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Selection of Target Sites for Mobile DNA Integration in the Human Genome

Overview of attention for article published in PLoS Computational Biology, November 2006
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Title
Selection of Target Sites for Mobile DNA Integration in the Human Genome
Published in
PLoS Computational Biology, November 2006
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.

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Mendeley readers

Mendeley readers

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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%