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GENE-IS: time-efficient and accurate analysis of viral integration events in large-scale gene therapy data

Integration site profiling and clonality analysis of viral vector distribution in gene therapy is a key factor to monitor the fate of gene-corrected cells, assess the risk of malignant transformation, and establish vector biosafety. We developed the Genome Integration Site Analysis Pipeline (GENE-IS...

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Hauptverfasser: Afzal, Saira (VerfasserIn) , Wilkening, Stefan (VerfasserIn) , Kalle, Christof von (VerfasserIn) , Schmidt, Manfred (VerfasserIn) , Fronza, Raffaele (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2017
In: Molecular therapy. Nucleic Acids
Year: 2017, Jahrgang: 6, Pages: 133-139
ISSN:2162-2531
DOI:10.1016/j.omtn.2016.12.001
Online-Zugang:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1016/j.omtn.2016.12.001
Verlag, kostenfrei, Volltext: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363413/
Volltext
Verfasserangaben:Saira Afzal, Stefan Wilkening, Christof von Kalle, Manfred Schmidt, and Raffaele Fronza
Beschreibung
Zusammenfassung:Integration site profiling and clonality analysis of viral vector distribution in gene therapy is a key factor to monitor the fate of gene-corrected cells, assess the risk of malignant transformation, and establish vector biosafety. We developed the Genome Integration Site Analysis Pipeline (GENE-IS) for highly time-efficient and accurate detection of next-generation sequencing (NGS)-based viral vector integration sites (ISs) in gene therapy data. It is the first available tool with dual analysis mode that allows IS analysis both in data generated by PCR-based methods, such as linear amplification method PCR (LAM-PCR), and by rapidly evolving targeted sequencing (e.g., Agilent SureSelect) technologies. GENE-IS makes use of trimming strategies, customized reference genome, and soft-clipped information with sequential filtering steps to provide annotated IS with clonality information. It is a scalable, robust, precise, and reliable tool for large-scale pre-clinical and clinical data analysis that provides users complete flexibility and control over analysis with a broad range of configurable parameters. GENE-IS is available at https://github.com/G100DKFZ/gene-is.
Beschreibung:Published online: 1 December 2016
Gesehen am 04.09.2018
Beschreibung:Online Resource
ISSN:2162-2531
DOI:10.1016/j.omtn.2016.12.001

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