Title |
VISPA: a computational pipeline for the identification and analysis of genomic vector integration sites
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Published in |
Genome Medicine, September 2014
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DOI | 10.1186/s13073-014-0067-5 |
Pubmed ID | |
Authors |
Andrea Calabria, Simone Leo, Fabrizio Benedicenti, Daniela Cesana, Giulio Spinozzi, Massimilano Orsini, Stefania Merella, Elia Stupka, Gianluigi Zanetti, Eugenio Montini |
Abstract |
The analysis of the genomic distribution of viral vector genomic integration sites is a key step in hematopoietic stem cell-based gene therapy applications, allowing to assess both the safety and the efficacy of the treatment and to study the basic aspects of hematopoiesis and stem cell biology. Identifying vector integration sites requires ad-hoc bioinformatics tools with stringent requirements in terms of computational efficiency, flexibility, and usability. We developed VISPA (Vector Integration Site Parallel Analysis), a pipeline for automated integration site identification and annotation based on a distributed environment with a simple Galaxy web interface. VISPA was successfully used for the bioinformatics analysis of the follow-up of two lentiviral vector-based hematopoietic stem-cell gene therapy clinical trials. Our pipeline provides a reliable and efficient tool to assess the safety and efficacy of integrating vectors in clinical settings. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 2 | 3% |
Germany | 1 | 2% |
France | 1 | 2% |
Finland | 1 | 2% |
Argentina | 1 | 2% |
United States | 1 | 2% |
Unknown | 58 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 23 | 35% |
Student > Ph. D. Student | 12 | 18% |
Student > Bachelor | 6 | 9% |
Other | 5 | 8% |
Student > Master | 5 | 8% |
Other | 6 | 9% |
Unknown | 8 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 26 | 40% |
Biochemistry, Genetics and Molecular Biology | 13 | 20% |
Computer Science | 6 | 9% |
Medicine and Dentistry | 5 | 8% |
Engineering | 3 | 5% |
Other | 5 | 8% |
Unknown | 7 | 11% |