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美国卫生研究院文献>BMC Bioinformatics
>An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions
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An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions
BackgroundRNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.
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机译:BackgroundRNAmute是一种交互式Java应用程序,具有RNA序列,可以计算所有单点突变的二级结构,并根据与野生型预测结构的相似性将它们组织为类别。使用Vienna RNA软件包进行二级结构预测。但是,需要更有效的RNAmute实施方案,以从单点突变的情况扩展到多点突变的一般情况,这通常可能是诱变实验所需要的计算预测。但是分析多点突变是一个需要遍历所有可能的突变的过程,因此变得很昂贵,因为对于具有m点突变的长度为n的序列,运行时间为O(n m sup>)。使用Vienna的RNAsubopt,我们提出了一种方法,该方法基于稳定性考虑仅选择那些可能是构象重排的突变。最好使用点图表示法对RNA二级结构进行检查。
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