ArticleComputational and structural biotechnology journal2016
A Graph Based Framework to Model Virus Integration Sites.
Article in Computational and structural biotechnology journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- NetCIS: a network-based common insertion site pipeline for case-control Sleeping Beauty screens.Briefings in bioinformatics · 2026Article
- Comprehensive analysis of off-target and on-target effects resulting from liver-directed CRISPR-Cas9-mediated gene targeting with AAV vectors.Molecular therapy. Methods & clinical development · 2024Article
- The longitudinal kinetics of AAV5 vector integration profiles and evaluation of clonal expansion in mice.Molecular therapy. Methods & clinical development · 2024Article
- Restoration of T and B Cell Differentiation after RAG1 Gene Transfer in Human RAG1 Defective Hematopoietic Stem Cells.Biomedicines · 2024Article
- Prenatal AAV9-GFP administration in fetal lambs results in transduction of female germ cells and maternal exposure to virus.Molecular therapy. Methods & clinical development · 2024Article
- Efficient Nuclease-Directed Integration of Lentivirus Vectors into the Human Ribosomal DNA Locus.Molecular therapy : the journal of the American Society of Gene Therapy · 2020Article
- Common integration sites of published datasets identified using a graph-based framework.Computational and structural biotechnology journal · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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