首页 | 本学科首页   官方微博 | 高级检索  
   检索      


Improved free energy parameters for RNA pseudoknotted secondary structure prediction
Authors:Mirela S Andronescu  Cristina Pop  Anne E Condon
Institution:1.Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA;2.Department of Computer Science, Stanford University, Stanford, California 94305, USA;3.Department of Computer Science, University of British Columbia, Vancouver BC V6T 1Z4, Canada
Abstract:Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.
Keywords:RNA secondary structure prediction  RNA pseudoknots  RNA free energy parameters  RNA thermodynamic models  RNA free energy models
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号