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Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction

机译:RNA最近邻参数的分析显示相互依赖性并量化RNA二级结构预测的不确定性

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RNA secondary structure prediction is often used to develop hypotheses about structure-function relationships for newly discovered RNA sequences, to identify unknown functional RNAs, and to design sequences. Secondary structure prediction methods typically use a thermodynamic model that estimates the free energy change of possible structures based on a set of nearest neighbor parameters. These parameters were derived from optical melting experiments of small model oligonucleotides. This work aims to better understand the precision of structure prediction. Here, the experimental errors in optical melting experiments were propagated to errors in the derived nearest neighbor parameter values and then to errors in RNA secondary structure prediction. To perform this analysis, the optical melting experimental values were systematically perturbed within the estimates of experimental error and alternative sets of nearest neighbor parameters were then derived from these error-bounded values. Secondary structure predictions using either the perturbed or reference parameter sets were then compared. This work demonstrated that the precision of RNA secondary structure prediction is more robust than suggested by previous work based on perturbation of the nearest neighbor parameters. This robustness is due to correlations between parameters. Additionally, this work identified weaknesses in the parameter derivation that makes accurate assessment of parameter uncertainty difficult. Considerations for experimental design are provided to mitigate these weaknesses are provided.
机译:RNA二级结构预测通常用于开发关于新发现的RNA序列的结构功能关系的假设,以鉴定未知的功能RNA和设计序列。二次结构预测方法通常使用热力学模型,其基于一组最接近的邻接参数估计可能结构的自由能变化。这些参数源自小型寡核苷酸的光学熔化实验。这项工作旨在更好地了解结构预测的精度。这里,光学熔化实验中的实验误差被传播到导出的最近邻接参数值中的误差,然后传播到RNA二级结构预测中的误差。为了执行该分析,在实验误差的估计中系统地扰乱了光学熔化实验值,然后从这些误报值导出了最近邻参数的替代组。然后比较使用扰动或参考参数集的二级结构预测。这项工作表明,基于最近邻参数的扰动,RNA二级结构预测的精度比以前的工作所示更强大。这种稳健性是由于参数之间的相关性。此外,这项工作确定了参数推导中的缺点,这使得对参数不确定性的准确评估困难。提供实验设计的考虑因素来减轻提供这些弱点。

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