Shape based kinetic outlier detection in real-time PCR
- Equal contributors
1 Dipartimento DiSUAN, Sezione di Biomatematica, Università degli Studi di Urbino "Carlo Bo", Campus Scientifico Sogesta; Località Crocicchia - 61029 Urbino, Italy
2 Dipartimento di Scienze Biomolecolari, Sezione di Ricerca sull'Attività Motoria e della Salute, Università degli Studi di Urbino "Carlo Bo", Via I Maggetti, 26/2 - 61029 Urbino, Italy
BMC Bioinformatics 2010, 11:186 doi:10.1186/1471-2105-11-186Published: 12 April 2010
Real-time PCR has recently become the technique of choice for absolute and relative nucleic acid quantification. The gold standard quantification method in real-time PCR assumes that the compared samples have similar PCR efficiency. However, many factors present in biological samples affect PCR kinetic, confounding quantification analysis. In this work we propose a new strategy to detect outlier samples, called SOD.
Richards function was fitted on fluorescence readings to parameterize the amplification curves. There was not a significant correlation between calculated amplification parameters (plateau, slope and y-coordinate of the inflection point) and the Log of input DNA demonstrating that this approach can be used to achieve a "fingerprint" for each amplification curve. To identify the outlier runs, the calculated parameters of each unknown sample were compared to those of the standard samples. When a significant underestimation of starting DNA molecules was found, due to the presence of biological inhibitors such as tannic acid, IgG or quercitin, SOD efficiently marked these amplification profiles as outliers. SOD was subsequently compared with KOD, the current approach based on PCR efficiency estimation. The data obtained showed that SOD was more sensitive than KOD, whereas SOD and KOD were equally specific.
Our results demonstrated, for the first time, that outlier detection can be based on amplification shape instead of PCR efficiency. SOD represents an improvement in real-time PCR analysis because it decreases the variance of data thus increasing the reliability of quantification.