A New Quantum Cuckoo Search Algorithm for Multiple Sequence Alignment

2014 ◽  
Vol 23 (3) ◽  
pp. 261-275 ◽  
Author(s):  
Widad Kartous ◽  
Abdesslem Layeb ◽  
Salim Chikhi
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Initial Population ◽  
Biological Sequences ◽  
Alignment Method ◽  
Progressive Alignment ◽  
Multiple Sequence

AbstractMultiple sequence alignment (MSA) is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution.

Using a Bio-Inspired Algorithm to Resolve the Multiple Sequence Alignment Problem

2016 ◽  
Vol 7 (3) ◽  
pp. 36-55 ◽  
Author(s):  
El-amine Zemali ◽  
Abdelmadjid Boukra
Keyword(s):  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Bat Algorithm ◽  
Premature Convergence ◽  
Hill Climbing ◽  
Initial Population ◽  
Multiple Sequence ◽  
Guide Tree ◽  
And Function

One of the most challenging tasks in bioinformatics is the resolution of Multiple Sequence Alignment (MSA) problem. It consists in comparing a set of protein or DNA sequences, in aim of predicting their structure and function. This paper introduces a new bio-inspired approach to solve such problem. This approach named BA-MSA is based on Bat Algorithm. Bat Algorithm (BA) is a recent evolutionary algorithm inspired from Bats behavior seeking their prey. The proposed approach includes new mechanism to generate initial population. It consists in generating a guide tree for each solution with progressive approach by varying some parameters. The generated guide tree will be enhanced by Hill-Climbing algorithm. In addition, to deal with the premature convergence of BA, a new restart technique is proposed to introduce more diversification when detecting premature convergence. Balibase 2.0 datasets are used for experiments. The comparison with well-known methods as MSA-GA MSA-GA (w\prealign), ClustalW, and SAGA and recent method (BBOMP) shows the effectiveness of the proposed approach.

scholarly journals Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

2018 ◽  
Vol 9 (1) ◽  
pp. 234
Author(s):  
Agung Widyo Utomo
Keyword(s):  
Shared Memory ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Programming Model ◽  
Heuristic Method ◽  
Pairwise Alignment ◽  
Neighbor Joining ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.

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