A Bayesian statistical algorithm for RNA secondary structure prediction
A Bayesian statistical algorithm for RNA secondary structure prediction
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DOI：
10.1016/s0097-8485(99)00010-8
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发表时间：
1999-01-01
期刊：
影响因子：
--
通讯作者：
Lawrence, CE
中科院分区：
文献类型：
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作者：
Ding, Y;Lawrence, CE
描述了一种预测RNA二级结构的贝叶斯方法，该方法解决了以下三个开放问题：(1)需要表示可能结构的完整集合；(2)需要指定一组固定的能量参数；(3)希望对问题中的所有变量进行统计推断。最近的研究表明，贝叶斯推理可以用来放松或消除指定生物信息学递归算法参数的需要，并通过在参数值中加入不确定性来给出可能解的完整集合的统计表示。在本文中，我们对贝叶斯方法的这些潜在优势进行了初步探索。我们提出了一种基于堆叠能量规则的贝叶斯算法，但放松了对参数的指定。该算法返回不稳定环路、堆积能量矩阵和二级结构数量的准确后验分布。该算法从可能的二级结构的完整集成中生成统计上具有代表性的结构，其比例与后验概率完全成比例。一旦算法的前向递归完成，后向递归采样在O(N)时间内执行，为生成典型结构提供了一种非常有效的方法。我们用几个tRNA序列演示了贝叶斯方法的有效性。通过对大肠杆菌tRNA(ALA)序列和非洲爪哇卵母细胞5S rRNA序列的应用，说明了该方法在预测RNA二级结构和呈现替代结构方面的潜力。(C)1999爱思唯尔科学有限公司。保留所有权利。
A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence. (C) 1999 Elsevier Science Ltd. All rights reserved.