Genetic Algorithm Using Guide Tree in Mutation Operator for Solving Multiple Sequence Alignment

2015 ◽  
pp. 145-157 ◽  
Author(s):  
Rohit Kumar Yadav ◽  
Haider Banka
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Mutation Operator ◽  
Multiple Sequence ◽  
Guide Tree

scholarly journals SOGA: space oriented genetic algorithm for multiple sequence alignment

2018 ◽  
Vol 7 (4.5) ◽  
pp. 481
Author(s):  
Ruchi Gupta ◽  
Pankaj Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Iterative Process ◽  
Search Space ◽  
Optimization Method ◽  
Motif Finding ◽  
Mutation Operator ◽  
Multiple Sequence ◽  
Crossover And Mutation

Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presents a new genetic algorithm, namely SOGA (Space Oriented Genetic Algorithm for Multiple Sequence Alignment), which has two new mechanisms: the first generates the population with randomly inserting the space between the selected sequences and the second applying new crossover and mutation operator, within an iterative process, to generate new and better solutions. This method is simple and fast. Its performance will further be tested on standard benchmark databas- es and will be compared with well-known algorithms. However, as its solutions clears that there is scope for further improvement. 

Ant Colony with Genetic Algorithm Based on Planar Graph for Multiple Sequence Alignment

2010 ◽  
Vol 9 (2) ◽  
pp. 274-281 ◽  
Author(s):  
Xuyu Xiang ◽  
Dafan Zhang ◽  
Jiaohua Qin ◽  
Yuanyuan Fu
Keyword(s):  
Genetic Algorithm ◽  
Planar Graph ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Ant Colony ◽  
Multiple Sequence

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.

Sign in / Sign up

Export Citation Format

Share Document