Rapid ab initio prediction of RNA pseudoknots via graph tree decomposition

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...

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Bibliographic Details
Published in:Journal of mathematical biology Vol. 56; no. 1-2; pp. 145 - 159
Main Authors: Zhao, Jizhen, Malmberg, Russell L., Cai, Liming
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.01.2008
Springer Nature B.V
Subjects:
Algorithms
Applications of Mathematics
Computational Biology - methods
Mathematical and Computational Biology
Mathematics
Mathematics and Statistics
Nucleic Acid Conformation
RNA - chemistry
Maximum independent set
Pseudoknot
Tree decomposition
Tree width
Thermodynamic energy
RNA secondary structure prediction
ISSN:0303-6812, 1432-1416
Online Access:Get full text
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Summary: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 time-efficient 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.
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ISSN:0303-6812
1432-1416
DOI:10.1007/s00285-007-0124-4