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A Biologically Meaningful Extension of the Efficient Method for Deleterious Mutations Prediction in RNAs: Insertions and Deletions in Addition to Substitution Mutations

机译:在RNA中有害突变预测的有效方法的生物学意义延伸:添加和缺失除替代突变之外

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The RNAmute and MultiRNAmute interactive java programs were developed to predict deleterious point mutations in RNA sequences, which intently cause a global conformational rearrangement of the secondary structure of the functional RNA molecules and thereby disrupt their function. RNAmute was designed to deal with only single point mutations in a brute force manner, while the MultiRNAmute tool uses an efficient approach to deal with multiple point mutations. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding solutions and/or destabilization of the optimal MFE structure of the wild type RNA molecule. Both programs utilize the Vienna RNA package to find the optimal and suboptimal (in case of MultiRNAmute) RNA secondary structures. The main limitation of both programs is their ability to predict only substitution mutations and these programs were not designed to work with deletion or insertion mutations. Herein we develop an efficient approach, based on suboptimal folding solutions, to predict multiple point mutations consisting of deletions, insertions and substitution mutations. All RNAmute algorithms were validated on the TPP-riboswitch and some other functional RNAs.
机译:开发了Rnamute和Multimutnute Interactive Java程序以预测RNA序列中的有害点突变,其专注地引起功能性RNA分子的二次结构的全局构象重排,从而破坏其功能。 Rnamute旨在仅以蛮力方式处理单点突变,而Multramute工具使用有效的方法来处理多点突变。 Multramute中使用的方法是基于次优RNA折叠溶液的稳定性和野生型RNA分子的最佳MFE结构的稳定性。这两个程序都利用了维也纳RNA包,找到了最佳和次优(在多阵列的情况下)RNA二级结构。两个程序的主要限制是他们只预测替代突变的能力,这些程序并非设计用于删除或插入突变。在此,我们基于次优折叠解决方案开发一种有效的方法,以预测由缺失,插入和替代突变组成的多个点突变。在TPP-Riboswitch和其他一些功能RNA上验证了所有R个曲线算法。

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