Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction

Cotta, Carlos; Fernández, Antonio J.; Gallardo, José E.; Luque, Gabriel; Alba, Enrique

doi:10.1002/9780470411353.ch16

Cited by 3 publications

(7 citation statements)

References 37 publications

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Section: Understanding Dna Assemblymentioning

confidence: 99%

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Investigating stochastic diffusion search in DNA sequence assembly problem

al-Rifaie

2015

2015 SAI Intelligent Systems Conference (IntelliSys)

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Abstract-This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS) -swarm intelligence algorithm -to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally the performance of the algorithm is compared against several other techniques, demonstrating its weaknesses and strength in the experiments presented in the paper.

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Section: Understanding Dna Assemblymentioning

confidence: 99%

“…The quality of a consensus sequence is measured by the term coverage [9], [7]. Coverage is evaluated according to the following equation:…”

Section: Understanding Dna Assemblymentioning

confidence: 99%

Investigating stochastic diffusion search in DNA sequence assembly problem

al-Rifaie

2015

2015 SAI Intelligent Systems Conference (IntelliSys)

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“…The higher the coverage, the higher the probability of covering original genome, the higher the correctness of the assembled parts, the fewer the number of the gaps, and the better the result [7,12].…”

Section: Understanding Dna Assemblymentioning

confidence: 99%

“…Therefore, the goal of genome projects is to reconstruct the original genome sequence of an organism. To achieve the goal, DNA fragment assembly process is divided into three phases [7,10] 1. Overlap Phase is tasked to find the common sequence among the prefix of one sequence and suffix of another.…”

Section: Understanding Dna Assemblymentioning

confidence: 99%

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Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

al-Rifaie

2016

Studies in Computational Intelligence

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This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS) -a swarm intelligence algorithm -to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to overlap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments realworld data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper.

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Transmembrane Helix Assembly by Max–Min Ant System Algorithm

et al. 2015

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Because of the rapid progress in biochemical and structural studies of membrane proteins, considerable attention has been given on developing efficient computational methods for solving low-to-medium resolution structures using sparse structural data. In this study, we demonstrate a novel algorithm, max-min ant system (MMAS), designed to find an assembly of α-helical transmembrane proteins using a rigid helix arrangement guided by distance constraints. The new algorithm generates a large variety with finite number of orientations of transmembrane helix bundle and finds the solution that is matched with the provided distance constraints based on the behavior of ants to search for the shortest possible path between their nest and the food source. To demonstrate the efficiency of the novel search algorithm, MMAS is applied to determine the transmembrane packing of KcsA and MscL ion channels from a limited distance information extracted from the crystal structures, and the packing of KvAP voltage sensor domain using a set of 10 experimentally determined constraints, and the results are compared with those of two popular used stochastic methods, simulated annealing Monte Carlo method and genetic algorithm.

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Metaheuristics in Bioinformatics: DNA Sequencing and Reconstruction

Cited by 3 publications

References 37 publications

Investigating stochastic diffusion search in DNA sequence assembly problem

Investigating stochastic diffusion search in DNA sequence assembly problem

Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

Transmembrane Helix Assembly by Max–Min Ant System Algorithm

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