Title |
Tangram: a comprehensive toolbox for mobile element insertion detection
|
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Published in |
BMC Genomics, September 2014
|
DOI | 10.1186/1471-2164-15-795 |
Pubmed ID | |
Authors |
Jiantao Wu, Wan-Ping Lee, Alistair Ward, Jerilyn A Walker, Miriam K Konkel, Mark A Batzer, Gabor T Marth |
Abstract |
Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicating. Mobile element insertions (MEIs) have been associated with human genetic disorders, including Crohn's disease, hemophilia, and various types of cancer, motivating the need for accurate MEI detection methods. To comprehensively identify and accurately characterize these variants in whole genome next-generation sequencing (NGS) data, a computationally efficient detection and genotyping method is required. Current computational tools are unable to call MEI polymorphisms with sufficiently high sensitivity and specificity, or call individual genotypes with sufficiently high accuracy. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 3 | 30% |
Canada | 1 | 10% |
United States | 1 | 10% |
Germany | 1 | 10% |
Switzerland | 1 | 10% |
Unknown | 3 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 8 | 80% |
Scientists | 2 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 2% |
Brazil | 2 | 2% |
France | 1 | <1% |
Norway | 1 | <1% |
Germany | 1 | <1% |
Italy | 1 | <1% |
Spain | 1 | <1% |
Canada | 1 | <1% |
Unknown | 109 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 34 | 29% |
Researcher | 30 | 25% |
Student > Master | 15 | 13% |
Professor | 9 | 8% |
Student > Bachelor | 4 | 3% |
Other | 13 | 11% |
Unknown | 14 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 49 | 41% |
Biochemistry, Genetics and Molecular Biology | 28 | 24% |
Computer Science | 11 | 9% |
Medicine and Dentistry | 5 | 4% |
Social Sciences | 2 | 2% |
Other | 4 | 3% |
Unknown | 20 | 17% |