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Rapid ab initio Prediction of RNA Pseudoknots via Graph Tree Decomposition (2006)
| Citations: | 8 - 0 self |
BibTeX
@MISC{Zhao06rapidab,
author = {Jizhen Zhao and Russell L. Malmberg and Liming Cai},
title = {Rapid ab initio Prediction of RNA Pseudoknots via Graph Tree Decomposition },
year = {2006}
}
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Abstract
The prediction of RNA secondary structure including pseudoknots remains a challenge due to the intractable computation of the sequence conformation from nucleotide interactions under free energy models. Optimal algorithms often assume a restricted class for the predicted RNA structures and yet still require a high-degree polynomial time complexity, which is too expensive to use. Heuristic methods may yield timeefficient algorithms but they do not guarantee optimality of the predicted structure. This paper introduces a new and efficient algorithm for the prediction of RNA structure with pseudoknots for which the structure is not restricted. Novel prediction techniques are developed based on graph tree decomposition. In particular, based on a simplified energy model, stem overlapping relationships are defined with a graph, in which a specialized maximum independent set corresponds to the desired optimal structure. Such a graph is tree decomposable; dynamic programming over a tree decomposition of the graph leads to an efficient optimal algorithm. The final structure predictions are then based on re-ranking a list of suboptimal structures under a more comprehensive free energy model. The new algorithm is evaluated on a large number of RNA sequence sets taken from diverse resources. It demonstrates overall sensitivity and specificity that outperforms or is comparable with those of previous optimal and heuristic algorithms yet it requires significantly less time than the compared optimal algorithms.
Keyphrases
graph tree decomposition rna pseudoknots rapid ab initio prediction rna sequence set comprehensive free energy model optimal algorithm large number nucleotide interaction intractable computation efficient optimal algorithm timeefficient algorithm efficient algorithm rna structure specialized maximum independent set corresponds sequence conformation simplified energy model predicted rna structure diverse resource free energy model tree decomposition high-degree polynomial time complexity optimal structure suboptimal structure final structure prediction dynamic programming predicted structure rna secondary structure novel prediction technique new algorithm heuristic method heuristic algorithm overall sensitivity restricted class
