-binding atlas in yeast, we can fundamentally improve the current RNA secondary structure prediction force field by including the protein interactions.