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

ÖZTÜRK C., Aslan S.

INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, cilt.14, sa.4, ss.332-353, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1504/ijdmb.2016.075823
  • Dergi Adı: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.332-353
  • Anahtar Kelimeler: bioinformatics, multiple sequence alignment, swarm intelligence, artificial bee colony algorithm, GENETIC ALGORITHM, CLASSIFICATION, OPTIMIZATION, SELECTION, COFFEE
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.