Mobster: accurate detection of mobile element insertions in next generation sequencing data |
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| 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 |
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| Affiliation: | .Department of Human Genetics, RadboudUMC, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands ;.Department of Molecular Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands ;.The Genome Institute, Washington University, St Louis, Missouri USA ;.Hubrecht Institute, KNAW, Utrecht, The Netherlands ;.Department of Clinical Genetics, Maastricht University Medical Centre, Maastricht, The Netherlands |
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| 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0488-x) contains supplementary material, which is available to authorized users. |
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