A study of different annealing schedules in SARNA-predict A permutation based SA algorithm for RNA folding

• Published in: International journal of intelligent computing and cybernetics Vol. 8; no. 2; pp. 152 - 171
• Main Authors: Tsang, Herbert H., Wiese, Kay C.
• Format: Journal Article
• Language: English
• Published: 01.01.2015
• Subjects: Algorithms; Annealing; Folding; Mathematical models; Permutations; Ribonucleic acids; Schedules; Thermodynamic models
• ISSN: 1756-378X
• DOI: 10.1108/IJICC-02-2015-0007

Purpose - The purpose of this paper is to present a study of the effect of different types of annealing schedules for a ribonucleic acid (RNA) secondary structure prediction algorithm based on simulated annealing (SA). Design/methodology/approach - An RNA folding algorithm was implemented that assembles the final structure from potential substructures (helixes). Structures are encoded as a permutation of helixes. An SA searches this space of permutations. Parameters and annealing schedules were studied and fine-tuned to optimize algorithm performance. Findings - In comparing with mfold, the SA algorithm shows comparable results (in terms of F -measure) even with a less sophisticated thermodynamic model. In terms of average specificity, the SA algorithm has provided surpassing results. Research limitations/implications - Most of the underlying thermodynamic models are too simplistic and incomplete to accurately model the free energy for larger structures. This is the largest limitation of free energy-based RNA folding algorithms in general. Practical implications - The algorithm offers a different approach that can be used in practice to fold RNA sequences quickly. Originality/value - The algorithm is one of only two SA-based RNA folding algorithms. The authors use a very different encoding, based on permutation of candidate helixes. The in depth study of annealing schedules and other parameters makes the algorithm a strong contender. Another benefit is that new thermodynamic models can be incorporated with relative ease (which is not the case for algorithms based on dynamic programming).