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Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

An Analytical RNA Secondary Structure Benchmark for the RNA Inverse Folding Problem

Author(s): Javad Mohammadzadeh, Mohammad Ganjtabesh and Abbas Nowzari-Dalini

Volume 11, Issue 5, 2016

Page: [571 - 577] Pages: 7

DOI: 10.2174/1574893611666160527100850

Price: $65

Abstract

Background: RNA molecules play several fundamental roles in any living organism. The function of an RNA molecule is highly related to its three dimensional conformation which is referred to as RNA tertiary structure. Since the experimental determination or computational prediction of the RNA tertiary structure is very complicated, tremendous efforts have been focused on the relatively simple RNA secondary structure.

Objective: One of the interesting problems in this context is the RNA inverse folding problem. The goal of this problem is to computationally design an RNA sequence that folds into the given secondary structure. Different methods have been proposed to solve this problem, each of which has been evaluated on a specific dataset regarding accuracy and reliability and therefore no standard benchmark is available to fairly compare these methods.

Method: In this paper, an analytical RNA secondary structure benchmark is constructed that can be used to fairly compare the existing methods and to measure their abilities. The topological properties of a previously introduced variation network over the RNA shapes are employed for the selection of RNA structures and the construction of the benchmark.

Results: All existing methods are evaluated using different measures, including success rate, execution time, Boltzmann probability, energy value, and range of energy. In addition, all the methods are compared and ranked against the mentioned measures. Using these evaluations, one can easily select an appropriate method for a specific usage.

Keywords: Complex networks, RNA inverse folding, RNA secondary structures, variation networks.

Graphical Abstract


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