Title |
VISA - Vector Integration Site Analysis server: a web-based server to rapidly identify retroviral integration sites from next-generation sequencing
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Published in |
BMC Bioinformatics, July 2015
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DOI | 10.1186/s12859-015-0653-6 |
Pubmed ID | |
Authors |
Jonah D. Hocum, Logan R. Battrell, Ryan Maynard, Jennifer E. Adair, Brian C. Beard, David J. Rawlings, Hans-Peter Kiem, Daniel G. Miller, Grant D. Trobridge |
Abstract |
Analyzing the integration profile of retroviral vectors is a vital step in determining their potential genotoxic effects and developing safer vectors for therapeutic use. Identifying retroviral vector integration sites is also important for retroviral mutagenesis screens. We developed VISA, a vector integration site analysis server, to analyze next-generation sequencing data for retroviral vector integration sites. Sequence reads that contain a provirus are mapped to the human genome, sequence reads that cannot be localized to a unique location in the genome are filtered out, and then unique retroviral vector integration sites are determined based on the alignment scores of the remaining sequence reads. VISA offers a simple web interface to upload sequence files and results are returned in a concise tabular format to allow rapid analysis of retroviral vector integration sites. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Israel | 1 | 17% |
Unknown | 5 | 83% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 67% |
Members of the public | 2 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 1 | 2% |
Germany | 1 | 2% |
Unknown | 49 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 17 | 33% |
Student > Ph. D. Student | 7 | 14% |
Student > Bachelor | 5 | 10% |
Student > Master | 4 | 8% |
Student > Postgraduate | 3 | 6% |
Other | 8 | 16% |
Unknown | 7 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 16 | 31% |
Biochemistry, Genetics and Molecular Biology | 9 | 18% |
Immunology and Microbiology | 6 | 12% |
Computer Science | 4 | 8% |
Engineering | 3 | 6% |
Other | 5 | 10% |
Unknown | 8 | 16% |