Analysis of stochastic context-free grammars for RNA secondary structure prediction

该项目的目标是对用于RNA二级结构建模的随机上下文无关文法进行严格的数学分析。重点分析了预测对产生式规则类型和所使用的概率参数的依赖性，目的是更好地理解在语法设计过程中所做选择的局限性。这些结果应该会提出修改语法的方法，从而改善预测。在过去的几十年里，科学上认为RNA分子是遗传密码的被动传递者的观点发生了巨大变化，已知的RNA生物功能在数量上继续增长，范围不断扩大。为了执行必要的功能，RNA核苷酸链必须折叠成特定的三维功能形状。因此，了解RNA执行其功能的方式与了解其结构密切相关。由于实验测定结构昂贵且耗时，因此预测RNA分子结构的问题是计算生物学中的一个重要问题。本项目将研究基于随机上下文无关文法的RNA结构预测的计算方法，以及改变模型的不同组成部分对预测精度的影响。

The objective of the project is a rigorous mathematical analysis of stochastic context-free grammars for RNA secondary structure modeling. An accent is put on the analysis of the dependency of the prediction on the type of production rules and the probability parameters used, with the goal of better understanding the limitations of the choices made during the grammar design. The results should suggest ways of modifying the grammars that would lead to improvement in the prediction.The scientific view of RNA molecules as passive transmitters of the genetic code has drastically changed during the last few decades and the known biological functions of RNA continue to grow in number and expand in scope. To perform the necessary function, the RNA nucleotide chain must fold into a specific three-dimensional functional shape. Thus, understanding the ways RNA performs its function is tightly related to knowing its structure. Since experimental determination of the structure is expensive and time consuming, the problem of predicting the structure of the RNA molecule is an important problem in computational biology. This project will investigate computational methods for RNA structure prediction based on stochastic context-free grammars and the effects of changing different components of the model on the accuracy of the prediction.