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GAVGA: A Genetic Algorithm for Viral Genome Assembly

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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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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Author information

Authors and Affiliations

  1. Computer Science Graduate Program (PPGCC), UFPA, Belém, Pará, Brazil

    Renato R. M. Oliveira, Reginaldo Santos, Manoel Lima & Claudomiro Sales

  2. Laboratory of Bioinformatics and High-performance Computing (LaBioCAD), UFPA, Belém, Pará, Brazil

    Renato R. M. Oliveira, Filipe Damasceno, Ronald Souza, Regiane Kawasaki & Claudomiro Sales

  3. Laboratory of Applied Electromagnetics, UFPA, Belém, Pará, Brazil

    Reginaldo Santos & Claudomiro Sales

Authors
  1. Renato R. M. Oliveira
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  2. Filipe Damasceno
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  3. Ronald Souza
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  4. Reginaldo Santos
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  5. Manoel Lima
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  6. Regiane Kawasaki
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  7. Claudomiro Sales
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Corresponding author

Correspondence to Renato R. M. Oliveira .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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