Improved RNA secondary structure prediction by maximizing expected pair accuracy
Improved RNA secondary structure prediction by maximizing expected pair accuracy
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DOI：
10.1261/rna.1643609
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发表时间：
2009-10-01
期刊：
影响因子：
4.5
通讯作者：
Mathews, David H.
中科院分区：
文献类型：
--
作者：
Lu, Zhi John;Gloor, Jason W.;Mathews, David H.
几十年来，自由能最小化一直是 RNA 二级结构预测最流行的方法。它基于使用最近邻模型进行实验得出的一组经验自由能变化参数。在这项研究中，报道了一个名为 MaxExpect 的程序，它通过最大化预期的碱基对准确性来预测 RNA 二级结构。这种方法首先在 CONTRAfold 程序中首创，使用统计学习方法预测的配对概率。这里，利用自由能变化最近邻参数的配分函数计算用于预测碱基对概率以及单链核苷酸的概率。 MaxExpect 预测最优结构（具有最高的预期对精度）和次优结构，作为结构的替代假设。在不同类型 RNA 的大型数据库上进行测试，平均而言，最大预期精度结构比最小自由能结构具有更高的精度。准确性通过灵敏度（正确预测的已知碱基对的百分比）和阳性预测值（PPV）（已知结构中的预测碱基对的百分比）来衡量。通过支持双链或单链，可以分别支持更高的灵敏度或PPV预测。使用 MaxExpect，与自由能最小化相比，在相同灵敏度水平 (73%) 下，最优结构的平均 PPV 从 66% 提高到 68%。
Free energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the free energy change nearest-neighbor parameters is used to predict base-pair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum free energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring double-strandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with free energy minimization.