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

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...

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Main Authors:	Thung, Djie Tjwan, de Ligt, Joep, Vissers, Lisenka EM, Steehouwer, Marloes, Kroon, Mark, de Vries, Petra, Slagboom, Eline P, Ye, Kai, Veltman, Joris A, Hehir-Kwa, Jayne Y
Format:	Online
Language:	English
Published:	BioMed Central 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228151/

id	pubmed-4228151
recordtype	oai_dc
spelling	pubmed-42281512014-11-13 Mobster: accurate detection of mobile element insertions in next generation sequencing data Thung, Djie Tjwan de Ligt, Joep Vissers, Lisenka EM Steehouwer, Marloes Kroon, Mark de Vries, Petra Slagboom, Eline P Ye, Kai Veltman, Joris A Hehir-Kwa, Jayne Y Method 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. BioMed Central 2014-10-28 2014 /pmc/articles/PMC4228151/ /pubmed/25348035 http://dx.doi.org/10.1186/s13059-014-0488-x Text en © Thung et al.; licensee BioMed Central Ltd. 2014 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Thung, Djie Tjwan de Ligt, Joep Vissers, Lisenka EM Steehouwer, Marloes Kroon, Mark de Vries, Petra Slagboom, Eline P Ye, Kai Veltman, Joris A Hehir-Kwa, Jayne Y
spellingShingle	Thung, Djie Tjwan de Ligt, Joep Vissers, Lisenka EM Steehouwer, Marloes Kroon, Mark de Vries, Petra Slagboom, Eline P Ye, Kai Veltman, Joris A Hehir-Kwa, Jayne Y Mobster: accurate detection of mobile element insertions in next generation sequencing data
author_facet	Thung, Djie Tjwan de Ligt, Joep Vissers, Lisenka EM Steehouwer, Marloes Kroon, Mark de Vries, Petra Slagboom, Eline P Ye, Kai Veltman, Joris A Hehir-Kwa, Jayne Y
author_sort	Thung, Djie Tjwan
title	Mobster: accurate detection of mobile element insertions in next generation sequencing data
title_short	Mobster: accurate detection of mobile element insertions in next generation sequencing data
title_full	Mobster: accurate detection of mobile element insertions in next generation sequencing data
title_fullStr	Mobster: accurate detection of mobile element insertions in next generation sequencing data
title_full_unstemmed	Mobster: accurate detection of mobile element insertions in next generation sequencing data
title_sort	mobster: accurate detection of mobile element insertions in next generation sequencing data
description	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.
publisher	BioMed Central
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228151/
_version_	1613155189586395136

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

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