Robust quantification of polymerase chain reactions using global fitting.

<h4>Background</h4>Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismiss...

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Autores principales:	Ana C Carr, Sean D Moore
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6205236dc9c249d881b5b2c1d7d2370c
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spelling	oai:doaj.org-article:6205236dc9c249d881b5b2c1d7d2370c2021-11-18T07:16:37ZRobust quantification of polymerase chain reactions using global fitting.1932-620310.1371/journal.pone.0037640https://doaj.org/article/6205236dc9c249d881b5b2c1d7d2370c2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22701526/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismissed. Numerous analytical methods have been employed that can extract the relative template abundance between samples. However, each method is sensitive to baseline assignment and to the unique shape profiles of individual reactions, which gives rise to increased variance stemming from the analytical procedure itself.<h4>Principal findings</h4>We developed a simple mathematical model that accurately describes the entire PCR reaction profile using only two reaction variables that depict the maximum capacity of the reaction and feedback inhibition. This model allows quantification that is more accurate than existing methods and takes advantage of the brighter fluorescence signals from later cycles. Because the model describes the entire reaction, the influences of baseline adjustment errors, reaction efficiencies, template abundance, and signal loss per cycle could be formalized. We determined that the common cycle-threshold method of data analysis introduces unnecessary variance because of inappropriate baseline adjustments, a dynamic reaction efficiency, and also a reliance on data with a low signal-to-noise ratio.<h4>Significance</h4>Using our model, fits to raw data can be used to determine template abundance with high precision, even when the data contains baseline and signal loss defects. This improvement reduces the time and cost associated with qPCR and should be applicable in a variety of academic, clinical, and biotechnological settings.Ana C CarrSean D MoorePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e37640 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ana C Carr Sean D Moore Robust quantification of polymerase chain reactions using global fitting.
description	<h4>Background</h4>Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismissed. Numerous analytical methods have been employed that can extract the relative template abundance between samples. However, each method is sensitive to baseline assignment and to the unique shape profiles of individual reactions, which gives rise to increased variance stemming from the analytical procedure itself.<h4>Principal findings</h4>We developed a simple mathematical model that accurately describes the entire PCR reaction profile using only two reaction variables that depict the maximum capacity of the reaction and feedback inhibition. This model allows quantification that is more accurate than existing methods and takes advantage of the brighter fluorescence signals from later cycles. Because the model describes the entire reaction, the influences of baseline adjustment errors, reaction efficiencies, template abundance, and signal loss per cycle could be formalized. We determined that the common cycle-threshold method of data analysis introduces unnecessary variance because of inappropriate baseline adjustments, a dynamic reaction efficiency, and also a reliance on data with a low signal-to-noise ratio.<h4>Significance</h4>Using our model, fits to raw data can be used to determine template abundance with high precision, even when the data contains baseline and signal loss defects. This improvement reduces the time and cost associated with qPCR and should be applicable in a variety of academic, clinical, and biotechnological settings.
format	article
author	Ana C Carr Sean D Moore
author_facet	Ana C Carr Sean D Moore
author_sort	Ana C Carr
title	Robust quantification of polymerase chain reactions using global fitting.
title_short	Robust quantification of polymerase chain reactions using global fitting.
title_full	Robust quantification of polymerase chain reactions using global fitting.
title_fullStr	Robust quantification of polymerase chain reactions using global fitting.
title_full_unstemmed	Robust quantification of polymerase chain reactions using global fitting.
title_sort	robust quantification of polymerase chain reactions using global fitting.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/6205236dc9c249d881b5b2c1d7d2370c
work_keys_str_mv	AT anaccarr robustquantificationofpolymerasechainreactionsusingglobalfitting AT seandmoore robustquantificationofpolymerasechainreactionsusingglobalfitting
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Robust quantification of polymerase chain reactions using global fitting.

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