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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 945401, 11 pages
Research Article

An Efficient Genome Fragment Assembling Using GA with Neighborhood Aware Fitness Function

Faculty of Software and Information Science, Iwate Prefectural University, Takizawa-mura, Iwate 020-0193, Japan

Received 12 March 2012; Accepted 11 May 2012

Academic Editor: Qiangfu Zhao

Copyright © 2012 Satoko Kikuchi and Goutam Chakraborty. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To decode a long genome sequence, shotgun sequencing is the state-of-the-art technique. It needs to properly sequence a very large number, sometimes as large as millions, of short partially readable strings (fragments). Arranging those fragments in correct sequence is known as fragment assembling, which is an NP-problem. Presently used methods require enormous computational cost. In this work, we have shown how our modified genetic algorithm (GA) could solve this problem efficiently. In the proposed GA, the length of the chromosome, which represents the volume of the search space, is reduced with advancing generations, and thereby improves search efficiency. We also introduced a greedy mutation, by swapping nearby fragments using some heuristics, to improve the fitness of chromosomes. We compared results with Parsons’ algorithm which is based on GA too. We used fragments with partial reads on both sides, mimicking fragments in real genome assembling process. In Parsons’ work base-pair array of the whole fragment is known. Even then, we could obtain much better results, and we succeeded in restructuring contigs covering 100% of the genome sequences.