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The impact of quantitative optimization of hybridization conditions on gene expression analysis
BACKGROUND: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarra...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3065421/ https://www.ncbi.nlm.nih.gov/pubmed/21401920 http://dx.doi.org/10.1186/1471-2105-12-73 |
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author | Sykacek, Peter Kreil, David P Meadows, Lisa A Auburn, Richard P Fischer, Bettina Russell, Steven Micklem, Gos |
author_facet | Sykacek, Peter Kreil, David P Meadows, Lisa A Auburn, Richard P Fischer, Bettina Russell, Steven Micklem, Gos |
author_sort | Sykacek, Peter |
collection | PubMed |
description | BACKGROUND: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. RESULTS: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays. For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions. CONCLUSIONS: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks. |
format | Text |
id | pubmed-3065421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-30654212011-03-29 The impact of quantitative optimization of hybridization conditions on gene expression analysis Sykacek, Peter Kreil, David P Meadows, Lisa A Auburn, Richard P Fischer, Bettina Russell, Steven Micklem, Gos BMC Bioinformatics Methodology Article BACKGROUND: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. RESULTS: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of up to 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays. For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We can ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirmed an unbiased determination of generally optimal experimental conditions. CONCLUSIONS: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive pro filing of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks. BioMed Central 2011-03-14 /pmc/articles/PMC3065421/ /pubmed/21401920 http://dx.doi.org/10.1186/1471-2105-12-73 Text en Copyright ©2011 Sykacek et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Sykacek, Peter Kreil, David P Meadows, Lisa A Auburn, Richard P Fischer, Bettina Russell, Steven Micklem, Gos The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title | The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title_full | The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title_fullStr | The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title_full_unstemmed | The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title_short | The impact of quantitative optimization of hybridization conditions on gene expression analysis |
title_sort | impact of quantitative optimization of hybridization conditions on gene expression analysis |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3065421/ https://www.ncbi.nlm.nih.gov/pubmed/21401920 http://dx.doi.org/10.1186/1471-2105-12-73 |
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