DNA Fragment Assembly by Ant Colony and Nearest Neighbour Heuristics

Wetcharaporn, Wannasak; Chaiyaratana, Nachol; Tongsima, Sissades

doi:10.1007/11785231_106

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Since then various algorithms [13] have been proposed in the literature to solve this problem. Ant Colony Optimization [14,15,16], Genetic Algorithm [17,18,10], Artificial Bee Colony [19], Hierarchy Clustering, Simulated Annealing, Firefly Algorithm [20], Tabu Search [11], De Novo Assembly Algorithm [21] and Crow Search Algorithm [12] techniques have been leveraged too.…”

Section: Consensus Stagementioning

confidence: 99%

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Jain¹,

Bharti²

2021

Preprint

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With the advent of Genome Sequencing, the field of Personalized Medicine has been revolutionized. From drug testing and studying diseases and mutations to clan genomics, studying the genome is required. However, genome sequence assembly is a very complex combinatorial optimization problem of computational biology. PSO is a popular metaheuristic swarm intelligence optimization algorithm, used to solve combinatorial optimization problems. In this paper, we propose a new variant of PSO to address this permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of the PSO proposed in the literature. The analysis is conducted on four DNA coverage datasets. The conducted analysis demonstrates that the proposed model attain a better performance with better reliability and consistency in comparison to others competitive methods in all cases.

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Section: Consensus Stagementioning

confidence: 99%

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Jain¹,

Bharti²

2021

Preprint

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“…Through research in the previous literature, we found some studies that indicated a relationship between TSP and DNA_FA problem [12], [13], [14]. In 1995 Parsons et al [12] www.ijacsa.thesai.org noticed the similarity between DNA_FA and TSP but they argued that the mapping is not easy for some issues; some of these issues are (i) the DNA_FA problem is a maximization problem where the TSP is a minimization problem, and (ii) the TSP seeks to find a Hamiltonian circuit where the DNA_FA seeks to find a Hamiltonian path.…”

Section: Related Workmentioning

confidence: 99%

“…Notice that although many works mentioned the relationship between DNA_FA with the TSP problem [12], [13], [14], but to the best of our knowledge none provided a formal demonstration enabling to take advantages from the existent solvers of TSP and QAP problems to treat efficiently the DNA_FA problem. For this purpose, we revisited the relationship and the similarities between DNA_FA and TSP presented in [15] and established a new relationship and similarities between DNA_FA and QAP.…”

Section: Polynomial Reduction Of Dna_fa Into Tsp and Qapmentioning

confidence: 99%

An Investigative Study of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2020

IJACSA

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This paper aims to highlight the motivations for investigating genetic algorithms to solve the DNA Fragments Assembly problem (DNA_FA). DNA_FA is an optimization problem that attempts to reconstruct the original DNA sequence by finding the shortest DNA sequence from a given set of fragments. We showed that the DNA_FA optimization problem is a special case of the two well-known optimization problems: The Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP). TSP and QAP are important problems in the field of combinatorial optimization and for which there exists an abundant literature. Genetic Algorithms (GA) applied to these problems have led to very satisfactory results in practice. In the perspective of designing efficient genetic algorithms to solve DNA_FA we showed the existence of a polynomial-time reduction of DNA-FA into TSP and QAP enabling us to point out some technical similarities in terms of solutions and search space complexity. We then conceptually designed a genetic algorithm platform for solving the DNA-FA problem inspired from the existing efficient genetic algorithms in the literature solving TSP and QAP problems. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNA assembly optimization problems.

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“…Another approach that uses an ant colony system combined with a nearest neighbor search heuristic is proposed in [17]. The assembly process is composed of two phases: fragment assembly and contig assembly.…”

Section: Distributed Fragment Assemblymentioning

confidence: 99%

A Distributed Q-Learning Approach to Fragment Assembly

Bocicor¹,

Czibula²,

Czibula³

2011

SIC

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The process of DNA sequencing has nowadays become of great importance in basic biology research, as well as in various fields such as medicine, biotechnology or forensic biology. The fragment assembly problem is a very complex optimization problem that deals with sequencing of DNA, and many computational techniques including computational intelligence algorithms were designed for finding good solutions for this problem. Since DNA fragment assembly is a crucial part of any sequencing project, researchers are still focusing on developing better assemblers. We are introducing in this paper a distributed reinforcement learning based approach for solving the fragment assembly problem, an NP-complete optimization problem that attempts to reconstruct the original DNA sequence from a large number of fragments, each several hundred base-pairs long. Our model is based on a distributed Q-learning approach. The experimental evaluation of the proposed system has provided encouraging results, indicating the potential of our proposal. The advantages and drawbacks of the proposed approach are also emphasized.

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DNA Fragment Assembly by Ant Colony and Nearest Neighbour Heuristics

Cited by 9 publications

References 14 publications

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly

An Investigative Study of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

A Distributed Q-Learning Approach to Fragment Assembly

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