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Updated: Sep 12, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

调整折叠和调整分割:改善对准然后折叠方法用于RNA二次结构预测

Abhinav Mittal¹, David H Mathews^1,2

¹Department of Biochemistry and Biophysics and Center for RNA Biology, University of Rochester Medical Center, Rochester, New York, 14642, USA.

bioRxiv : the preprint server for biology

|August 6, 2025

概括

本研究介绍了用于RNA二次结构预测的AlignmentFold和AlignmentPartition. 这些工具通过结合新的热力学参数和评估对齐影响来提高准确性,实现与现有方法相比的结果.

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
分子生物学分子生物学

背景情况:

从同源序列中预测保存的RNA二次结构至关重要.
现有的方法通常将自由能量最小化与序列共变分析相结合.
固定对齐是常用的,但引入诸如差距和非正规基数对等挑战.

研究的目的:

开发新的计算工具,AlignmentFold和AlignmentPartition,用于增强RNA二次结构预测.
通过包括差距和非正规基数对的参数来完善热力学模型.
评估对齐质量和协变分析对预测准确性的影响.

主要方法:

调整的发展为了达成共识,折叠最小的自由能源结构预测.
调整分区的开发用于基配对的概率预测.
确定空隙和非正规基数对的新近邻热力学参数.
基于对齐方法,大小和包含共变量分析的预测准确性的评估.

主要成果:

调整折叠和调整分割预测RNA二次结构和基配对概率.
对于差距和非正规的基数对,确定了新的热力学参数.
根据各种对齐策略和协变分析评估了预测准确性.
与其他工具相比,排除结构质量评分中的共变性并没有降低,甚至可能提高预测准确性.

结论:

调整折叠和调整分割提供准确的RNA二次结构预测.
开发的热力学参数增强了对与差距和非正规对对齐的预测.
这些工具可在RNAstructure软件包中使用,促进了更广泛的研究应用.

关键词:

预测RNA的二次结构调整-然后-折叠.达成共识的结构是共识的结构.共同变异共同变异共同变异尽量减少自由能源的使用.分区的功能是分区功能.

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