Prediction of RNA secondary structure using generalized centroid estimators
Prediction of RNA secondary structure using generalized centroid estimators
复制标题
DOI：
10.1093/bioinformatics/btn601
复制
发表时间：
2009-02-15
期刊：
影响因子：
5.8
通讯作者：
Asai, Kiyoshi
中科院分区：
文献类型：
--
作者：
Hamada, Michiaki;Kiryu, Hisanori;Asai, Kiyoshi
动机：最近的研究表明，基于碱基配对概率的后验解码来预测RNA二级结构的方法在预测精度方面优于常规使用的最小自由能方法。然而，在以前的研究中提出的目标函数，这是最大限度地提高在后解码的准确性措施二级structure.Results：我们提出了新的估计，提高了RNA的二级结构预测的准确性的改进空间。所提出的估计最大化的目标函数，这是真阳性和真阴性的碱基对的期望数量的加权和。建议的估计也是改进版本的使用在以前的作品中，即从一个单一的RNA序列和McCascilla-MEA的共同二级结构预测的二级结构预测从多个对齐的RNA序列的双倍。我们澄清建议的估计量和估计量在以前的作品之间的关系，并从理论上表明，以前的估计量包括额外的不必要的条款，在评价措施的准确性。此外，计算实验证实了理论分析，表明经验精度的提高。所提出的估计量代表了Ding等人和Carvalho和Lawrence提出的质心估计量的扩展，并且适用于生物信息学中的各种问题。
Motivation: Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures.Results: We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics.