Published in

BioMed Central, Genome Biology, 10(15), 2014

DOI: 10.1186/s13059-014-0488-x

BioMed Central, Genome Biology, 10(15), p. 488

DOI: 10.1186/preaccept-2240693321179140

Links

Tools

Export citation

Search in Google Scholar

Mobster: accurate detection of mobile element insertions in next generation sequencing data

Journal article published in 2014 by Djie Tjwan Thung, Joep de Ligt ORCID, Lisenka Em Vissers ORCID, Marloes Steehouwer, Mark Kroon, Petra de Vries, Eline P. Slagboom ORCID, Kai Ye, Joris A. Veltman, Jayne Y. Hehir-Kwa
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

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 .