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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

An Implementation of Three-level Multi-objective ABC Algorithm for RNA Multiple Structural Alignment

Author(s): Soniya Lalwani*, Harish Sharma and Kusum Deep

Volume 13, Issue 1, 2020

Page: [68 - 76] Pages: 9

DOI: 10.2174/2213275912666181128121104

Price: $65

Abstract

Background: Structural alignment of ribose nucleic acid (RNA) is one of the most challenging multi-objective real world problems from the field of bioinformatics.

Objective: RNA molecules are less stable; hence they require inclusion of most stable secondary structure during their alignment. Therefore, the structural alignment requires the consideration of similarity score and structure score, as two objectives. Trade-off between these two objectives exists since obtaining optimum similarity score at concurrent optimum structure score is not possible. This paper presents artificial bee colony algorithm based three level multi-objective approach for performing structural alignment of RNA sequences, namely MO-3LABC.

Methods: Algorithm firstly builds the secondary structure of all sequences at minimum free energy (MFE). Then sequence alignment is performed in level one at average percent sequence identity (APSI) score based gap length, optimized by ABC algorithm. Level two now builds the secondary structure of these aligned sequences based on base-pair probability and co-variation. Now the scores of level one and level two move towards level three for multi-objective optimization at Pareto optimality criteria with few additional strategies.

Results: The results of MO-3LABC are compared with an already established efficient strategy MO-TLPSO; multi-objective two level strategy based on particle swarm optimization. The outputs are compared for pairwise and multiple sequence alignment datasets at prediction accuracy and solution quality criteria.

Conclusion: MO-3LABC is found significantly better than MO-TLPSO at all the four evaluation criteria for both the datasets.

Keywords: RNA structural alignment, artificial bee colony algorithm, non-dominated solutions, multiple sequence alignment, multi-objective optimization, pareto optimal solutions, conflicting objectives.

Graphical Abstract

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