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


ÖZTÜRK C. , Aslan S.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, vol.14, no.4, pp.332-353, 2016 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 4
  • Publication Date: 2016
  • Doi Number: 10.1504/ijdmb.2016.075823
  • Title of Journal : INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
  • Page Numbers: pp.332-353
  • Keywords: bioinformatics, multiple sequence alignment, swarm intelligence, artificial bee colony algorithm, GENETIC ALGORITHM, CLASSIFICATION, OPTIMIZATION, SELECTION, COFFEE

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.