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

Djie T.jwan Thung; Joep de Ligt; Lisenka E.M. Vissers; Marloes Steehouwer; Mark Kroon; Petra de Vries; Eline P. Slagboom; Kai Ye; Joris A. Veltman; Jayne Y. Hehir-Kwa

doi:10.1186/s13059-014-0488-x

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

Djie T.jwan Thung, Joep de Ligt, Lisenka E.M. Vissers, Marloes Steehouwer, Mark Kroon, Petra de Vries, Eline P. Slagboom, Kai Ye, Joris A. Veltman, Jayne Y. Hehir-Kwa

Research output: Contribution to journal › Article › peer-review

73 Citations (Scopus)

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 rateand high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

Original language	English
Article number	488
Pages (from-to)	488
Number of pages	1
Journal	Genome Biology
Volume	15
Issue number	10
DOIs	https://doi.org/10.1186/s13059-014-0488-x
Publication status	Published - 2014
Externally published	Yes

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title = "Mobster: accurate detection of mobile element insertions in next generation sequencing data",

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 rateand high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.",

author = "Thung, {Djie T.jwan} and {de Ligt}, Joep and Vissers, {Lisenka E.M.} and Marloes Steehouwer and Mark Kroon and {de Vries}, Petra and Slagboom, {Eline P.} and Kai Ye and Veltman, {Joris A.} and Hehir-Kwa, {Jayne Y.}",

note = "Funding Information: DT Thung, M Steehouwer, and JA Veltman are supported by the European Research Council (DENOVO 281964 to JA Veltman). We also wish to thank A. Hoischen for providing access to the paired-end WES dataset and validation samples. This publication was supported by the Dutch national program COMMIT.",

year = "2014",

doi = "10.1186/s13059-014-0488-x",

language = "English",

volume = "15",

pages = "488",

journal = "Genome Biology",

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publisher = "BioMed Central Ltd.",

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T2 - accurate detection of mobile element insertions in next generation sequencing data

AU - Thung, Djie T.jwan

AU - de Ligt, Joep

AU - Vissers, Lisenka E.M.

AU - Steehouwer, Marloes

AU - Kroon, Mark

AU - de Vries, Petra

AU - Slagboom, Eline P.

AU - Ye, Kai

AU - Veltman, Joris A.

AU - Hehir-Kwa, Jayne Y.

N1 - Funding Information: DT Thung, M Steehouwer, and JA Veltman are supported by the European Research Council (DENOVO 281964 to JA Veltman). We also wish to thank A. Hoischen for providing access to the paired-end WES dataset and validation samples. This publication was supported by the Dutch national program COMMIT.

PY - 2014

Y1 - 2014

N2 - 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 rateand high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

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

Abstract

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