An integrative genomic strategy to infer global RNA regulatory networks

项目摘要 越来越多的人认识到，哺乳动物的生物复杂性通过调节 RNA复杂性这种调节由数百种RNA结合蛋白（RBP）和microRNA介导。 （miRNAs）通过与其靶标的序列特异性相互作用。RNA的错误调节可以导致 遗传疾病的数量。尽管它们发挥着关键作用，但研究RNA复杂性的努力在很大程度上受到阻碍 由于高通量的缺陷，难以准确地推断全球RNA调控网络， 实验技术和有效的计算方法。本申请描述了以下的组合： 实验和计算方法，以促进体内RNA调控的理解， 哺乳动物大脑的系统水平。在实验方面，我将利用老鼠的遗传 在洛克菲勒的罗伯特·达内尔实验室建立的系统和高通量技术 大学，在那里指导阶段的研究将进行。HITS-CLIP将用于生成 几种重要神经元RBP的生化足迹的全基因组图谱;外显子连接微阵列 和RNA-Seq将用于生成核苷酸分辨率转录组图谱，用于比较分析 野生型脑和缺乏个体RBP的脑，以及发育中的小鼠脑。对计算 另一方面，综合建模技术，如隐马尔可夫模型（HHRM）和贝叶斯网络，将 用于对生物化学、结构、基因组和进化信息进行概率建模， 多个数据源，因此RBP/miRNA靶位点和RNA调控的高度预测模型 网络可以发展。从RNA分析中获得了大量的初步数据， 通过Nova的剪接调节，这证明了这种整合基因组策略的有效性， 定义准确和全面的RNA调控网络，并获得新的生物学见解。这里我 建议进一步改进这些方法，并将该战略扩展到其他限制性商业惯例， 组合和RNA调节的其他步骤。基因组中发现的调控机制 工程系统将进一步扩展到研究组合和动态RNA调控， 发育中的大脑和不同的大脑区域。虽然我在机器学习方面接受了广泛的培训， 和RNA调控的计算研究，这个职业发展奖将使我能够扩大我的 继续发展我的实验技能。达内尔实验室的优良环境， 洛克菲勒大学将大大促进不仅指导研究，而且我的过渡到一个 独立的学术地位。这项研究将为我的未来铺平道路 旨在解码正常生物过程和人类中RNA调控规则的研究 疾病

Project summary It is increasingly recognized that mammalian biologic complexity is amplified enormously by the regulation of RNA complexity. This regulation is mediated by hundreds of RNA-binding proteins (RBPs) and microRNAs (miRNAs) through sequence-specific interactions with their targets. Misregulation of RNAs can cause a number of genetic diseases. Despite their critical roles, efforts to study RNA complexity are largely impeded by the difficulty to accurately infer global RNA-regulatory networks, due to deficiencies of high-throughput experimental technologies and effective computational methods. This application describes a combination of experimental and computational approaches to advance the understanding of in vivo RNA regulation in mammalian brains at the systems level. On the experimental side, I will take advantage of the mouse genetic systems and high-throughput technologies established in the Robert Darnell laboratory at Rockefeller University, where the mentored phase of research will be performed. HITS-CLIP will be used to generate genome-wide maps of biochemical footprints of several important neuronal RBPs; exon-junction microarrays and RNA-Seq will be used to generate nucleotide-resolution transcriptome profiles for comparative analysis of wild type brains and brains lacking individual RBPs, and of developing mouse brains. On the computational side, integrative modeling techniques, such as hidden Markov models (HMMs) and Bayesian networks, will be employed to probabilistically model biochemical, structural, genomic, and evolutionary information from multiple data sources, so that highly predictive models of RBP/miRNA target sites and RNA-regulatory networks can be developed. Substantial preliminary data have been obtained from the analysis of RNA splicing regulation by Nova, which demonstrates the effectiveness of such an integrative genomic strategy to define accurate and comprehensive RNA-regulatory networks and obtain novel biological insights. Here I propose to further improve these methods, and extend the strategy to other RBPs, individually or in combination, and other steps of RNA regulation. The regulatory mechanisms discovered in genetically engineered systems will be further extended to study the combinatorial and dynamic RNA regulation in developing brains and in different brain regions. While I have received extensive training in machine learning and computational studies of RNA regulation, this career development award will allow me to expand my existing skills and continue to develop my experimental skills. The excellent environment in the Darnell lab and Rockefeller University will greatly facilitate not only the mentored research, but also my transition to an independent academic position. Together, the proposed study will pave the road to launch my future investigations that aim to decode rules governing RNA regulation in normal biological processes and human diseases.