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 Multiple Sequence Alignment Reveals Diversity among Eight African Bush Mango (Irvingia gabonensis Aubry-Lecomte ex O’Rorke) Cultivars

2021 ◽  
pp. 91-96
Author(s):  
U. G. Adebo ◽  
J. O. Matthew
Keyword(s):  
Sequence Analysis ◽  
Phylogenetic Tree ◽  
Genetic Resources ◽  
Data Base ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Bush Mango ◽  
Irvingia Gabonensis ◽  
Cluster 2

Multiple sequence analysis is one of the most widely used model in estimating similarity among genotypes. In a bid to access useful information for the utilization of bush mango genetic resources, nucleotide sequences of eight bush mango (Irvingia gabonensis) cultivars were sourced for and retrieved form NCBI data base, and evaluated for diversity and similarity using computational biology approach. The highest alignment score (26.18), depicting the highest similarity, was between two pairs of sequence combinations; BM07:BM58 and BM12:BM69 respectively, while the least score (19.43) was between BM01: BM13. The phylogenetic tree broadly divided the cultivars into four distinct groups; BM07, BM58 (cluster one), BM01 (cluster 2), BM15, BM13 and BM35 (cluster 3), and BM12, BM69 (cluster 4), while the sequences obtained from the analysis revealed only few fully conserved regions, with the single nucleotides A, and T, which were consistent throughout the evolution. Results obtained from this study indicate that the bush mango cultivars are divergent and can be useful genetic resources for bush mango improvement through breeding.

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

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.

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.

MULTIPLE SEQUENCE ALIGNMENT USING AN EXHAUSTIVE AND GREEDY ALGORITHM

2005 ◽  
Vol 03 (02) ◽  
pp. 243-255 ◽  
Author(s):  
YI WANG ◽  
KUO-BIN LI
Keyword(s):  
Greedy Algorithm ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Alignment ◽  
Initial Alignment ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Java Programming ◽  
Multiple Alignments ◽  
Objective Score

We describe an exhaustive and greedy algorithm for improving the accuracy of multiple sequence alignment. A simple progressive alignment approach is employed to provide initial alignments. The initial alignment is then iteratively optimized against an objective function. For any working alignment, the optimization involves three operations: insertions, deletions and shuffles of gaps. The optimization is exhaustive since the algorithm applies the above operations to all eligible positions of an alignment. It is also greedy since only the operation that gives the best improving objective score will be accepted. The algorithms have been implemented in the EGMA (Exhaustive and Greedy Multiple Alignment) package using Java programming language, and have been evaluated using the BAliBASE benchmark alignment database. Although EGMA is not guaranteed to produce globally optimized alignment, the tests indicate that EGMA is able to build alignments with high quality consistently, compared with other commonly used iterative and non-iterative alignment programs. It is also useful for refining multiple alignments obtained by other methods.

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