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Fast and accurate search for non-coding RNA pseudoknot structures in genomes

机译:快速准确地寻找基因组中非编码RNA假结的结构

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Motivation: Searching genomes for non-coding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknot-free structures, ncRNA search can be effective based on the covariance model and CYK-type dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structure–sequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute a best alignment in time O(ktn) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that, without degrading the accuracy, only a small number of stem candidates need to be examined and a tree decomposition of a small tree width can always be found for the structure graph.
机译:动机:通过其二级结构在基因组中搜索非编码RNA(ncRNA)已成为生物信息学的重要目标。对于无假结结构,基于协方差模型和CYK型动态规划,ncRNA搜索可能有效。然而,将RNA序列与假结进行比对的计算困难阻止了对任意RNA结构的快速和准确搜索。我们先前的工作介绍了RNA假结的图模型,并提出通过图优化来解决结构序列比对问题。给定结构中n个茎中每个茎的目标序列中的k个候选区域,我们可以根据结构图的树宽t分解计算时间O(k t n)的最佳比对。但是,要将这种方法实施到可以常规执行快速而准确的RNA假结搜索的程序中,我们需要新颖的启发式方法,以确保在不降低准确性的情况下,仅需要检查少量的候选茎并且对少量的茎进行树分解总是可以在结构图中找到树的宽度。

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