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A competitive hybridization model predicts probe signal intensity on high density DNA microarrays
A central, unresolved problem of DNA microarray technology is the interpretation of different signal intensities from multiple probes targeting the same transcript. We propose a competitive hybridization model for DNA microarray hybridization. Our model uses a probe-specific dissociation constant th...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2582621/ https://www.ncbi.nlm.nih.gov/pubmed/18931378 http://dx.doi.org/10.1093/nar/gkn740 |
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author | Li, Shuzhao Pozhitkov, Alex Brouwer, Marius |
author_facet | Li, Shuzhao Pozhitkov, Alex Brouwer, Marius |
author_sort | Li, Shuzhao |
collection | PubMed |
description | A central, unresolved problem of DNA microarray technology is the interpretation of different signal intensities from multiple probes targeting the same transcript. We propose a competitive hybridization model for DNA microarray hybridization. Our model uses a probe-specific dissociation constant that is computed with current nearest neighbor model and existing parameters, and only four global parameters that are fitted to Affymetrix Latin Square data. This model can successfully predict signal intensities of individual probes, therefore makes it possible to quantify the absolute concentration of targets. Our results offer critical insights into the design and data interpretation of DNA microarrays. |
format | Text |
id | pubmed-2582621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-25826212008-11-13 A competitive hybridization model predicts probe signal intensity on high density DNA microarrays Li, Shuzhao Pozhitkov, Alex Brouwer, Marius Nucleic Acids Res Computational Biology A central, unresolved problem of DNA microarray technology is the interpretation of different signal intensities from multiple probes targeting the same transcript. We propose a competitive hybridization model for DNA microarray hybridization. Our model uses a probe-specific dissociation constant that is computed with current nearest neighbor model and existing parameters, and only four global parameters that are fitted to Affymetrix Latin Square data. This model can successfully predict signal intensities of individual probes, therefore makes it possible to quantify the absolute concentration of targets. Our results offer critical insights into the design and data interpretation of DNA microarrays. Oxford University Press 2008-11 2008-10-18 /pmc/articles/PMC2582621/ /pubmed/18931378 http://dx.doi.org/10.1093/nar/gkn740 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Li, Shuzhao Pozhitkov, Alex Brouwer, Marius A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title | A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title_full | A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title_fullStr | A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title_full_unstemmed | A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title_short | A competitive hybridization model predicts probe signal intensity on high density DNA microarrays |
title_sort | competitive hybridization model predicts probe signal intensity on high density dna microarrays |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2582621/ https://www.ncbi.nlm.nih.gov/pubmed/18931378 http://dx.doi.org/10.1093/nar/gkn740 |
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