An integrative genomic strategy to infer global RNA regulatory networks

项目总结 越来越多的人认识到，哺乳动物的生物复杂性被调节为极大地放大 RNA复杂性。这种调节是由数百种RNA结合蛋白(RBPs)和microRNAs介导的 (MiRNAs)通过与其目标的序列特异性相互作用。RNA的错误调节可能会导致 遗传病的数量。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了阻碍 由于高通量的不足，准确推断全球RNA调控网络的难度 实验技术和有效的计算方法。此应用程序描述了以下各项的组合 促进对体内RNA调控的理解的实验和计算方法 哺乳动物的大脑处于系统水平。在实验方面，我将利用老鼠的基因 洛克菲勒罗伯特·达内尔实验室建立的系统和高通量技术 大学，将在那里进行研究的指导阶段。Hits-Clip将用于生成 几种重要神经元限制性商业惯例的全基因组生化足迹图.外显子连接微阵列 和RNA-Seq将被用来生成核苷酸解析转录组图谱，用于比较分析 野生型大脑和缺乏个体限制性商业惯例的大脑，以及发育中的小鼠大脑。关于计算性的 另一方面，综合建模技术，如隐马尔可夫模型(HMM)和贝叶斯网络，将是 用来对生物化学、结构、基因组和进化信息进行概率建模 多个数据源，以便高度预测RBP/miRNA靶点和RNA调控模型 网络是可以开发的。从对RNA的分析中获得了大量的初步数据 Nova的剪接调控，这表明这种整合基因组策略对 定义准确和全面的RNA调控网络，并获得新的生物学见解。在这里，我 建议进一步改进这些方法，并将该战略推广到其他限制性商业惯例，无论是单独的还是 结合，以及RNA调节的其他步骤。在基因工程中发现的调控机制 工程系统将进一步扩展到研究组合和动态的RNA调控。 在不同的脑区发育大脑。虽然我在机器学习方面接受了广泛的培训 和对RNA调节的计算研究，这个职业发展奖将使我能够扩大我的 并继续发展我的实验技能。达内尔实验室的良好环境和 洛克菲勒大学不仅将极大地促进指导研究，而且将极大地促进我向 独立的学术立场。总之，拟议的研究将为我的未来铺平道路 旨在破译正常生物过程和人类体内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.