Title
Novel and efficient RNA secondary structure prediction using hierarchical folding.
Abstract
Algorithms for prediction of RNA secondary structure - the set of base pairs that form when an RNA molecule folds - are valuable to biologists who aim to understand RNA structure and function. Improving the accuracy and efficiency of prediction methods is an ongoing challenge, particularly for pseudoknotted secondary structures, in which base pairs overlap. This challenge is biologically important, since pseudoknotted structures play essential roles in functions of many RNA molecules, such as splicing and ribosomal frameshifting. State-of-the- art methods, which are based on free energy minimization, have high run-time complexity (typically Theta(n(5)) or worse), and can handle (minimize over) only limited types of pseudoknotted structures. We propose a new approach for prediction of pseudoknotted structures, motivated by the hypothesis that RNA structures fold hierarchically, with pseudoknot-free (non-overlapping) base pairs forming first, and pseudoknots forming later so as to minimize energy relative to the folded pseudoknot-free structure. Our HFold algorithm uses two-phase energy minimization to predict hierarchically formed secondary structures in O(n(3)) time, matching the complexity of the best algorithms for pseudoknot-free secondary structure prediction via energy minimization. Our algorithm can handle a wide range of biological structures, including kissing hairpins and nested kissing hairpins, which have previously required Theta(n(6)) time.
Year
DOI
Venue
2008
10.1089/cmb.2007.0198
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
computational molecular biology,RNA,secondary structure
RNA,Nucleic acid structure,Rna secondary structure prediction,RNA splicing,Bioinformatics,Translational frameshift,Base pair,Protein secondary structure,Mathematics,Energy minimization
Journal
Volume
Issue
ISSN
15.0
2
1066-5277
Citations 
PageRank 
References 
3
0.39
13
Authors
3
Name
Order
Citations
PageRank
Hosna Jabbari1303.94
Anne E. Condon21277113.38
Shelly Zhao3301.98