Overcoming bias and systematic errors in next generation sequencing data

Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject t...
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Gespeichert in:

Autor*in:	Taub, Margaret A [verfasserIn] Corrada Bravo, Hector Irizarry, Rafael A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Batch Effect Quality Assessment Method Error Correction Method Microarray Quality Control Surrogate Variable Analysis

Anmerkung:	© BioMed Central Ltd 2010

Übergeordnetes Werk:	Enthalten in: Genome medicine - London : BioMed Central, 2009, 2(2010), 12 vom: 10. Dez.
Übergeordnetes Werk:	volume:2 ; year:2010 ; number:12 ; day:10 ; month:12

Links:	Volltext

DOI / URN:	10.1186/gm208

Katalog-ID:	SPR030566452

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spelling	10.1186/gm208 doi (DE-627)SPR030566452 (SPR)gm208-e DE-627 ger DE-627 rakwb eng Taub, Margaret A verfasserin aut Overcoming bias and systematic errors in next generation sequencing data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2010 Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. Batch Effect (dpeaa)DE-He213 Quality Assessment Method (dpeaa)DE-He213 Error Correction Method (dpeaa)DE-He213 Microarray Quality Control (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Corrada Bravo, Hector aut Irizarry, Rafael A aut Enthalten in Genome medicine London : BioMed Central, 2009 2(2010), 12 vom: 10. Dez. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:2 year:2010 number:12 day:10 month:12 https://dx.doi.org/10.1186/gm208 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_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2 2010 12 10 12
allfields_unstemmed	10.1186/gm208 doi (DE-627)SPR030566452 (SPR)gm208-e DE-627 ger DE-627 rakwb eng Taub, Margaret A verfasserin aut Overcoming bias and systematic errors in next generation sequencing data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2010 Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. Batch Effect (dpeaa)DE-He213 Quality Assessment Method (dpeaa)DE-He213 Error Correction Method (dpeaa)DE-He213 Microarray Quality Control (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Corrada Bravo, Hector aut Irizarry, Rafael A aut Enthalten in Genome medicine London : BioMed Central, 2009 2(2010), 12 vom: 10. Dez. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:2 year:2010 number:12 day:10 month:12 https://dx.doi.org/10.1186/gm208 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_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2 2010 12 10 12
allfieldsGer	10.1186/gm208 doi (DE-627)SPR030566452 (SPR)gm208-e DE-627 ger DE-627 rakwb eng Taub, Margaret A verfasserin aut Overcoming bias and systematic errors in next generation sequencing data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2010 Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. Batch Effect (dpeaa)DE-He213 Quality Assessment Method (dpeaa)DE-He213 Error Correction Method (dpeaa)DE-He213 Microarray Quality Control (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Corrada Bravo, Hector aut Irizarry, Rafael A aut Enthalten in Genome medicine London : BioMed Central, 2009 2(2010), 12 vom: 10. Dez. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:2 year:2010 number:12 day:10 month:12 https://dx.doi.org/10.1186/gm208 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_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2 2010 12 10 12
allfieldsSound	10.1186/gm208 doi (DE-627)SPR030566452 (SPR)gm208-e DE-627 ger DE-627 rakwb eng Taub, Margaret A verfasserin aut Overcoming bias and systematic errors in next generation sequencing data 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © BioMed Central Ltd 2010 Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. Batch Effect (dpeaa)DE-He213 Quality Assessment Method (dpeaa)DE-He213 Error Correction Method (dpeaa)DE-He213 Microarray Quality Control (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213 Corrada Bravo, Hector aut Irizarry, Rafael A aut Enthalten in Genome medicine London : BioMed Central, 2009 2(2010), 12 vom: 10. Dez. (DE-627)594424275 (DE-600)2484394-5 1756-994X nnns volume:2 year:2010 number:12 day:10 month:12 https://dx.doi.org/10.1186/gm208 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_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 2 2010 12 10 12
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topic_facet	Batch Effect Quality Assessment Method Error Correction Method Microarray Quality Control Surrogate Variable Analysis
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spellingShingle	Taub, Margaret A misc Batch Effect misc Quality Assessment Method misc Error Correction Method misc Microarray Quality Control misc Surrogate Variable Analysis Overcoming bias and systematic errors in next generation sequencing data
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topic_title	Overcoming bias and systematic errors in next generation sequencing data Batch Effect (dpeaa)DE-He213 Quality Assessment Method (dpeaa)DE-He213 Error Correction Method (dpeaa)DE-He213 Microarray Quality Control (dpeaa)DE-He213 Surrogate Variable Analysis (dpeaa)DE-He213
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title	Overcoming bias and systematic errors in next generation sequencing data
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title_full	Overcoming bias and systematic errors in next generation sequencing data
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title_sort	overcoming bias and systematic errors in next generation sequencing data
title_auth	Overcoming bias and systematic errors in next generation sequencing data
abstract	Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. © BioMed Central Ltd 2010
abstractGer	Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. © BioMed Central Ltd 2010
abstract_unstemmed	Abstract Considerable time and effort has been spent in developing analysis and quality assessment methods to allow the use of microarrays in a clinical setting. As is the case for microarrays and other high-throughput technologies, data from new high-throughput sequencing technologies are subject to technological and biological biases and systematic errors that can impact downstream analyses. Only when these issues can be readily identified and reliably adjusted for will clinical applications of these new technologies be feasible. Although much work remains to be done in this area, we describe consistently observed biases that should be taken into account when analyzing high-throughput sequencing data. In this article, we review current knowledge about these biases, discuss their impact on analysis results, and propose solutions. © BioMed Central Ltd 2010
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title_short	Overcoming bias and systematic errors in next generation sequencing data
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