Discovery of structural RNAs involved in human health and disease

许多基本的细胞功能依赖于各种通过进化而保守的RNA结构，以及 其他功能的RNA结构有望被发现。保守的RNA结构的特征是 在配对位置显示保留碱基的相关替换(协变)的比对中发现 一对。这种进化信号既可用于预测RNA结构，也可用于识别新的保守RNA。 最近的出版物和初步结果取得了三个重要进展：统计上的协变性 检验IdentifiEs Signalfi不能因系统发育而导致背景协变。这项测试，已实施 在一种名为R-SCAPE(高于系统发育预期的RNA结构协同变异)的方法中，提供了信息 并控制假阳性预测率。最近发表的协变乘方计算， 同源fiEs“负”与幂(变异)但标记fi不能协变，不可能形成rna碱基对。 一种新的级联折叠算法--CaCoFold(级联协变/变差约束折叠) 也是最近发表的，它将所有积极和消极的进化信息组合成复杂的结构 包括所有类型的伪结和三胞胎。在人类身上，这些进步的EFfi的准确性已经通过了AC-ACT的测试。 准确预测人类非编码RNA MALAT1和端粒酶RNA的结构，并通过推断 非编码RNA HOTAIR和XIST没有保守结构。 这三项进展为我们提供了竞争优势，可以进行无偏见的全基因组筛查。 为脊椎动物的结构RNA提供准确的3D结构预测。以前的脊椎动物屏幕 因为结构RNA已经被成千上万的假阳性预测所阻碍。相比之下，我们的新协变 统计测试可以控制假阳性的比率。R-Scale已经被用于fi和Struc. 细菌和病毒中的结构RNA。我们最近在真菌中进行的真核实验筛选已经鉴定出17种新的结构。 RNA。我们假设，许多对人类健康和疾病有影响的结构RNA仍将是 发现，我们现在有了工具来fi和表征这些RNA。 这一建议有三个特定的目的，将推进结构fi生物学的研究，并发现。 涉及RNA结构的新生物机制的研究进展。fi的第一个目标是在全基因组范围内提出系统化的 寻找人类新的保守脊椎动物fi结构。第二个目标是提出将 机器学习中革命性的3D结构预测方法，将CaCoFold使用的信号转化为 用于准确预测3D RNA结构的最先进的RNA折叠方法。第三个目标是引入一个 一种在没有协变信号的超保守脊椎动物UTRs中鉴定RNA结构的方法， 而我们目前的方法缺乏力量。我们预计我们的工作将揭开灵长类物种fic新的调控机制的面纱。 尼斯主义。被发现具有与疾病相关的因果变异的新的人类RNA结构将优先用于 实验验证fi阳离子。

Many fundamental cellular functions depend on a variety of RNA structures conserved through evolution, and other functional RNA structures are expected to be discovered. A signature of a conserved RNA structure is found in alignments where paired positions display correlated substitutions (covariation) that preserve the base pair. This evolutionary signal can be used both to predict RNA structure and to identify new conserved RNAs. Recent publications and preliminary results have made three important advances: A statistical covariation test that identifies significant covariation over background covariation due to phylogeny. This test, implemented in a method called R-scape (RNA Structural Covariation Above Phylogenetic Expectation), provides information and control over the rate of false positive predictions. A power of covariation calculation, recently published, that identifies “negative” pairs with power (variation) but insignificant covariation, unlikely to form RNA base pairs. A new cascading folding algorithm, named CaCoFold (Cascade covariation/variation Constrained Folding) also recently published, that combines all positive and negative evolutionary information into complex structures including all types of pseudoknots and triplets. In human, the efficacy of these advances has been tested by ac- curately predicting the structures of the human non-coding RNAs MALAT1 and telomerase RNA, and by inferring that the non-coding RNAs HOTAIR and XIST do not have a conserved structure. These three advances give us a competitive advantage to perform unbiased genome-wide screens for con- served structural RNAs in vertebrates with accurate 3D structure prediction. Previous vertebrate screens for structural RNAs have been hindered by thousand of false positive predictions. In contrast, our new covariation statistical test allows for controlling the rate of false positives. R-scape has already been used to find struc- tural RNAs in bacteria and viruses. Our recent eukaryotic pilot screen in fungi has identified 17 novel structural RNAs. We hypothesize that many structural RNAs with implications for human health and disease are still to be discovered, and that we now have the tools to find and characterize these RNAs. This proposal has three specific aims that will advance the study of structural RNA biology, and the discov- ery of novel biological mechanisms involving RNA structures. The first aim proposes systematic genome-wide searches to find novel conserved vertebrate RNA structures in human. The second aim proposes to combine revolutionary 3D structure prediction methods in machine learning with the signals used by CaCoFold into a state of the art RNA folding method for the accurate prediction of 3D RNA structures. The third aim introduces a method to identify RNA structures in ultra conserved vertebrate UTRs where there is no covariation signal, and our current method lacks power. We expect our work will unveil primate-specific novel regulatory mecha- nisms. Novel human RNA structures found to have causal variants associated with disease will be prioritized for experimental verification.