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Viral Genome Compression

Viruses compress their genome to reduce space. One of the main techniques is overlapping genes. We model this process by the shortest common superstring problem, that is, we look for the shortest genome which still contains all genes. We give an algorithm for computing optimal solutions which is slo...

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Detalles Bibliográficos
Autores principales: Ilie, Lucian, Tinta, Liviu, Popescu, Cristian, Hill, Kathleen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121740/
http://dx.doi.org/10.1007/11925903_9
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author Ilie, Lucian
Tinta, Liviu
Popescu, Cristian
Hill, Kathleen A.
author_facet Ilie, Lucian
Tinta, Liviu
Popescu, Cristian
Hill, Kathleen A.
author_sort Ilie, Lucian
collection PubMed
description Viruses compress their genome to reduce space. One of the main techniques is overlapping genes. We model this process by the shortest common superstring problem, that is, we look for the shortest genome which still contains all genes. We give an algorithm for computing optimal solutions which is slow in the number of strings but fast (linear) in their total length. This algorithm is used for a number of viruses with relatively few genes. When the number of genes is larger, we compute approximate solutions using the greedy algorithm which gives an upper bound for the optimal solution. We give also a lower bound for the shortest common superstring problem. The results obtained are then compared with what happens in nature. Remarkably, the compression obtained by viruses is quite high and also very close to the one achieved by modern computers.
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spelling pubmed-71217402020-04-06 Viral Genome Compression Ilie, Lucian Tinta, Liviu Popescu, Cristian Hill, Kathleen A. DNA Computing Article Viruses compress their genome to reduce space. One of the main techniques is overlapping genes. We model this process by the shortest common superstring problem, that is, we look for the shortest genome which still contains all genes. We give an algorithm for computing optimal solutions which is slow in the number of strings but fast (linear) in their total length. This algorithm is used for a number of viruses with relatively few genes. When the number of genes is larger, we compute approximate solutions using the greedy algorithm which gives an upper bound for the optimal solution. We give also a lower bound for the shortest common superstring problem. The results obtained are then compared with what happens in nature. Remarkably, the compression obtained by viruses is quite high and also very close to the one achieved by modern computers. 2006 /pmc/articles/PMC7121740/ http://dx.doi.org/10.1007/11925903_9 Text en © Springer-Verlag Berlin Heidelberg 2006 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Ilie, Lucian
Tinta, Liviu
Popescu, Cristian
Hill, Kathleen A.
Viral Genome Compression
title Viral Genome Compression
title_full Viral Genome Compression
title_fullStr Viral Genome Compression
title_full_unstemmed Viral Genome Compression
title_short Viral Genome Compression
title_sort viral genome compression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121740/
http://dx.doi.org/10.1007/11925903_9
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