Kawaguchi Risa Karakida, Kiryu Hisanori
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Methods Mol Biol. 2023;2586:35-48. doi: 10.1007/978-1-0716-2768-6_3.
The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.
RNA二级结构信息已被广泛应用于RNA功能的推断。然而，由于计算时间和数值误差问题，经典的预测方法对于诸如mRNA等长RNA是不可行的。为了克服这些问题，人们应用了滑动窗口方法，但其结果无法直接与全局RNA结构预测相比较。在本章中，我们介绍ParasoR，一种为全基因组RNA二级结构并行计算而设计的方法。为了实现全基因组预测，ParasoR将结构预测所需的动态规划（DP）矩阵分布到多个计算节点上。通过使用不是原始DP变量而是变量比率的数据库，ParasoR可以按需在本地计算诸如茎概率或可及性等结构得分。通过ParasoR对局部二级结构进行全面分析有望成为检测长RNA统计约束的一种有前景的方法。
