Title: A new artificial bee colony algorithm to solve the multiple sequence alignment problem

Authors: Celal Öztürk; Selcuk Aslan

Addresses: Department of Computer Engineering, Erciyes University, Melikgazi, 38039 Kayseri, Turkey ' Department of Computer Engineering, Erciyes University, Melikgazi, 38039 Kayseri, Turkey

Abstract: Aligning three or more sequences simultaneously is one of the most challenging problems in bioinformatics. In this paper, a new Artificial Bee Colony algorithm (ABC-Aligner) is proposed to solve multiple sequence alignment. Multiple alignments obtained from ABC-Aligner are compared in terms of the SPS, COFFEE and standard SP scores with Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and basic Artificial Bee Colony (ABC) algorithm; with Sequence Alignment by Genetic Algorithm (SAGA) and CLUSTALX software packages; and with nine well-known alignment tools including CLUSTALW, CLUSTAL OMEGA, DIALIGN-TX, MAFFT, MUSCLE, POA, Probalign, Probcons and T-COFFEE, over the sequences extracted from the BAliBASE 1.0, 3D_ali and BAliBASE 3.0 benchmark datasets, respectively. From the simulation results, it is concluded that proposed ABC-Aligner algorithm outperforms the other population-based meta-heuristics and obtains very close or better scores than software packages used in the experiments without requiring any a priori information or applying complex procedures.

Keywords: bioinformatics; multiple sequence alignment; swarm intelligence; artificial bee colony; ABC algorithm; simulation; multiple sequences.

DOI: 10.1504/IJDMB.2016.075823

International Journal of Data Mining and Bioinformatics, 2016 Vol.14 No.4, pp.332 - 353

Accepted: 09 Feb 2016
Published online: 06 Apr 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article