School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia.
Bioinformatics. 2012 Dec 1;28(23):3058-65. doi: 10.1093/bioinformatics/bts575. Epub 2012 Oct 8.
Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity.
A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support.
DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw.
Supplementary data are available at Bioinformatics online.
实验室 RNA 结构测定要求高、成本高,因此计算结构预测是一项重要任务。如果一个结构得到了一组进化相关序列的支持,那么用于 RNA 二级结构预测的单序列方法的准确性就会受到限制,人们可以更有信心地认为预测是准确的。RNA 假结是具有高度保守结构的功能元件。然而,由于计算复杂性,很少有比较结构预测方法能够处理假结。
引入了一种称为 DotKnot-PW 的比较假结预测方法,该方法基于二级结构元素和 H 型假结候选结构的结构比较。在具有实验支持的 RNA 结构手工制作测试集上,DotKnot-PW 的性能优于文献中的其他方法。
DotKnot-PW 和 RNA 结构测试集可在网站 http://dotknot.csse.uwa.edu.au/pw 上获得。
补充数据可在生物信息学在线获得。