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A standard curve based method for relative real time PCR data processing

BACKGROUND: Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At...

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Detalles Bibliográficos
Autores principales: Larionov, Alexey, Krause, Andreas, Miller, William
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1274258/
https://www.ncbi.nlm.nih.gov/pubmed/15780134
http://dx.doi.org/10.1186/1471-2105-6-62
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author Larionov, Alexey
Krause, Andreas
Miller, William
author_facet Larionov, Alexey
Krause, Andreas
Miller, William
author_sort Larionov, Alexey
collection PubMed
description BACKGROUND: Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. RESULTS: We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation. The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation. A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes. CONCLUSION: A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR.
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spelling pubmed-12742582005-10-29 A standard curve based method for relative real time PCR data processing Larionov, Alexey Krause, Andreas Miller, William BMC Bioinformatics Methodology Article BACKGROUND: Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. RESULTS: We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation. The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation. A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes. CONCLUSION: A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR. BioMed Central 2005-03-21 /pmc/articles/PMC1274258/ /pubmed/15780134 http://dx.doi.org/10.1186/1471-2105-6-62 Text en Copyright © 2005 Larionov et al; licensee BioMed Central Ltd.
spellingShingle Methodology Article
Larionov, Alexey
Krause, Andreas
Miller, William
A standard curve based method for relative real time PCR data processing
title A standard curve based method for relative real time PCR data processing
title_full A standard curve based method for relative real time PCR data processing
title_fullStr A standard curve based method for relative real time PCR data processing
title_full_unstemmed A standard curve based method for relative real time PCR data processing
title_short A standard curve based method for relative real time PCR data processing
title_sort standard curve based method for relative real time pcr data processing
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1274258/
https://www.ncbi.nlm.nih.gov/pubmed/15780134
http://dx.doi.org/10.1186/1471-2105-6-62
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