Aligning Multiple Sequences Using an Improved Tabu Search Algorithm

2016 ◽  
Vol 26 (04) ◽  
pp. 1750066 ◽  
Author(s):  
Lamiche Chaabane ◽  
Moussaoui Abdelouahab
Keyword(s):  
Tabu Search ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Large Scale ◽  
Search Algorithm ◽  
Protein Structures ◽  
Biological Sequence ◽  
Multiple Sequence ◽  
Alignment Problem

One of the most essential operations in biological sequence analysis is multiple sequence alignment (MSA), where it is used for constructing evolutionary trees for DNA sequences and for analyzing the protein structures to help design new proteins. In this research study, a new method for solving sequence alignment problem is proposed, which is named improved tabu search (ITS). This algorithm is based on the classical tabu search (TS) optimizing technique. ITS is implemented in order to obtain results of multiple sequence alignment. Several variants concerning neighborhood generation and intensification/diversification strategies for our proposed ITS are investigated. Simulation results on a large scale of datasets have shown the efficacy of the developed approach and its capacity to achieve good quality solutions in terms of scores comparing to those given by other existing methods.

Resolving the multiple sequence alignment problem using biogeography-based optimization with multiple populations

2015 ◽  
Vol 13 (04) ◽  
pp. 1550016 ◽  
Author(s):  
El-Amine Zemali ◽  
Abdelmadjid Boukra
Keyword(s):  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Search Space ◽  
New Method ◽  
Average Score ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Multiple Populations ◽  
Alignment Problem

The multiple sequence alignment (MSA) is one of the most challenging problems in bioinformatics, it involves discovering similarity between a set of protein or DNA sequences. This paper introduces a new method for the MSA problem called biogeography-based optimization with multiple populations (BBOMP). It is based on a recent metaheuristic inspired from the mathematics of biogeography named biogeography-based optimization (BBO). To improve the exploration ability of BBO, we have introduced a new concept allowing better exploration of the search space. It consists of manipulating multiple populations having each one its own parameters. These parameters are used to build up progressive alignments allowing more diversity. At each iteration, the best found solution is injected in each population. Moreover, to improve solution quality, six operators are defined. These operators are selected with a dynamic probability which changes according to the operators efficiency. In order to test proposed approach performance, we have considered a set of datasets from Balibase 2.0 and compared it with many recent algorithms such as GAPAM, MSA-GA, QEAMSA and RBT-GA. The results show that the proposed approach achieves better average score than the previously cited methods.

A TABU SEARCH ALGORITHM FOR POST-PROCESSING MULTIPLE SEQUENCE ALIGNMENT

2005 ◽  
Vol 03 (01) ◽  
pp. 145-156 ◽  
Author(s):  
TARIQ RIAZ ◽  
WANG YI ◽  
KUO-BIN LI
Keyword(s):  
Tabu Search ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Solution Space ◽  
Adaptive Memory ◽  
Multiple Sequence ◽  
Local Optima ◽  
Combinatorial Optimization Problems

Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. 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. 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 tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at .

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