A stochastic de novo assembly algorithm for viral-sized genomes obtains correct genomes and builds consensus

Bucur, Doina

doi:10.1016/j.ins.2017.07.039

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The studies of Bucur [6], [11] focus on minimizing the total length of the scaffold (summing the length of the overlapped contigs). Unlike previous works, Bucur used simulated data sets where the fragments were of uniform length, they were able to measure the accuracy since they had the reference genome.…”

Section: A Genetic Algorithm For Dnafa Problemmentioning

confidence: 99%

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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This paper aims to highlight the motivations for investigating genetic algorithms (GAs) to solve the DNA Fragment Assembly (DNAFA) problem. DNAFA problem is an optimization problem that attempts to reconstruct an original DNA sequence by finding the shortest DNA sequence from a given set of fragments. This paper is a continuation of our previous research paper in which the existence of a polynomialtime reduction of DNAFA into the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP) was discussed. Taking advantage of this reduction, this work conceptually designed a genetic algorithm (GA) platform to solve the DNAFA problem. This platform offers several ingredients enabling us to create several variants of GA solvers for the DNAFA optimization problems. The main contribution of this paper is the designing of an efficient GA variant by carefully integrating different GAs operators of the platform. For that, this work individually studied the effects of different GAs operators on the performance of solving the DNAFA problem. This study has the advantage of benefiting from prior knowledge of the performance of these operators in the contexts of the TSP and QAP problems. The best designed GA variant shows a significant improvement in accuracy (overlap score) reaching more than 172% of what is reported in the literature.

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Section: A Genetic Algorithm For Dnafa Problemmentioning

confidence: 99%

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Bennaceur¹,

Almutairy²,

Alqhtani³

2023

IJACSA

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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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“…They assessed the accuracy and measured how the assembler actually covers the reference genome. In the next work [30] some improvements have been applied; the first one is to merge any two contigs that have overlapped between them longer than the length of the fragment. The other improvement is a post-processing step to merge each left chaff contigs into the appropriate location on long contigs (chaff means a contig of length shorter than 3-4 times the fragment length).…”

Section: Overlap (W)mentioning

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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A stochastic de novo assembly algorithm for viral-sized genomes obtains correct genomes and builds consensus

Cited by 4 publications

References 30 publications

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

Experimental Evaluation of Genetic Algorithms to Solve the DNA Assembly Optimization Problem

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

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