Abstract
Bioinformatics has grown considerably since the development of the first sequencing machine, being today intensively used with the next generation DNA sequencers. Viral genomes represent a great challenge to bioinformatics due to its high mutation rate, forming quasispecies in the same infected host. In this paper, we implement and evaluate the performance of a genetic algorithm, named GAVGA, through the quality of a viral genome assembly. The assembly process works by first clustering the reads that share a common substring called seed and for each cluster, checks if there are overlapping reads with a given similarity percentage using a genetic algorithm. The assembled data are then compared to Newbler, SPAdes and ABySS assemblers, and also to a viral assembler such as VICUNA, which confirms the feasibility of our approach. GAVGA was implemented in python 2.7+ and can be downloaded at https://sourceforge.net/projects/gavga-assembler/.
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Oliveira, R.R.M. et al. (2017). GAVGA: A Genetic Algorithm for Viral Genome Assembly. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_33
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DOI: https://doi.org/10.1007/978-3-319-65340-2_33
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