Childs Liam, Nikoloski Zoran, May Patrick, Walther Dirk
Max-Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, Golm, Germany.
Nucleic Acids Res. 2009 May;37(9):e66. doi: 10.1093/nar/gkp206. Epub 2009 Apr 1.
The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.
近年来，非编码RNA基因的研究受到了越来越多的关注。越来越多的证据表明，基因组中比以前认为的更大比例被转录成功能大多未知的RNA分子，同时新型非编码RNA类型和功能性RNA元件的发现也推动了这一研究。在此，我们证明了代表预测RNA二级结构的图的特定属性反映了功能信息。我们引入了一种计算算法和一个基于网络的相关工具（GraPPLE），用于将非编码RNA分子分类为功能性分子，并进一步根据其图属性将其归入Rfam家族。与基于序列相似性的方法和协方差模型不同，GraPPLE在序列差异增加时表现出更强的鲁棒性，并且与现有方法结合使用时，能显著提高预测准确性。此外，被确定为最具信息性的图属性显示出能让人了解是哪些特定的结构特征使RNA分子具有功能。因此，GraPPLE可能提供一种有价值的计算筛选工具，用于在大型候选数据集中识别潜在有趣的RNA分子。
