Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence

<p>Abstract</p> <p>Background</p> <p>In real-time PCR, it is necessary to consider the efficiency of amplification (EA) of amplicons in order to determine initial target levels properly. EAs can be deduced from standard curves, but these involve extra effort and cost and may yield invalid EAs. Alternatively, EA can be extracted from individual fluorescence curves. Unfortunately, this is not reliable enough.</p> <p>Results</p> <p>Here we introduce simultaneous non-linear fitting to determine – without standard curves – an optimal common EA for all samples of a group. In order to adjust EA as a function of target fluorescence, and still to describe fluorescence as a function of cycle number, we use an iterative algorithm that increases fluorescence cycle by cycle and thus simulates the PCR process. A Gauss peak function is used to model the decrease of EA with increasing amplicon accumulation. Our approach was validated experimentally with hydrolysis probe or SYBR green detection with dilution series of 5 different targets. It performed distinctly better in terms of accuracy than standard curve, DART-PCR, and LinRegPCR approaches. Based on reliable EAs, it was possible to detect that for some amplicons, extraordinary fluorescence (EA > 2.00) was generated with locked nucleic acid hydrolysis probes, but not with SYBR green.</p> <p>Conclusion</p> <p>In comparison to previously reported approaches that are based on the separate analysis of each curve and on modelling EA as a function of cycle number, our approach yields more accurate and precise estimates of relative initial target levels.</p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-1

Rbitrarily chosen; numbering refers to the Excel raw data file [see Additional file ]) was analyzed. After subtraction of linear background, EA was calculated as F/Fratio and plotted against fluorescence (F) as shown. To avoid confusion, points with F< 0.1 are not displayed; most of these, because of large errors in EA, lie outside the y axis range. The line graphs were drawn with the Gauss peak function (upper row) or the logistic peak function (lower row). Note that function parameters were not fitted to the points shown, but determined by our stepwise PCR simulation approach based on the raw fluorescence versus cycle number data. Open circles represent data that was not used for fitting.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-0

Filled circles were used for fitting with function EAvPeak; fitting results are displayed as line graph. Circles marked by an asterisk indicate the 5 point peak. (B) As part A, but at higher magnification on the y axis. Points for definition of background fluorescence are selected as follows: each position i, going down cycle by cycle from the 5 point peak, is checked until the dF values of at least 3 points in the interval i-8 to i-1 surpass the reference level, which is the average dF of points i, i+1, and i+2. The upper limit of the background definition interval, denoted by a filled arrow, corresponds to i; the lower limit, denoted by an open arrow, is at i-8. Finally, slope and offset of the background line is determined by linear regression on the raw fluorescence data at i-8 to i. (C) Corresponding raw fluorescence data. Fitting results from function EAv are displayed as line graph. Open circles represent points that were excluded from fitting. Asterisks and arrows as above.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-2

SYBR green detection. The diagram shows forward primer, LNA hydrolysis probe with 5' fluorophore (filled circle) and 3' quencher (open circle), and the entire amplicon antisense strand, with reverse primer sequence underlined. A 3' phosphate (P) prevents elongation of the probe.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p

Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence-4

Rbitrarily chosen; numbering refers to the Excel raw data file [see Additional file ]) was analyzed. After subtraction of linear background, EA was calculated as F/Fratio and plotted against fluorescence (F) as shown. To avoid confusion, points with F< 0.1 are not displayed; most of these, because of large errors in EA, lie outside the y axis range. The line graphs were drawn with the Gauss peak function (upper row) or the logistic peak function (lower row). Note that function parameters were not fitted to the points shown, but determined by our stepwise PCR simulation approach based on the raw fluorescence versus cycle number data. Open circles represent data that was not used for fitting.<p><b>Copyright information:</b></p><p>Taken from "Simultaneous fitting of real-time PCR data with efficiency of amplification modeled as Gaussian function of target fluorescence"</p><p>http://www.biomedcentral.com/1471-2105/9/95</p><p>BMC Bioinformatics 2008;9():95-95.</p><p>Published online 12 Feb 2008</p><p>PMCID:PMC2276494.</p><p></p