Virmid: accurate detection of somatic mutations with sample impurity inference

Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. He...
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Gespeichert in:

Autor*in:	Kim, Sangwoo [verfasserIn] Jeong, Kyowon Bhutani, Kunal Lee, Jeong Ho Patel, Anand Scott, Eric Nam, Hojung Lee, Hayan Gleeson, Joseph G Bafna, Vineet

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Somatic Mutation Read Depth Breast Cancer Data Genotype Probability Mutation Burden

Anmerkung:	© Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: Genome biology - London : BioMed Central, 2000, 14(2013), 8 vom: 29. Aug.
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:8 ; day:29 ; month:08

Links:	Volltext

DOI / URN:	10.1186/gb-2013-14-8-r90

Katalog-ID:	SPR03001851X

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520			\|a Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.
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allfields	10.1186/gb-2013-14-8-r90 doi (DE-627)SPR03001851X (SPR)gb-2013-14-8-r90-e DE-627 ger DE-627 rakwb eng Kim, Sangwoo verfasserin aut Virmid: accurate detection of somatic mutations with sample impurity inference 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213 Jeong, Kyowon aut Bhutani, Kunal aut Lee, Jeong Ho aut Patel, Anand aut Scott, Eric aut Nam, Hojung aut Lee, Hayan aut Gleeson, Joseph G aut Bafna, Vineet aut Enthalten in Genome biology London : BioMed Central, 2000 14(2013), 8 vom: 29. Aug. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:14 year:2013 number:8 day:29 month:08 https://dx.doi.org/10.1186/gb-2013-14-8-r90 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 8 29 08
spelling	10.1186/gb-2013-14-8-r90 doi (DE-627)SPR03001851X (SPR)gb-2013-14-8-r90-e DE-627 ger DE-627 rakwb eng Kim, Sangwoo verfasserin aut Virmid: accurate detection of somatic mutations with sample impurity inference 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213 Jeong, Kyowon aut Bhutani, Kunal aut Lee, Jeong Ho aut Patel, Anand aut Scott, Eric aut Nam, Hojung aut Lee, Hayan aut Gleeson, Joseph G aut Bafna, Vineet aut Enthalten in Genome biology London : BioMed Central, 2000 14(2013), 8 vom: 29. Aug. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:14 year:2013 number:8 day:29 month:08 https://dx.doi.org/10.1186/gb-2013-14-8-r90 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 8 29 08
allfields_unstemmed	10.1186/gb-2013-14-8-r90 doi (DE-627)SPR03001851X (SPR)gb-2013-14-8-r90-e DE-627 ger DE-627 rakwb eng Kim, Sangwoo verfasserin aut Virmid: accurate detection of somatic mutations with sample impurity inference 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213 Jeong, Kyowon aut Bhutani, Kunal aut Lee, Jeong Ho aut Patel, Anand aut Scott, Eric aut Nam, Hojung aut Lee, Hayan aut Gleeson, Joseph G aut Bafna, Vineet aut Enthalten in Genome biology London : BioMed Central, 2000 14(2013), 8 vom: 29. Aug. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:14 year:2013 number:8 day:29 month:08 https://dx.doi.org/10.1186/gb-2013-14-8-r90 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 8 29 08
allfieldsGer	10.1186/gb-2013-14-8-r90 doi (DE-627)SPR03001851X (SPR)gb-2013-14-8-r90-e DE-627 ger DE-627 rakwb eng Kim, Sangwoo verfasserin aut Virmid: accurate detection of somatic mutations with sample impurity inference 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213 Jeong, Kyowon aut Bhutani, Kunal aut Lee, Jeong Ho aut Patel, Anand aut Scott, Eric aut Nam, Hojung aut Lee, Hayan aut Gleeson, Joseph G aut Bafna, Vineet aut Enthalten in Genome biology London : BioMed Central, 2000 14(2013), 8 vom: 29. Aug. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:14 year:2013 number:8 day:29 month:08 https://dx.doi.org/10.1186/gb-2013-14-8-r90 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 8 29 08
allfieldsSound	10.1186/gb-2013-14-8-r90 doi (DE-627)SPR03001851X (SPR)gb-2013-14-8-r90-e DE-627 ger DE-627 rakwb eng Kim, Sangwoo verfasserin aut Virmid: accurate detection of somatic mutations with sample impurity inference 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213 Jeong, Kyowon aut Bhutani, Kunal aut Lee, Jeong Ho aut Patel, Anand aut Scott, Eric aut Nam, Hojung aut Lee, Hayan aut Gleeson, Joseph G aut Bafna, Vineet aut Enthalten in Genome biology London : BioMed Central, 2000 14(2013), 8 vom: 29. Aug. (DE-627)326173617 (DE-600)2040529-7 1474-760X nnns volume:14 year:2013 number:8 day:29 month:08 https://dx.doi.org/10.1186/gb-2013-14-8-r90 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 8 29 08
language	English
source	Enthalten in Genome biology 14(2013), 8 vom: 29. Aug. volume:14 year:2013 number:8 day:29 month:08
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topic_facet	Somatic Mutation Read Depth Breast Cancer Data Genotype Probability Mutation Burden
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container_title	Genome biology
authorswithroles_txt_mv	Kim, Sangwoo @@aut@@ Jeong, Kyowon @@aut@@ Bhutani, Kunal @@aut@@ Lee, Jeong Ho @@aut@@ Patel, Anand @@aut@@ Scott, Eric @@aut@@ Nam, Hojung @@aut@@ Lee, Hayan @@aut@@ Gleeson, Joseph G @@aut@@ Bafna, Vineet @@aut@@
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author	Kim, Sangwoo
spellingShingle	Kim, Sangwoo misc Somatic Mutation misc Read Depth misc Breast Cancer Data misc Genotype Probability misc Mutation Burden Virmid: accurate detection of somatic mutations with sample impurity inference
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topic_title	Virmid: accurate detection of somatic mutations with sample impurity inference Somatic Mutation (dpeaa)DE-He213 Read Depth (dpeaa)DE-He213 Breast Cancer Data (dpeaa)DE-He213 Genotype Probability (dpeaa)DE-He213 Mutation Burden (dpeaa)DE-He213
topic	misc Somatic Mutation misc Read Depth misc Breast Cancer Data misc Genotype Probability misc Mutation Burden
topic_unstemmed	misc Somatic Mutation misc Read Depth misc Breast Cancer Data misc Genotype Probability misc Mutation Burden
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title	Virmid: accurate detection of somatic mutations with sample impurity inference
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title_full	Virmid: accurate detection of somatic mutations with sample impurity inference
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title_sort	virmid: accurate detection of somatic mutations with sample impurity inference
title_auth	Virmid: accurate detection of somatic mutations with sample impurity inference
abstract	Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Abstract Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/. © Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Virmid: accurate detection of somatic mutations with sample impurity inference
url	https://dx.doi.org/10.1186/gb-2013-14-8-r90
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author2	Jeong, Kyowon Bhutani, Kunal Lee, Jeong Ho Patel, Anand Scott, Eric Nam, Hojung Lee, Hayan Gleeson, Joseph G Bafna, Vineet
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Virmid: accurate detection of somatic mutations with sample impurity inference

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