Research on the DNA Sequence Design Based on GA/PSO Algorithms

2008 ◽  
Author(s):  
Chunxia Xu ◽  
Qiang Zhang ◽  
Bin Wang ◽  
Rui Zhang
Keyword(s):  
Dna Sequence ◽  
Sequence Design ◽  
Dna Sequence Design

Evolutionary Multi-Objective Optimization for DNA Sequence Design

2011 ◽  
pp. 239-264
Author(s):  
Soo-Yong Shin ◽  
In-Hee Lee ◽  
Byoung-Tak Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Dna Microarrays ◽  
Superior Performance ◽  
Final Decision ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design ◽  
Np Problems

Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

scholarly journals EGNAS: an exhaustive DNA sequence design algorithm

2012 ◽  
Vol 13 (1) ◽  
pp. 138 ◽  
Author(s):  
Alfred Kick ◽  
Martin Bönsch ◽  
Michael Mertig
Keyword(s):  
Dna Sequence ◽  
Design Algorithm ◽  
Sequence Design ◽  
Dna Sequence Design

scholarly journals DNA Chisel, a versatile sequence optimizer

2020 ◽  
Vol 36 (16) ◽  
pp. 4508-4509 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Optimization Problems ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequence Optimization ◽  
Sequence Design ◽  
Optimization Framework ◽  
Dna Sequence Design

Abstract Motivation Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine. Results DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Availability and implementation The framework is available as a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (see at https://github.com/Edinburgh-Genome-Foundry/DNAChisel for code and documentation). Supplementary information Supplementary data are available at Bioinformatics online.

Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design

2020 ◽  
Vol 15 (12) ◽  
pp. 1450-1459
Author(s):  
Ying Niu ◽  
Hangyu Zhou ◽  
Shida Wang ◽  
Kai Zhao ◽  
Xiaoxiao Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design

The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.

Evaluation of Ordering Methods for DNA Sequence Design Based on Ant Colony System

2008 ◽  
Author(s):  
Tri Basuki Kurniawan ◽  
Noor Khafifah Khalid ◽  
Zuwairie Ibrahim ◽  
Marzuki Khalid ◽  
Martin Middendorf
Keyword(s):  
Dna Sequence ◽  
Ant Colony ◽  
Ant Colony System ◽  
Sequence Design ◽  
Dna Sequence Design

scholarly journals DNA Sequence Design Based on Template Strategy.

ChemInform ◽  
2004 ◽  
Vol 35 (5) ◽  
Author(s):  
Wenbin Liu ◽  
Shudong Wang ◽  
Lin Gao ◽  
Fengyue Zhang ◽  
Jin Xu
Keyword(s):  
Dna Sequence ◽  
Sequence Design ◽  
Dna Sequence Design

scholarly journals DNA Chisel, a versatile sequence optimizer

2019 ◽  
Author(s):  
Valentin Zulkower ◽  
Susan Rosser
Keyword(s):  
Open Source ◽  
Dna Sequence ◽  
Web Application ◽  
Optimization Problems ◽  
Supplementary Information ◽  
Sequence Optimization ◽  
Sequence Design ◽  
Optimization Framework ◽  
Link Type ◽  
Dna Sequence Design

AbstractMotivationAccounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.ResultsDNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations.Availabilityas a web application (https://cuba.genomefoundry.org/sculpt_a_sequence) or open-source Python library (code and documentation at https://github.com/Edinburgh-Genome-Foundry/DNAChisel)[email protected] informationattached.

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