Automatic RNA secondary structure prediction with a comparative approach

Fariza Tahi , Manolo Gouy , Mireille Régnier
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引用次数: 26

Abstract

This paper presents an algorithm, DCFold, that automatically predicts the common secondary structure of a set of aligned homologous RNA sequences. It is based on the comparative approach. Helices are searched in one of the sequences, called the ‘target sequence’, and compared to the helices in the other sequences, called the ‘test sequences’. Our algorithm searches in the target sequence for palindromes that have a high probability to define helices that are conserved in the test sequences. This selection of significant palindromes is based on criteria that take into account their length and their mutation rate. A recursive search of helices, starting from these likely ones, is implemented using the ‘divide and conquer’ approach. Indeed, as pseudo-knots are not searched by DCFold, a selected palindrome (p, p′) makes possible to divide the initial sequence into two sequences, the internal one and the one resulting from the concatenation of the two external ones. New palindromes can be searched independently in these subsequences. This algorithm was run on ribosomal RNA sequences and recovered very efficiently their common secondary structures.

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基于比较方法的RNA二级结构自动预测
本文提出了一种自动预测一组同源RNA序列的共同二级结构的算法dcold。它是基于比较的方法。在其中一个序列(称为“目标序列”)中搜索螺旋,并与其他序列(称为“测试序列”)中的螺旋进行比较。我们的算法在目标序列中搜索具有高概率定义在测试序列中保守的螺旋的回文。这种重要回文的选择是基于考虑到它们的长度和突变率的标准。螺旋的递归搜索,从这些可能的螺旋开始,使用“分而治之”的方法实现。实际上,由于dcold不搜索伪结,因此选择回文(p, p ')可以将初始序列分为两个序列,一个是内部序列,另一个是由两个外部序列串联而成的序列。新的回文可以在这些子序列中独立搜索。该算法在核糖体RNA序列上运行,并非常有效地恢复了它们的共同二级结构。
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Instructions to authors Author Index Keyword Index Volume contents New molecular surface-based 3D-QSAR method using Kohonen neural network and 3-way PLS
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