Discovery of structural RNAs involved in human health and disease

许多基本的细胞功能依赖于通过进化而保守的各种RNA结构， 其他功能性RNA结构有望被发现。保守RNA结构的特征是 在配对位置显示保留碱基的相关替换（共变）的比对中发现 汇率这种进化信号既可以用来预测RNA结构，也可以用来鉴定新的保守RNA。 最近的出版物和初步结果取得了三个重要进展：统计协变 该检验识别由于遗传学而导致的背景协变上的显著协变。本次测试，实施 在一种称为R-scape（RNA结构协变高于系统发育预期）的方法中， 并控制假阳性预测的比率。最近发表的协变计算的功率， 这识别出具有功率（变异）但无显著协变的“负”对，不太可能形成RNA碱基对。 提出了一种新的级联折叠算法CaCoFold（Cascade covariation/variation Constrained Folding） 同样是最近发表的，它将所有积极和消极的进化信息结合到复杂的结构中， 包括所有类型的伪结和三重结。在人类中，这些进步的有效性已经通过AC测试， 精确预测人类非编码RNA MALAT 1和端粒酶RNA的结构，并通过推断 非编码RNA HOTAIR和XIST不具有保守结构。 这三个进步给了我们一个竞争优势，执行无偏见的基因组范围内的屏幕，为CON- 为脊椎动物中的结构RNA提供准确的3D结构预测。既往脊椎动物筛查 已经被数以千计的假阳性预测所阻碍。相比之下，我们的新协变 统计测试允许控制假阳性率。R-scape已经被用来寻找结构， 在细菌和病毒中的天然RNA。我们最近在真菌中的真核试验筛选已经鉴定了艾德17种新的结构 RNA。我们假设，许多与人类健康和疾病有关的结构RNA仍有待研究。 我们发现，我们现在有工具来发现和表征这些RNA。 该提案有三个具体目标，将推进结构RNA生物学的研究，并发现 一系列涉及RNA结构的新生物学机制。第一个目标是提出系统的全基因组 在人类中寻找新的保守的脊椎动物RNA结构。第二个目标是将联合收割机 机器学习中革命性的3D结构预测方法，CaCoFold使用的信号转化为 最先进的RNA折叠方法，用于准确预测3D RNA结构。第三个目标是介绍一个 鉴定超保守脊椎动物UTR中RNA结构的方法，其中没有共变信号， 我们现在的方法缺乏力量我们希望我们的工作将揭示灵长类特有的新监管机制， nisms。发现具有与疾病相关的致病变体的新型人类RNA结构将优先用于 实验验证。

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.