Title |
Mobster: accurate detection of mobile element insertions in next generation sequencing data
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Published in |
Genome Biology, October 2014
|
DOI | 10.1186/s13059-014-0488-x |
Pubmed ID | |
Authors |
Djie Tjwan Thung, Joep de Ligt, Lisenka EM Vissers, Marloes Steehouwer, Mark Kroon, Petra de Vries, Eline P Slagboom, Kai Ye, Joris A Veltman, Jayne Y Hehir-Kwa |
Abstract |
Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile elements. Mobster has a low false discovery rate and high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 22% |
Japan | 1 | 11% |
Israel | 1 | 11% |
France | 1 | 11% |
India | 1 | 11% |
Germany | 1 | 11% |
Norway | 1 | 11% |
Unknown | 1 | 11% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 5 | 56% |
Members of the public | 4 | 44% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 3 | 2% |
Germany | 1 | <1% |
Norway | 1 | <1% |
Brazil | 1 | <1% |
United Kingdom | 1 | <1% |
Russia | 1 | <1% |
United States | 1 | <1% |
Luxembourg | 1 | <1% |
Unknown | 161 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 48 | 28% |
Researcher | 37 | 22% |
Student > Master | 17 | 10% |
Student > Bachelor | 11 | 6% |
Professor | 9 | 5% |
Other | 20 | 12% |
Unknown | 29 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 63 | 37% |
Biochemistry, Genetics and Molecular Biology | 44 | 26% |
Computer Science | 10 | 6% |
Medicine and Dentistry | 6 | 4% |
Chemistry | 3 | 2% |
Other | 8 | 5% |
Unknown | 37 | 22% |