A new artificial bee colony algorithm to solve the multiple sequence alignment problem. (2016)

• Record Type: Journal Article
• Authors: Öztürk, Celal; Aslan, Selcuk
• Is Part Of: International journal of data mining and bioinformatics. Volume 14:Number 4(2016)
• Page Start: 332
• Page End: 353
• Publication Date: 2016
• Subjects: bioinformatics -- multiple sequence alignment -- swarm intelligence -- artificial bee colony -- ABC algorithm -- simulation -- multiple sequences; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.