Multiple Sequence Alignment Using Parallel Genetic Algorithms

1999 ◽  
pp. 130-137 ◽  
Author(s):  
L. A. Anbarasu ◽  
P. Narayanasamy ◽  
V. Sundararajan
Keyword(s):  
Genetic Algorithms ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Parallel Genetic Algorithms

Improving multiple sequence alignment biological accuracy through genetic algorithms

2013 ◽  
Vol 65 (3) ◽  
pp. 1076-1088 ◽  
Author(s):  
Miquel Orobitg ◽  
Fernando Cores ◽  
Fernando Guirado ◽  
Concepció Roig ◽  
Cedric Notredame
Keyword(s):  
Genetic Algorithms ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence

scholarly journals A Simple Genetic Algorithm for Optimizing Multiple Sequence Alignment on the Spread of the SARS Epidemic

2019 ◽  
Vol 12 (1) ◽  
pp. 30-39
Author(s):  
Siti Amiroch ◽  
M. Syaiful Pradana ◽  
M. Isa Irawan ◽  
Imam Mukhlash
Keyword(s):  
Genetic Algorithms ◽  
Phylogenetic Tree ◽  
Sequence Alignment ◽  
Dna Sequences ◽  
Multiple Sequence Alignment ◽  
Network System ◽  
Multiple Sequence ◽  
Network Analyses ◽  
Multiple Alignments ◽  
Mutation Region

Background:Multiple sequence alignment is a method of getting genomic relationships between 3 sequences or more. In multiple alignments, there are 3 mutation network analyses, namely topological network system, mutation region network and network system of mutation mode. In general, the three analyses show stable and unstable regions that map mutation regions. This area of ​​mutation is described further in a phylogenetic tree which simultaneously illustrates the path of the spread of an epidemic, the Severe Acute Respiratory Syndrome (SARS) epidemic. The process of spreading the SARS viruses, in this case, is described as the process of phylogenetic tree formation, and as a novelty of this research, multiple alignments in the process are analyzed in detail and then optimized with genetic algorithms.Methods:The data used to form the phylogenetic tree for the spread of the SARS epidemic are 14 DNA sequences which are then optimized by using genetic algorithms. The phylogenetic tree is constructed by using the neighbor-joining algorithm with a distance matrix that the intended distance is the genetic distance obtained from sequence alignment by using the Needleman Wunsch Algorithm.Results & Conclusion:The results of the analysis obtained 3649 stable areas and 19 unstable areas. The results of phylogenetic tree from the network system analysis indicated that the spread of the SARS epidemic extended from Guangzhou 16/12/02 to Zhongshan 27/12/02, then spread simultaneously to Guangzhou 18/02/03 and Guangzhou hospital. After that, the virus reached Metropole, Zhongshan, Hongkong, Singapore, Taiwan, Hong kong, and Hanoi which then continued to Guangzhou 01/01/03 and Toronto at once. The results of the mutation region network system demonstrate decomposition of orthogonal mutations in the 1st order arc.

scholarly journals Survey of the use of genetic algorithm for multiple sequence alignment

2016 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Mohamed Tahar Ben Othman
Keyword(s):  
Genetic Algorithms ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Genomic Analysis ◽  
Evolutionary Divergence ◽  
Sequence Motifs ◽  
Property A ◽  
Multiple Sequence ◽  
Complete Class ◽  
Conserved Sequence

Multiple Sequence Alignment (MSA) is used in genomic analysis, such as the identification of conserved sequence motifs, the estimation of evolutionary divergence between sequences, and the genes’ historical relationships inference. Several researches were conducted to determine the level of similarity of a set of sequences. Due to the problem of the NP-complete class property, a number of researches use genetic algorithms (GA) to find a solution to the multiple sequence alignment. However, the nature of genetic algorithms makes the complexity extremely high due to the redundancy provided by the different operators. The aim of this paper is to study some proposed GA solutions provided for MSA and to compare them using some criteria which we believe any solution should comply with in matters of representativeness, closeness and original sequence invariance.

A NEW GENETIC ALGORITHM FOR MULTIPLE SEQUENCE ALIGNMENT

2012 ◽  
Vol 11 (04) ◽  
pp. 1250023 ◽  
Author(s):  
ZAHRA NARIMANI ◽  
HAMID BEIGY ◽  
HASSAN ABOLHASSANI
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Protein Sequences ◽  
Approximate Solutions ◽  
Heuristic Methods ◽  
Multiple Sequence ◽  
Mutation Operators

Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use very complicated, problem specific and time consuming mutation operators. In this paper, we propose a new algorithm that uses a new way of population initialization and simple mutation and recombination operators. The strength of the proposed GA is using simple mutation operators and also a special recombination operator that does not have problems of similar recombination operators in other GAs. The experimental results show that the proposed algorithm is capable of finding good MSAs in contrast to existing methods, while it uses simple operators with low computational complexity.

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