RNA secondary structure prediction using stochastic context-free grammars and evolutionary history
RNA secondary structure prediction using stochastic context-free grammars and evolutionary history
复制标题
DOI：
10.1093/bioinformatics/15.6.446
复制
发表时间：
1999-06-01
期刊：
影响因子：
5.8
通讯作者：
Hein, J
中科院分区：
文献类型：
--
作者：
Knudsen, B;Hein, J
动机：许多计算机化的RNA二级结构预测方法已经发展。然而，这些方法中很少有采用进化模型，因此结构测定中往往会留下相关信息。本文介绍了一种结合进化历史预测RNA二级结构的方法。这里报道的方法是基于随机上下文无关文法（SCFGs），以提供一个先验概率分布的structure.Results：系统发生树相关的序列可以找到最大似然（ML）估计从这里介绍的模型。由于突变模式，该树显示出关于结构的信息。包含RNA结构的先验分布确保了即使对于少量相关序列也能进行良好的结构预测。在贝叶斯方法中使用最大后验估计（MAP）估计进行预测。对于小序列集，该方法与当前的自动化方法相比表现非常好。
Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however; employ an evolutionary model, thus relevant information is often left our from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic context-free grammars (SCFGs) to give a prior probability distribution of structures.Results: The phylogenetic tree relating the sequences can be found by maximum likelihood (ML) estimation from the model introduced here. The tree is shown to reveal information about the structure, due to mutation patterns. The inclusion of a prior distribution of RNA structures ensures good structure predictions even for a small number of related sequences. Prediction is carried out using maximum a posteriori estimation (MAP) estimation in a Bayesian approach. For small sequence sets, the method performs very well compared to current automated methods.