Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.
BMC Bioinformatics. 2020 May 24;21(1):210. doi: 10.1186/s12859-020-3535-5.
Analysis of secondary structures is essential for understanding the functions of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distributions of their secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical errors.
In this research, we reduced the computational complexity of calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with previous research on thermophilic bacteria, suggesting that our method is applicable in thermal stability analysis. Furthermore, we quantitatively assessed numerical stability using our method..
These results demonstrate that the proposed method is applicable to long RNAs..
分析二级结构对于理解 RNA 的功能至关重要。由于 RNA 分子会发生热波动,因此有必要分析其二级结构的概率分布。然而,由于计算复杂度高,现有的方法不适用于长 RNA。此外,之前的研究存在两个数值难题:溢出和显著的数值误差。
在这项研究中,我们通过引入最大跨度约束来降低计算二级结构概率分布景观的计算复杂度。此外,我们通过两种技术解决了数值计算问题:扩展的对数和以及有保证的精度数值计算。我们在没有溢出的情况下分析了不同温度下 16S 核糖体 RNA 的二级结构稳定性。得到的结果与嗜热菌的先前研究一致,表明我们的方法适用于热稳定性分析。此外,我们使用该方法定量评估了数值稳定性。
这些结果表明,所提出的方法适用于长 RNA。