Physiol. Genomics 25: 525-527, 2006.
First published February 28, 2006; doi:10.1152/physiolgenomics.00233.2005
1094-8341/06 $8.00
Received 21 September 2005;
accepted in final form 22 February 2006.
Physiological Genomics 25:525-527 (2006)
1094-8341/06 $8.00 © 2006 American Physiological Society
Toolbox
qPCR-DAMS: a database tool to analyze, manage, and store both relative and absolute quantitative real-time PCR data
Nili Jin,
Keyu He and
Lin Liu
Department of Physiological Sciences, Oklahoma State University, Stillwater, Oklahoma
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ABSTRACT
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Quantitative real-time PCR is an important high-throughput method in the biomedical sciences. However, existing software has limitations in handling both relative and absolute quantification. We designed quantitative PCR data analysis and management system (qPCR-DAMS), a database tool based on Access 2003, to deal with such shortcomings by the addition of integrated mathematical procedures. qPCR-DAMS allows a user to choose among four methods for data processing within a single software package: 1) ratio relative quantification, 2) absolute level, 3) normalized absolute expression, and 4) ratio absolute quantification. qPCR-DAMS also provides a tool for multiple reference gene normalization. qPCR-DAMS has three quality control steps and a data display system to monitor data variation. In summary, qPCR-DAMS is a handy tool for real-time PCR users.
quantitative PCR data analysis and management system
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INTRODUCTION
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QUANTITATIVE REAL-TIME PCR (qPCR) is becoming increasingly important in biomedical research because of its accuracy, sensitivity, and high efficiency (3). With the wide application of this technique, efficiently managing and processing raw data is becoming more complex than acquiring the data. Although many laboratories and companies have developed software to manage real-time PCR data, there are limitations in existing software.
An apparent problem is that no software can efficiently manage both relative and absolute qPCR data. Most software, such as relative expression software tool (REST) (5), Q-gene (4), and correlator of advanced real-time assays (CARTA) (1), was designed to process relative qPCR data. Absolute quantification has been widely used in microbiological detection and molecular diagnosis, as well as the determination of relative gene expression (2). Some software, such as ABI 7500 system software, provides packages for both absolute and relative quantification but lacks data storage capacity and has limited data processing functions. For an absolute quantitative method, it cannot perform interplate calculations and gives normalized expression. Furthermore, it cannot handle the standard curve method for relative quantification. The quantitative PCR data analysis and management system (qPCR-DAMS) software provides a single software package to process, manage, and store both relative and absolute quantitative real-time PCR data. A user is allowed to choose among four methods: 1) ratio relative quantification, 2) absolute levels, 3) normalized absolute expression, and 4) ratio absolute quantification. In the advanced option, a user can also use multiple reference gene normalization (6) for both relative and absolute quantification. This may be especially useful in a core facility, where many researchers share the same detector system but run real-time PCR and process data in different ways.
Figure 1 shows the mathematical model of how qPCR-DAMS works and how we resolve the internal conflict in data processing procedures for relative and absolute quantification methods [for details, see also Supplementary Materials, User's Manual, 6) Conceptions and Mathematical Structures, at http://www.cvm.okstate.edu/research/Facilities/LungBiologyLab/]. When there are multiple runs for the relative quantification, the normalized expression (NE) and relative expression (ratio) of a gene are first calculated from intraplate data, and then the final ratio is calculated from interplate data. However, because data from different runs are comparable, for absolute quantification, first the mean quantities of the reference gene and the target gene are calculated from interplate data, and then NE is calculated from the mean quantities. qPCR-DAMS assigns each stored plate report a plate name. For relative quantification, plate reports of the target gene and the reference gene generated from the same plate run share the same plate name. When processing data, the integrated mathematical procedures are plate name dependent. Therefore, NE is calculated from reports with the same plate name (Fig. 1A). However, the absolute quantitative method is plate name independent. Before calculation of NE, the interplate calculation is performed based on gene identification (ID) and sample names only (Fig. 1B).
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Fig. 1. Mathematical strategies of relative and absolute quantification methods in quantitative PCR data analysis and management system (qPCR-DAMS). A: relative quantification method calculates intraplate normalized expression, followed by intraplate ratio and final ratio. The calculation is plate name dependent. B: absolute quantification method calculates intra- and interplate mean quantities first and then normalized expression. The calculation is plate name independent.
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Another drawback of most current software is the limited data storage and sorting capacity. Most tools only store threshold cycle (Ct) and the processed data. Q-gene provides a better coverage for the whole experiment, but Q-gene is implemented on Microsoft Excel, which lacks database function (2). qPCR-DAMS is implemented on a Microsoft Access 2003 relational database management system based on Visual Basic. As shown in the database structure (Fig. 2), qPCR-DAMS stores all of the experiment-related information (gene, sample, plate, and experiment), and a clear hierarchical relationship is established as gene > sample > plate > experiment. There are almost no limitations to the number of genes, samples, and plates for qPCR-DAMS to handle. In addition to general information such as gene ID, sample name, treatment, sample description, researcher information, and experiment description, qPCR-DAMS also stores exported reports from the detector system. Therefore, the processed results are directly associated with the raw data, which can greatly improve the data validity compared with software that only hosts part of the raw data. Efficient data tracking is also critical for the accuracy and validation of real-time PCR. qPCR-DAMS has a "View Data" function on the main panel so that a user can easily track the stored information in the database. A user can track data by genes, samples, plates, or experiments. All the data tables are linked so that the user can trace back to the raw data from the detector systems starting from any point.
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Fig. 2. Database structure of qPCR-DAMS. qPCR-DAMS hosts gene, sample, and plate information. All the tables are linked so that efficient data processing and tracking functions can be implemented.
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Quality control is implemented through three error-checking steps during data processing. First, if Ct is undetermined or quantity is 0, the software assigns a value of 40 to Ct or 0.01 to the quantity to avoid the mathematical problem of calculating the ratio. Second, intraplate variation is calculated as a coefficient of variation from replicated samples on the same plate. Third, interplate variation is calculated as a coefficient of variation from replicated samples on different plates. qPCR-DAMS allows a user to set a threshold value for intraplate variation or interplate variation. If a sample has a variation above the threshold, the sample is marked and can be excluded manually by a user. There is one more advantage of qPCR-DAMS: the data display system. Most other software displays final quantitative results directly. However, several factors, such as bad sample, bad reaction, cross contamination, and pipeting error, may lead to misleading results. qPCR-DAMS can output separate reports for all the calculation steps, which include intraplate calculation, interplate calculation, NE, mean NE, and so forth.
The qPCR-DAMS runs with a Windows XP or Windows 2000 environment. This software is free for academic use and is downloadable at http://www.cvm.okstate.edu/research/Facilities/LungBiologyLab/. Supplementary Information can be found at the same web site. For installation, instructions on how to use, and details of qPCR-DAMS, see User's Manual.
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GRANTS
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This study was supported by National Heart, Lung, and Blood Institute Grants R01-HL-52146 and R01-HL-071628 (to L. Liu). N. Jin was supported by American Heart Association Predoctoral Fellowship 0315256Z.
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ACKNOWLEDGMENTS
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We are grateful to Zhongming Chen, Tingting Weng, and Manoj Bhaskaran, who gave many constructive suggestions. We also thank Tisha Posey for editorial assistance.
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FOOTNOTES
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Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).
Address for reprint requests and other correspondence: L. Liu, Dept. of Physiological Sciences, Oklahoma State Univ., 264 McElroy Hall, Stillwater, OK 74078 (e-mail: lin.liu{at}okstate.edu).
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Copyright © 2006 by the American Physiological Society.
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