DNA Fragment Assembly Using Multi-Objective Genetic Algorithms

DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of...

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Published in:International journal of applied evolutionary computation Vol. 5; no. 3; pp. 84 - 108
Main Authors: Rathee, Manisha, Kumar, T V. Vijay
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.07.2014
Subjects:
Algorithms
Analysis
Assembly
Deoxyribonucleic acid
DNA
Fragmentation
Fragments
Genetic algorithms
Layouts
Maximization
Minimization
Multiple objective analysis
Optimization
United Kingdom
India
ISSN:1942-3594, 1942-3608
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Abstract DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.
AbstractList DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments, by identifying the right order and orientation of each fragment in the layout. Several algorithms have been proposed for solving FAP. Most of these have solely dwelt on the single objective of maximizing the sum of the overlaps between adjacent fragments in order to optimize the fragment layout. This paper aims to formulate this FAP as a bi-objective optimization problem, with the two objectives being the maximization of the overlap between the adjacent fragments and the minimization of the overlap between the distant fragments. Moreover, since there is greater desirability for having lesser number of contigs, FAP becomes a tri-objective optimization problem where the minimization of the number of contigs becomes the additional objective. These problems were solved using the multi-objective genetic algorithm NSGA-II. The experimental results show that the NSGA-II-based Bi-Objective Fragment Assembly Algorithm (BOFAA) and the Tri-Objective Fragment Assembly Algorithm (TOFAA) are able to produce better quality layouts than those generated by the GA-based Single Objective Fragment Assembly Algorithm (SOFAA). Further, the layouts produced by TOFAA are also comparatively better than those produced using BOFAA.
Audience Academic
Author Kumar, T V. Vijay
Rathee, Manisha
AuthorAffiliation School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
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Snippet DNA Fragment Assembly Problem (FAP) is concerned with the reconstruction of the target DNA, using the several hundreds (or thousands) of sequenced fragments,...
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SubjectTerms Algorithms
Analysis
Assembly
Deoxyribonucleic acid
DNA
Fragmentation
Fragments
Genetic algorithms
Layouts
Maximization
Minimization
Multiple objective analysis
Optimization
Title DNA Fragment Assembly Using Multi-Objective Genetic Algorithms
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