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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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Bibliographische Detailangaben
Hauptverfasser: Fronza, Raffaele (VerfasserIn) , Vasciaveo, Alessandro (VerfasserIn) , Benso, Alfredo (VerfasserIn) , Schmidt, Manfred (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2016
In: Computational and structural biotechnology journal
Year: 2015, Jahrgang: 14, Pages: 69-77
ISSN:2001-0370
DOI:10.1016/j.csbj.2015.10.006
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.csbj.2015.10.006
Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S2001037015000495
Volltext
Verfasserangaben:Raffaele Fronza, Alessandro Vasciaveo, Alfredo Benso, Manfred Schmidt

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520 |a 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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