Institute for Cellular and Molecular Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, USA.
PLoS One. 2012;7(6):e39383. doi: 10.1371/journal.pone.0039383. Epub 2012 Jun 19.
Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab's new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab's Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair.
共变分析用于识别 RNA 序列比对中具有相似序列变异模式的位置。这些对两个位置进化的约束通常与螺旋中的碱基对有关。尽管互信息 (MI) 已被用于准确预测 RNA 二级结构及其少数三级相互作用,但早期研究表明,系统发育事件计数方法更敏感,并为碱基对的预测提供额外的可信度。我们开发了一种新颖而强大的基于系统发育事件计数的方法 (PEC),用于定量 RNA 比较分析数据库 (rCAD) 中位置共变。PEC 和基于 MI 的方法各自识别独特的碱基对,并共同识别许多其他碱基对。总共,两种方法结合 N 最佳和螺旋扩展策略识别出最大数量的碱基对。虽然共变方法有效地准确预测了 RNA 的二级结构,但仅识别出少数三级结构碱基对。本文和 Gutell 实验室的比较 RNA 网络 (CRW) 站点的分析表明,这些碱基对中的大多数彼此之间不共变。然而,共变分析确实揭示了在三维 RNA 结构中彼此接近的核苷酸组之间存在较弱但显著的共变。这表明共变分析除了形成碱基对的两个核苷酸之外,还识别其他类型的结构约束。