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

RNA二级结构预测的随机上下文无关语法分析

• 批准号: 1312817
• 负责人: Svetlana Poznanovikj
• 金额: $ 12.9万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1312817&HistoricalAwards=false
• 关键词: Analysis; stochastic; context; free; grammars

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.

该项目的目标是对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。一个重音放在依赖的预测类型的生产规则和使用的概率参数的分析，更好地理解在语法设计的选择的局限性的目标。这些结果应该能提供一些修改语法的方法，从而改善预测结果。在过去的几十年里，RNA分子作为遗传密码的被动传递者的科学观点发生了巨大的变化，RNA已知的生物学功能在数量上不断增加，范围也不断扩大。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，理解RNA发挥功能的方式与了解其结构密切相关。由于实验测定结构是昂贵和耗时的，预测RNA分子的结构的问题是计算生物学中的一个重要问题。该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件对预测准确性的影响。