A new artificial bee colony algorithm to solve the multiple sequence alignment problem
by Celal Öztürk; Selcuk Aslan
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 14, No. 4, 2016

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.

Online publication date: Wed, 06-Apr-2016

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