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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...

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Detalles Bibliográficos
Autores principales: Li, Shuzhao, Pozhitkov, Alex, Brouwer, Marius
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2008
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.
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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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