Particle swarm optimization for finding RNA secondary structures

Abstract

Purpose

The purpose of this paper is to present a new particle swarm optimization (PSO) algorithm called HelixPSO for finding ribonucleic acid (RNA) secondary structures that have a low energy and are similar to the native structure.

Design/methodology/approach

Two variants of HelixPSO are described and compared to the recent algorithms Rna‐Predict, SARNA‐Predict, SetPSO and RNAfold. Furthermore, a parallel version of the HelixPSO is proposed.

Findings

For a set of standard RNA test sequences it is shown experimentally that HelixPSO obtains a better average sensitivity than SARNA‐Predict and SetPSO and is as good as RNA‐Predict and RNAfold. When best values for different measures (e.g. number of correctly predicted base pairs, false positives and sensitivity) over several runs are compared, HelixPSO performs better than RNAfold, similar to RNA‐Predict, and is outperformed by SARNA‐Predict. It is shown that HelixPSO complements RNA‐Predict and SARNA‐Predict well since the algorithms show often very different behavior on the same sequence. For the parallel version of HelixPSO it is shown that good speedup values can be obtained for small to medium size PC clusters.

Originality/value

The new PSO algorithm HelixPSO for finding RNA secondary structures uses different algorithmic ideas than the other existing PSO algorithm SetPSO. HelixPSO uses thermodynamic information as well as the centroid as a reference structure and is based on a multiple swarm approach.

Citation

Geis, M. and Middendorf, M. (2011), "Particle swarm optimization for finding RNA secondary structures", International Journal of Intelligent Computing and Cybernetics, Vol. 4 No. 2, pp. 160-186. https://doi.org/10.1108/17563781111136685