Computational approaches for RNA energy parameter estimation
Computational approaches for RNA energy parameter estimation
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DOI：
10.1261/rna.1950510
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发表时间：
2010-12-01
期刊：
影响因子：
4.5
通讯作者：
Murphy, Kevin P.
中科院分区：
文献类型：
--
作者：
Andronescu, Mirela;Condon, Anne;Murphy, Kevin P.
鉴于目前对RNA分子在细胞中的各种功能的快速了解，有效和准确预测RNA结构的方法越来越有价值。为了提高二次结构预测的准确性，我们开发并改进了能量参数估计的优化技术。我们建立在之前的两种RNA自由能参数估计方法的基础上：(1)约束生成（CG）方法，该方法迭代地生成约束，强制已知结构具有比相同分子的其他结构更低的能量；(2)玻尔兹曼似然（Boltzmann Likelihood， BL）方法，该方法推断出一组RNA自由能参数，使一组参考RNA结构的条件似然最大化。在这里，我们以两种主要方式扩展这些方法：我们提出(1)CG的最大边界扩展，以及(2)一种新的线性高斯贝叶斯网络，该网络通过共享参数之间的统计强度来有效地利用稀疏数据。我们在2518个具有参考结构的RNA分子的综合集上测量时，获得了RNA最小自由能假结无二级结构预测精度的显着提高。我们的参数可以与预测RNA二级结构、RNA杂交或结构集成的软件一起使用。我们的数据、软件、结果和各种格式的参数集都可以在http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params上免费获得。
Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.