Lindgreen S, Gardner P P, Krogh A
Bioinformatics Centre, Institute of Molecular Biology, University of Copenhagen Universitetsparken 15, 2100 Copenhagen Ø, Denmark.
Bioinformatics. 2006 Dec 15;22(24):2988-95. doi: 10.1093/bioinformatics/btl514. Epub 2006 Oct 12.
The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures.
Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking.
Scripts, data and supplementary material can be found at http://www.binf.ku.dk/Stinus_covariation
非编码RNA的重要性日益明显，并且这些分子的功能通常取决于其结构。通过检测共同进化模式，使用相关RNA序列的比对来推断共有二级结构是很常见的。这种分析的核心部分是测量比对中两个位置之间的共变。在这里，我们对从简单互信息到更高级共变度量的各种度量进行排名。
互信息仍用于二级结构预测，但本研究结果表明哪些度量是有用的。通过考虑例如插入缺失和碱基堆积纳入更多结构信息可提高准确性，这表明符合物理实际的度量能产生更好的预测。这可用于改进当前和未来的二级结构预测程序。测试的最佳度量是经修改以纳入碱基堆积的RNAalifold共变度量。
脚本、数据和补充材料可在http://www.binf.ku.dk/Stinus_covariation上找到
