Title |
Selection of Target Sites for Mobile DNA Integration in the Human Genome
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Published in |
PLoS Computational Biology, November 2006
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DOI | 10.1371/journal.pcbi.0020157 |
Pubmed ID | |
Authors |
Charles Berry, Sridhar Hannenhalli, Jeremy Leipzig, Frederic D Bushman |
Abstract |
DNA sequences from retroviruses, retrotransposons, DNA transposons, and parvoviruses can all become integrated into the human genome. Accumulation of such sequences accounts for at least 40% of our genome today. These integrating elements are also of interest as gene-delivery vectors for human gene therapy. Here we present a comprehensive bioinformatic analysis of integration targeting by HIV, MLV, ASLV, SFV, L1, SB, and AAV. We used a mathematical method which allowed annotation of each base pair in the human genome for its likelihood of hosting an integration event by each type of element, taking advantage of more than 200 types of genomic annotation. This bioinformatic resource documents a wealth of new associations between genomic features and integration targeting. The study also revealed that the length of genomic intervals analyzed strongly affected the conclusions drawn--thus, answering the question "What genomic features affect integration?" requires carefully specifying the length scale of interest. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 4 | 11% |
Australia | 2 | 5% |
Grenada | 1 | 3% |
Comoros | 1 | 3% |
Unknown | 29 | 78% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 31 | 84% |
Scientists | 6 | 16% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 3% |
Finland | 2 | 1% |
Italy | 1 | <1% |
Germany | 1 | <1% |
United Kingdom | 1 | <1% |
France | 1 | <1% |
Unknown | 145 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 40 | 26% |
Student > Ph. D. Student | 34 | 22% |
Student > Master | 16 | 10% |
Professor > Associate Professor | 12 | 8% |
Student > Bachelor | 6 | 4% |
Other | 22 | 14% |
Unknown | 26 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 60 | 38% |
Biochemistry, Genetics and Molecular Biology | 31 | 20% |
Computer Science | 11 | 7% |
Medicine and Dentistry | 9 | 6% |
Immunology and Microbiology | 9 | 6% |
Other | 9 | 6% |
Unknown | 27 | 17% |