Meyer Irmtraud M, Miklós István
UBC Bioinformatics Centre, University of British Columbia, Vancouver, British Columbia, Canada.
PLoS Comput Biol. 2007 Aug;3(8):e149. doi: 10.1371/journal.pcbi.0030149.
Computational methods for predicting evolutionarily conserved rather than thermodynamic RNA structures have recently attracted increased interest. These methods are indispensable not only for elucidating the regulatory roles of known RNA transcripts, but also for predicting RNA genes. It has been notoriously difficult to devise them to make the best use of the available data and to predict high-quality RNA structures that may also contain pseudoknots. We introduce a novel theoretical framework for co-estimating an RNA secondary structure including pseudoknots, a multiple sequence alignment, and an evolutionary tree, given several RNA input sequences. We also present an implementation of the framework in a new computer program, called SimulFold, which employs a Bayesian Markov chain Monte Carlo method to sample from the joint posterior distribution of RNA structures, alignments, and trees. We use the new framework to predict RNA structures, and comprehensively evaluate the quality of our predictions by comparing our results to those of several other programs. We also present preliminary data that show SimulFold's potential as an alignment and phylogeny prediction method. SimulFold overcomes many conceptual limitations that current RNA structure prediction methods face, introduces several new theoretical techniques, and generates high-quality predictions of conserved RNA structures that may include pseudoknots. It is thus likely to have a strong impact, both on the field of RNA structure prediction and on a wide range of data analyses.
预测具有进化保守性而非热力学稳定性的RNA结构的计算方法，近来已引起越来越多的关注。这些方法不仅对于阐明已知RNA转录本的调控作用不可或缺，对于预测RNA基因也是如此。要设计出能充分利用现有数据并预测可能包含假结的高质量RNA结构的方法，一直以来都极为困难。给定几条RNA输入序列，我们引入了一个全新的理论框架，用于共同估计包含假结的RNA二级结构、多序列比对和进化树。我们还在一个名为SimulFold的新计算机程序中实现了该框架，该程序采用贝叶斯马尔可夫链蒙特卡罗方法，从RNA结构、比对和树的联合后验分布中进行采样。我们使用这个新框架来预测RNA结构，并通过将我们的结果与其他几个程序的结果进行比较，全面评估我们预测的质量。我们还展示了初步数据，这些数据显示了SimulFold作为一种比对和系统发育预测方法的潜力。SimulFold克服了当前RNA结构预测方法面临的许多概念上的限制，引入了几种新的理论技术，并生成了可能包含假结的保守RNA结构的高质量预测。因此，它很可能对RNA结构预测领域以及广泛的数据分析产生重大影响。
