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A Graph Based Framework to Model Virus Integration Sites

With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to dete...

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Detalles Bibliográficos
Autores principales: Fronza, Raffaele, Vasciaveo, Alessandro, Benso, Alfredo, Schmidt, Manfred
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874582/
https://www.ncbi.nlm.nih.gov/pubmed/27257470
http://dx.doi.org/10.1016/j.csbj.2015.10.006
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author Fronza, Raffaele
Vasciaveo, Alessandro
Benso, Alfredo
Schmidt, Manfred
author_facet Fronza, Raffaele
Vasciaveo, Alessandro
Benso, Alfredo
Schmidt, Manfred
author_sort Fronza, Raffaele
collection PubMed
description With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.
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spelling pubmed-48745822016-06-02 A Graph Based Framework to Model Virus Integration Sites Fronza, Raffaele Vasciaveo, Alessandro Benso, Alfredo Schmidt, Manfred Comput Struct Biotechnol J Research Article With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset. Research Network of Computational and Structural Biotechnology 2015-11-30 /pmc/articles/PMC4874582/ /pubmed/27257470 http://dx.doi.org/10.1016/j.csbj.2015.10.006 Text en © 2016 Natrix Separations http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Fronza, Raffaele
Vasciaveo, Alessandro
Benso, Alfredo
Schmidt, Manfred
A Graph Based Framework to Model Virus Integration Sites
title A Graph Based Framework to Model Virus Integration Sites
title_full A Graph Based Framework to Model Virus Integration Sites
title_fullStr A Graph Based Framework to Model Virus Integration Sites
title_full_unstemmed A Graph Based Framework to Model Virus Integration Sites
title_short A Graph Based Framework to Model Virus Integration Sites
title_sort graph based framework to model virus integration sites
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874582/
https://www.ncbi.nlm.nih.gov/pubmed/27257470
http://dx.doi.org/10.1016/j.csbj.2015.10.006
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