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aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences

机译:aliFreeFold:一种从同源RNA序列预测二级结构的无比对方法

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摘要

MotivationPredicting the conserved secondary structure of homologous ribonucleic acid (RNA) sequences is crucial for understanding RNA functions. However, fast and accurate RNA structure prediction is challenging, especially when the number and the divergence of homologous RNA increases. To address this challenge, we propose aliFreeFold, based on a novel alignment-free approach which computes a representative structure from a set of homologous RNA sequences using sub-optimal secondary structures generated for each sequence. It is based on a vector representation of sub-optimal structures capturing structure conservation signals by weighting structural motifs according to their conservation across the sub-optimal structures.
机译:动机预测同源核糖核酸(RNA)序列的保守二级结构对于理解RNA功能至关重要。但是,快速准确的RNA结构预测具有挑战性,特别是当同源RNA的数量和差异增加时。为了解决这一挑战,我们提出了aliFreeFold,它基于一种新颖的无比对方法,该方法使用为每个序列生成的次优二级结构,根据一组同源RNA序列计算出代表性结构。它基于次优结构的矢量表示,该次优结构通过根据结构主题在次优结构上的保守性对结构基元进行加权来捕获结构保守性信号。

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