Multiple sequence alignment using tabu search

  • Authors:

  • Tariq Riaz;Yi Wang;Kuo-Bin Li

  • Affiliations:

  • Bioinformatics Institute, Singapore;Bioinformatics Institute, Singapore;Bioinformatics Institute, Singapore

  • Venue:
  • APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

Tabu search is a meta-heuristic approach that is found to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to align multiple sequences. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. It intensifies by bringing the search process to poorly aligned regions of an elite solution, and softly diversifies by moving from one poorly aligned region to another. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that for datasets comprising orphan sequences, divergent families and long internal insertions, tabu search generates better alignment as compared to other methods studied in this paper. The source code of our tabu search algorithm is available at http://www.bii.a-star.edu.sg/~tariq/tabu/.