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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2015
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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. |
format | Online Article Text |
id | pubmed-4874582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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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