Optimizing the DNA fragment assembly using metaheuristic-based overlap layout consensus approach

被引:15
作者
Uzma [1 ]
Halim, Zahid [1 ]
机构
[1] Ghulam Ishaq Khan Inst Engn Sci & Technol, Fac Comp Sci & Engn, Machine Intelligence Res Grp MInG, Topi 23460, Pakistan
关键词
Metaheuristic; DNA fragment assembly; Hybrid genetic algorithm; Overlap layout consensus; Optimization; LOCAL SEARCH ALGORITHM; GENETIC ALGORITHM;
D O I
10.1016/j.asoc.2020.106256
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Nucleotide sequencing finds the exact order of nucleotides present in a DNA molecule. The correct DNA sequence is required to obtain the desired information about the complete genetic makeup of an organism. The DNA fragment assembly correctly combines the DNA information present in the form of fragments as a sequence. Reconstruction of the original DNA sequence from large fragments is a challenging task due to the limitations of the available technologies that reads the DNA sequence. Objective of the DNA fragment assembly is to find the correct order of the fragments which is further used in the generation of a consensus sequence that represents the original DNA sequence. Power Aware Local Search (PALS) algorithm proposed for the DNA fragment assembly is an efficient method that orders the fragments in a correct sequence by minimizing the number of contigs. This work presents a hybrid approach on the basis of Overlap Layout Consensus for the DNA fragment assembly, where Restarting and Recentering Genetic Algorithm (RRGA) with integrated PALS is utilized as an evolutionary operator. Quality of the current proposal is quantified using overlap scores and the number of contigs. This work is evaluated using 25 benchmark datasets with three types of experiments. The results are compared with four state-of-the-art methods for the same task, namely, Recentering-Restarting Genetic Algorithm variation for DNA fragment assembly, PALS, Genetic Algorithm, and Hybrid Genetic Algorithm. Results show better average performance of the proposed solution. (C) 2020 Elsevier B.V. All rights reserved.
引用
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页数:23
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