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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

GAVGA: A Genetic Algorithm for Viral Genome Assembly

verfasst von : Renato R. M. Oliveira, Filipe Damasceno, Ronald Souza, Reginaldo Santos, Manoel Lima, Regiane Kawasaki, Claudomiro Sales

Erschienen in: Progress in Artificial Intelligence

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Econometric Genetic Programming in Binary Classification: Evolving Logistic Regressions Through Genetic Programming
Nächstes Kapitel Cartesian Genetic Programming in an Open-Ended Evolution Environment
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Metadaten
Titel
GAVGA: A Genetic Algorithm for Viral Genome Assembly
verfasst von
Renato R. M. Oliveira
Filipe Damasceno
Ronald Souza
Reginaldo Santos
Manoel Lima
Regiane Kawasaki
Claudomiro Sales
Copyright-Jahr
2017
Buch
Progress in Artificial Intelligence

Print ISBN: 978-3-319-65339-6

Electronic ISBN: 978-3-319-65340-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-65340-2_33